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Zhang Z, Liu Z, Hong N, Chen L. Effect of a second-generation motion correction algorithm on image quality and measurement reproducibility of coronary CT angiography in patients with a myocardial bridge and mural coronary artery. Clin Radiol 2024; 79:e462-e467. [PMID: 38135576 DOI: 10.1016/j.crad.2023.11.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/14/2023] [Accepted: 11/20/2023] [Indexed: 12/24/2023]
Abstract
AIM To determine the effect of second-generation motion correction (MC2) on image quality and measurement reproducibility of cardiac CT images in patients with a myocardial bridge and mural coronary artery (MB-MCA) compared to standard (STD) images without motion correction and with first-generation motion correction (MC1). MATERIALS AND METHODS A total of 66 patients with MB-MCA in the left anterior descending branch who underwent 256-detector CT with single-heartbeat acquisition were included. Images were reconstructed at 45% and 75% R-R intervals using STD, MC1, and MC2 algorithms. Image quality for MB-MCA was assessed by two observers on a four-point scale (1 = poor and 4 = excellent) and compared among STD, MC1, and MC2. Depth and length of MB, lumen area, and minimal diameter of MCA were measured and compared. RESULTS At 45% R-R interval, image quality scores were 1.59 ± 0.78, 2.21 ± 0.97, and 3.21 ± 0.62 for MCA, and 2.48 ± 0.79, 2.76 ± 0.75, and 3.58 ± 0.58 for MB with STD, MC1 and MC2, respectively. At 75% R-R interval, these values were 2.26 ± 0.60, 3.03 ± 0.89, and 3.59 ± 0.55 for MCA and 3.00 ± 0.93, 3.17 ± 0.83, and 3.80 ± 0.44 for MB. Although MC1 was superior to STD in displaying MCA, there was no statistical difference between the two algorithms for MB (p>0.05). Compared with STD and MC1, MC2 statistically improved image quality and interpretability for both MCA and MB and had narrower limits in interobserver agreement for measurements at both 45% and 75% R-R intervals. CONCLUSION MC2 improves CT image quality and measurement reproducibility in patients with MB-MCA compared to STD and MC1.
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Affiliation(s)
- Z Zhang
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Z Liu
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - N Hong
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - L Chen
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China.
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Wen H, Deng G, Shi X, Liu Z, Lin A, Cheng Q, Zhang J, Luo P. Body mass index, weight change, and cancer prognosis: a meta-analysis and systematic review of 73 cohort studies. ESMO Open 2024; 9:102241. [PMID: 38442453 PMCID: PMC10925937 DOI: 10.1016/j.esmoop.2024.102241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/19/2023] [Accepted: 01/09/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Identifying the association between body mass index (BMI) or weight change and cancer prognosis is essential for the development of effective cancer treatments. We aimed to assess the strength and validity of the evidence of the association between BMI or weight change and cancer prognosis by a systematic evaluation and meta-analysis of relevant cohort studies. METHODS We systematically searched the PubMed, Web of Science, EconLit, Embase, Food Sciences and Technology Abstracts, PsycINFO, and Cochrane databases for literature published up to July 2023. Inclusion criteria were cohort studies with BMI or weight change as an exposure factor, cancer as a diagnostic outcome, and data type as an unadjusted hazard ratio (HR) or headcount ratio. Random- or fixed-effects models were used to calculate the pooled HR along with the 95% confidence interval (CI). RESULTS Seventy-three cohort studies were included in the meta-analysis. Compared with normal weight, overweight or obesity was a risk factor for overall survival (OS) in patients with breast cancer (HR 1.37, 95% CI 1.22-1.53; P < 0.0001), while obesity was a protective factor for OS in patients with gastrointestinal tumors (HR 0.67, 95% CI 0.56-0.80; P < 0.0001) and lung cancer (HR 0.67, 95% CI 0.48-0.92; P = 0.01) compared with patients without obesity. Compared with normal weight, underweight was a risk factor for OS in patients with breast cancer (HR 1.15, 95% CI 0.98-1.35; P = 0.08), gastrointestinal tumors (HR 1.54, 95% CI 1.32-1.80; P < 0.0001), and lung cancer (HR 1.28, 95% CI 1.22-1.35; P < 0.0001). Compared with nonweight change, weight loss was a risk factor for OS in patients with gastrointestinal cancer. CONCLUSIONS Based on the results of the meta-analysis, we concluded that BMI, weight change, and tumor prognosis were significantly correlated. These findings may provide a more reliable argument for the development of more effective oncology treatment protocols.
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Affiliation(s)
- H Wen
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong
| | - G Deng
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong; The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong
| | - X Shi
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong
| | - Z Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing; Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
| | - A Lin
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong.
| | - Q Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China.
| | - J Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong.
| | - P Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong.
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Hu L, Guo X, Zhou D, Wang Z, Dai L, Li L, Li Y, Zhang T, Long H, Yu C, Shi ZW, Han C, Lu C, Zhao J, Li Y, Zha Y, Liu Z. Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis. Radiol Artif Intell 2024; 6:e230362. [PMID: 38446042 DOI: 10.1148/ryai.230362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Purpose To develop an MRI-based model for clinically significant prostate cancer (csPCa) diagnosis that can resist rectal artifact interference. Materials and Methods This retrospective study included 2203 male patients with prostate lesions who underwent biparametric MRI and biopsy between January 2019 and June 2023. Targeted adversarial training with proprietary adversarial samples (TPAS) strategy was proposed to enhance model resistance against rectal artifacts. The automated csPCa diagnostic models trained with and without TPAS were compared using multicenter validation datasets. The impact of rectal artifacts on the diagnostic performance of each model at the patient and lesion levels was compared using the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC). The AUC between models was compared using the DeLong test, and the AUPRC was compared using the bootstrap method. Results The TPAS model exhibited diagnostic performance improvements of 6% at the patient level (AUC: 0.87 vs 0.81, P < .001) and 7% at the lesion level (AUPRC: 0.84 vs 0.77, P = .007) compared with the control model. The TPAS model demonstrated less performance decline in the presence of rectal artifact-pattern adversarial noise than the control model (ΔAUC: -17% vs -19%, ΔAUPRC: -18% vs -21%). The TPAS model performed better than the control model in patients with moderate (AUC: 0.79 vs 0.73, AUPRC: 0.68 vs 0.61) and severe (AUC: 0.75 vs 0.57, AUPRC: 0.69 vs 0.59) artifacts. Conclusion This study demonstrates that the TPAS model can reduce rectal artifact interference in MRI-based csPCa diagnosis, thereby improving its performance in clinical applications. Keywords: MR-Diffusion-weighted Imaging, Urinary, Prostate, Comparative Studies, Diagnosis, Transfer Learning Clinical trial registration no. ChiCTR23000069832 Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
- Lei Hu
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Xiangyu Guo
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Dawei Zhou
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Zhen Wang
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Lisong Dai
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Liang Li
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Ying Li
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Tian Zhang
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Haining Long
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Chengxin Yu
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Zhen-Wei Shi
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Chu Han
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Cheng Lu
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Jungong Zhao
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Yuehua Li
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Yunfei Zha
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
| | - Zaiyi Liu
- From the Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China (L.H.); Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China (L.H., Z.W.S., C.H., C.L., Z.L.); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (L.H., Z.W.S., C.H., C.L., Z.L.); Department of TPS Algorithm, Xi'an OUR United Corporation, Xi'an, China (X.G.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.Z.); Department of Radiology, Yichang Central People's Hospital Affiliated to the First Clinical Medical College of Three Gorges University, Yichang, China (Z.W., C.Y.); Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (L.D., H.L., J.Z., Yuehua Li); and Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China (L.L., Ying Li, T.Z., Y.Z.)
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Su H, Xu Z, Bao MDL, Luo S, Liang JW, Pei W, Guan X, Liu Z, Jiang Z, Zhang MG, Zhao ZX, Jin WS, Zhou HT. [The clinical significance of lateral pelvic sentinel lymph node biopsy using indocyanine green fluorescence navigation in laparoscopic lateral pelvic lymph node dissection]. Zhonghua Zhong Liu Za Zhi 2024; 46:140-145. [PMID: 38418188 DOI: 10.3760/cma.j.cn112152-20231026-00265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Objectives: This study aims to explore the clinical significance of lateral pelvic sentinel lymph node biopsy (SLNB) using indocyanine green (ICG) fluorescence navigation in laparoscopic lateral pelvic lymph node dissection (LLND) and evaluate the accuracy and feasibility of this technique to predict the status of lateral pelvic lymph nodes (LPLNs). Methods: The clinical and pathological characteristics, surgical outcomes, lymph node findings and perioperative complications of 16 rectal cancer patients who underwent SLNB using ICG fluorescence navigation in laparoscopic LLND in the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College during April 2017 and October 2022 were retrospectively collected and analyzed. The patients did not receive preoperative neoadjuvant radiotherapy and presented with LPLNs but without LPLN enlargement (MRI showed the maximum short axes of the LPLNs were ≥5 mm and <10 mm at first visit). Results: All 16 patients were successfully performed SLNB using ICG fluorescence navigation in laparoscopic LLND. Three patients underwent bilateral LLND and 13 patients underwent unilateral LLND. The lateral pelvic sentinel lymph nodes (SLNs) were clearly fluorescent before dissection in 14 patients and the detection rate of SLNs for these patients was 87.5%. Lateral pelvic SLN metastasis was diagnosed in 2 patients and negative results were found in 12 patients by frozen pathological examinations. Among the 14 patients in whom lateral pelvic SLNs were detected, the dissected lateral pelvic non-SLNs were all negative. All dissected LPLNs were negative in two patients without fluorescent lateral pelvic SLNs. The specificity, sensitivity, negative predictive value, and accuracy was 85.7%, 100%, 100%, and 100%, respectively. Conclusions: This study indicates that lateral pelvic SLNB using ICG fluorescence navigation shows promise as a safe and feasible procedure with good accuracy. This technique may replace preventive LLND for locally advanced lower rectal cancer.
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Affiliation(s)
- H Su
- Department of Gastrointestinal Surgery, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Z Xu
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - M D L Bao
- Department of Pancreatic and Gastric Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - S Luo
- Department of Gastrointestinal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - J W Liang
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - W Pei
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - X Guan
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Z Liu
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Z Jiang
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - M G Zhang
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Z X Zhao
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - W S Jin
- Department of Anorectal Diseases, Third Medical Center of Chinese PLA General Hospital, Beijing 100039, China
| | - H T Zhou
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
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Li X, Liang X, Li Z, Liang J, Qi Z, Zhong L, Geng Z, Liang W, Quan X, Liang C, Liu Z. A novel stratification scheme combined with internal arteries in CT imaging for guiding postoperative adjuvant transarterial chemoembolization in hepatocellular carcinoma: a retrospective cohort study. Int J Surg 2024:01279778-990000000-01108. [PMID: 38377071 DOI: 10.1097/js9.0000000000001191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 01/31/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND Although postoperative adjuvant transarterial chemoembolization (PA-TACE) improves survival outcomes in a subset of patients with resected hepatocellular carcinoma (HCC), the lack of reliable biomarkers for patient selection remains a significant challenge. The present study aimed to evaluate whether computed tomography (CT) imaging can provide more value for predicting benefits from PA-TACE and to establish a new scheme for guiding PA-TACE benefits. METHODS In this retrospective study, patients with HCC who had undergone preoperative contrast-enhanced CT and curative hepatectomy were evaluated. Inverse probability of treatment weight was performed to balance the difference of baseline characteristics. Cox models were used to test the interaction among PA-TACE, imaging features and pathological indicators. An HCC imaging and pathological classification (HIPC) scheme incorporating these imaging and pathological indicators was established. RESULTS This study included 1,488 patients (median age, 52 y [IQR, 45-61 y]; 1,309 male). Microvascular invasion (MVI) positive, and diameter>5 cm tumors achieved a higher recurrence-free survival (RFS), and overall survival (OS) benefit, respectively, from PA-TACE than MVI negative, and diameter ≤5 cm tumors. Patients with internal arteries (IA) positive benefited more than those with IA negative in terms of RFS (P=0.016) and OS (P=0.018). PA-TACE achieved significant RFS and OS improvements in HIPC3 (IA present and diameter, or two or three tumors) patients but not in HIPC1 (diameter ≤5 cm, MVI negative) and HIPC2 (other single tumor) patients. Our scheme may decrease the number of patients receiving PA-TACE by approximately 36.5% compared to the previous suggestion. CONCLUSIONS IA can provide more value for predicting the benefit of PA-TACE treatment. The proposed HIPC scheme can be used to stratify patients with and without survival benefits from PA-TACE.
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Affiliation(s)
- Xinming Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xiangjing Liang
- Ultrasound Medical Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhipeng Li
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jianye Liang
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhendong Qi
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Liming Zhong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Zhijun Geng
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wen Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xianyue Quan
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Changhong Liang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Zaiyi Liu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
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Cai M, Zhao K, Wu L, Huang Y, Zhao M, Hu Q, Chen Q, Yao S, Li Z, Fan X, Liu Z. Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis. Chin Med J (Engl) 2024; 137:421-430. [PMID: 38238158 PMCID: PMC10876244 DOI: 10.1097/cm9.0000000000002964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology. METHODS The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted. RESULTS The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037). CONCLUSIONS We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.
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Affiliation(s)
- Ming Cai
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
| | - Ke Zhao
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
| | - Lin Wu
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
| | - Minning Zhao
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Qingru Hu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Qicong Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, Guangdong 510006, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
| | - Zhenhui Li
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China
| | - Xinjuan Fan
- Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510655, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
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Liu Z, Xiao D. [Annual progress in tobacco medicine in 2023]. Zhonghua Jie He He Hu Xi Za Zhi 2024; 47:163-166. [PMID: 38309968 DOI: 10.3760/cma.j.cn112147-20231030-00278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/05/2024]
Abstract
Smoking is one of the major risk factors for several chronic non-infectious diseases, including chronic respiratory diseases, cancers, cardiovascular diseases, and diabetes, which has become a major public health issue in China. Tobacco control is proven to be the most effective and cost-effective strategy to reduce the risk of smoking-related disease and premature death. From October 2022 to September 2023, several high quality studies on tobacco medicine have been published. This review systematically summarizes the representative studies in terms of epidemiological study, clinical study, mechanism study, and tobacco control progress. These studies further highlight the concept that "tobacco smoking is the main evil for disease and tobacco control is the main good for disease prevention", which will promote the development of tobacco medicine in China.
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Affiliation(s)
- Z Liu
- Department of Tobacco Control and Prevention of Respiratory Diseases, China-Japan Friendship Hospital, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, World Health Organization Collaborating Centre for Tobacco Cessation and Respiratory Diseases Prevention, Beijing 100029, China
| | - D Xiao
- Department of Tobacco Control and Prevention of Respiratory Diseases, China-Japan Friendship Hospital, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, World Health Organization Collaborating Centre for Tobacco Cessation and Respiratory Diseases Prevention, Beijing 100029, China
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8
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Wang G, Liu Z. COVID-19 infection experience regarded as new traumatic stressors worsen mental health status of ICU patients' family members. QJM 2024; 117:87-88. [PMID: 37651589 DOI: 10.1093/qjmed/hcad200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Indexed: 09/02/2023] Open
Affiliation(s)
- G Wang
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Z Liu
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
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Wang G, Zhuo N, Liu Z. Immune-mediated toxicity leading to organ failure may achieve good outcomes from ICU admission. QJM 2024; 117:82-83. [PMID: 37471619 DOI: 10.1093/qjmed/hcad178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Indexed: 07/22/2023] Open
Affiliation(s)
- G Wang
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - N Zhuo
- Department of Nephrology, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410000, China
| | - Z Liu
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
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Feng GC, Liu Z, Li HQ, Zuo DH, Sun HL, Qiao LX, Yin DT. [Clinical diagnosis and treatment analysis of 21 cases of intrathyroid thymic carcinoma]. Zhonghua Yi Xue Za Zhi 2024; 104:440-444. [PMID: 38326056 DOI: 10.3760/cma.j.cn112137-20231008-00676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Objective: To analyze the clinical efficacy of intrathyroid thymic carcinoma (ITTC). Methods: This study retrospectively analyzed the clinical data of 21 patients with ITTC diagnosed and treated at the First Affiliated Hospital of Zhengzhou University from January 2018 to July 2023, including 9 males and 12 females, with a median age of 52 years (40-60 years old). Results: There is a correlation between the maximum diameter of the tumor (≥40 mm) and lymph node metastasis (P=0.044). Seventeen patients received surgical treatment, and 4 patients only received chemotherapy. During the follow-up period, a total of 4 patients experienced death or progression, with a 2-year mortality or progression free survival rate of 74.8%. Conclusions: The prognosis of ITTC is good, and surgical treatment is the preferred treatment option, lymph node metastasis is significantly correlated with prognosis. The radiotherapy and chemotherapy of ITTC need to be determined based on the patient's condition.
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Affiliation(s)
- G C Feng
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou 450052, China
| | - Z Liu
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou 450052, China
| | - H Q Li
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou 450052, China
| | - D H Zuo
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou 450052, China
| | - H L Sun
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou 450052, China
| | - L X Qiao
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou 450052, China
| | - D T Yin
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou 450052, China
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Liu Z, Fu Y, Huang W, Li C, Wei X, Zhan J, Zheng J. LINC01094 promotes human nasal epithelial cell epithelial-to-mesenchymal transition and pyroptosis via upregulating HMGB1. Rhinology 2024; 62:88-100. [PMID: 37864411 DOI: 10.4193/rhin23.184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
BACKGROUND Excessive epithelial-to-mesenchymal transition (EMT) of nasal epithelial cells (NECs) play a prominent role in chronic rhinosinusitis with nasal polyps (CRSwNP) pathogenesis. Long intergenic non-coding RNA 01094 (LINC01094) was previously reported to be overexpressed in CRSwNP, while the regulatory mechanism by which LINC01094 regulates CRSwNP progression remains unclear. Our study aimed to investigate the role of LINC01094 in CRSwNP development. METHODS hNEC were isolated from tissues of controls and CRSwNP patients and stimulated with interleukin (IL)-13. 3-(4, 5-Dimethylthiazolyl2)-2, 5-diphenyltetrazolium bromide (MTT) assay was employed to analyze hNEC viability. Flow cytometry was employed to analyze pyroptosis. Immunofluorescence was employed to analyze Snail nuclear translocation. The interactions between LINC01094, fused in sarcoma (FUS) and high mobility group box-1 (HMGB1) were analyzed by RNA immunoprecipitation (RIP) and RNA pull-down assays. RESULTS LINC01094 and EMT-related proteins were markedly upregulated in nasal polyp tissues of CRSwNP. LINC01094 knockdown inhibited IL-13-induced hNEC EMT and pyroptosis. LINC01094 promoted HMGB1 expression in CRSwNP by binding with FUS. HMGB1 promoted Snail nuclear import in GSK-B phosphorylation-dependent manner. CONCLUSION LINC01094 facilitated hNEC EMT and pyroptosis in CRSwNP by activating the HMGB1/GSK-B Snail axis, which suggested that LINC01094 might serve as a biomarker and therapeutic target in CRSwNP.
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Affiliation(s)
- Z Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, P.R. China
| | - Y Fu
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, P.R. China
| | - W Huang
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, P.R. China
| | - C Li
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, P.R. China
| | - X Wei
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, P.R. China
| | - J Zhan
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, P.R. China
| | - J Zheng
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, P.R. China
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12
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Zhao Z, Du S, Xu Z, Yin Z, Huang X, Huang X, Wong C, Liang Y, Shen J, Wu J, Qu J, Zhang L, Cui Y, Wang Y, Wee L, Dekker A, Han C, Liu Z, Shi Z, Liang C. SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation. Comput Biol Med 2024; 169:107939. [PMID: 38194781 DOI: 10.1016/j.compbiomed.2024.107939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 12/12/2023] [Accepted: 01/01/2024] [Indexed: 01/11/2024]
Abstract
Accurate and automated segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in computer-aided diagnosis and treatment of breast cancer. However, this task is challenging, due to random variation in tumor sizes, shapes, appearances, and blurred boundaries of tumors caused by inherent heterogeneity of breast cancer. Moreover, the presence of ill-posed artifacts in DCE-MRI further complicate the process of tumor region annotation. To address the challenges above, we propose a scheme (named SwinHR) integrating prior DCE-MRI knowledge and temporal-spatial information of breast tumors. The prior DCE-MRI knowledge refers to hemodynamic information extracted from multiple DCE-MRI phases, which can provide pharmacokinetics information to describe metabolic changes of the tumor cells over the scanning time. The Swin Transformer with hierarchical re-parameterization large kernel architecture (H-RLK) can capture long-range dependencies within DCE-MRI while maintaining computational efficiency by a shifted window-based self-attention mechanism. The use of H-RLK can extract high-level features with a wider receptive field, which can make the model capture contextual information at different levels of abstraction. Extensive experiments are conducted in large-scale datasets to validate the effectiveness of our proposed SwinHR scheme, demonstrating its superiority over recent state-of-the-art segmentation methods. Also, a subgroup analysis split by MRI scanners, field strength, and tumor size is conducted to verify its generalization. The source code is released on (https://github.com/GDPHMediaLab/SwinHR).
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Affiliation(s)
- Zhihe Zhao
- School of Medicine, South China University of Technology, Guangzhou, 510006, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Siyao Du
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning Province, 110001, China
| | - Zeyan Xu
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China
| | - Zhi Yin
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, China
| | - Xiaomei Huang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Xin Huang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Shantou University Medical College, Shantou, 515041, China
| | - Chinting Wong
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Yanting Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China
| | - Jinrong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Lina Zhang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning Province, 110001, China
| | - Yanfen Cui
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, China
| | - Ying Wang
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China
| | - Leonard Wee
- Clinical Data Science, Faculty of Health Medicine Life Sciences, Maastricht University, Maastricht, 6229 ET, The Netherlands; Department of Radiation Oncology (Maastro), GROW School of Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School of Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands
| | - Chu Han
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
| | - Changhong Liang
- School of Medicine, South China University of Technology, Guangzhou, 510006, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
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Reiker T, Liu Z, Winter C, Cappellari MV, Abradelo DG, Strassert CA, Zhang D, Zacharias H. Ultrafast electron dynamics in excited states of conjugated thiophene-fluorene organic polymer (pF8T2) thin films. Phys Chem Chem Phys 2024; 26:4736-4751. [PMID: 38251969 DOI: 10.1039/d3cp00502j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
The electronic states of poly(9,9-dioctylfluorenyl-alt-bithiophene) pF8T2 on H/Si(100) substrates, prototypical for organic photovoltaics, were investigated by ultrafast photoelectron spectroscopy and by time-resolved fluorescence studies. Occupied and unoccupied electronic states were analysed by ultraviolet photoelectron spectroscopy (UPS), static and dynamic femtosecond two-photon photoemission (2PPE), and time-correlated single photon counting (TCSPC). Time-resolved measurements allow assessment of population lifetimes of intermediate states. The combination of time-resolved photoelectron spectroscopy and fluorescence excitation allows following the electronic dynamics in excited states from the femtosecond to the nanosecond time scale. For this prototypical material the electron kinetic energy resolved lifetimes range from about a few tens of femtoseconds up to hundreds of picoseconds. After annealing these types of organic thin films the efficiency of organic solar cells usually increases. We show that annealing does not influence the initial ultrafast charge generation processes, but the long-lived states. However, the nanosecond scale fluorescence lifetimes measured by TCSPC are prolonged after annealing, which therefore is identified as the cause of a greater exciton diffusion range and thus is beneficial for charge carrier extraction.
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Affiliation(s)
- T Reiker
- Center for Soft Nanoscience, University of Münster, 48149 Münster, Germany.
- Physics Institute, University of Münster, 48149 Münster, Germany
| | - Z Liu
- Organic Solids Laboratory, Institute of Chemistry, Chinese Academy of Science, Beijing 100190, P. R. China
| | - C Winter
- Physics Institute, University of Münster, 48149 Münster, Germany
| | - M V Cappellari
- Center for Nanotechnology and Institute for Inorganic and Analytical Chemistry, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany
| | - D Gonzalez Abradelo
- Center for Nanotechnology and Institute for Inorganic and Analytical Chemistry, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany
| | - C A Strassert
- Center for Nanotechnology and Institute for Inorganic and Analytical Chemistry, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany
| | - D Zhang
- Organic Solids Laboratory, Institute of Chemistry, Chinese Academy of Science, Beijing 100190, P. R. China
| | - H Zacharias
- Center for Soft Nanoscience, University of Münster, 48149 Münster, Germany.
- Physics Institute, University of Münster, 48149 Münster, Germany
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Li Y, Zhang P, Sun C, Xiao N, Yang Y, Zhong B, Fang C, Kui G, Liu Z, Li F, Yang S, Feng Y. [Effectiveness of the central government-funded echinococcosis control programme in Tianzhu Tibetan Autonomous County, Gansu Province from 2007 to 2022]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2024; 35:626-632. [PMID: 38413024 DOI: 10.16250/j.32.1374.2023179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
OBJECTIVE To evaluate the effectiveness of the central government-funded echinococcosis control programme in Tianzhu Tibetan Autonomous County, Gansu Province from 2007 to 2022, so as to provide insights into echinococcosis control. METHODS Administrative villages were sampled using a multi-stage cluster random sampling method from Tianzhu Tibetan Autonomous County, Gansu Province from 2007 to 2022, and all residents at ages of 12 years and older in the sampled villages were screened for echinococcosis, and schools were sampled using a cluster sampling method, and all children at ages of 12 years and older in the sampled schools were screened for echinococcosis. Domestic dogs were sampled using a systematic random sampling method, and one domestic dog stool sample was collected from each household. Stray dog stool samples were collected outside the villages, and Echinococcus coproantigens were detected using enzyme-linked immunosorbent assay in domestic and stray dogs. In addition, echinococcosis was screened in sheep and cattle in designated slaughterhouses in Tianzhu Tibetan Autonomous County. The trends in the prevalence of echinococcosis in humans and livestock and the positive rate of Echinococcus coproantigens in dogs were examined with the Cochran-Armitage trend test. In addition, individuals screened for echinococcosis were randomly sampled from 2007 to 2022 for survey on the awareness of echinococcosis control knowledge. RESULTS A total of 290 356 person-times were screened for echinococcosis among residents at ages of 12 years and older in Tianzhu Tibetan Autonomous County, Gansu Province from 2007 to 2022, with 1 094 residents detected with cystic echinococcosis, and the detection of echinococcosis appeared a tendency towards a gradual decline over years (χ2 = 358.602, P < 0.001). A total of 32 931 person-times were screened for echinococcosis among children at ages of 12 years and older in Tianzhu Tibetan Autonomous County, Gansu Province from 2007 to 2022, with 296 children detected with echinococcosis, and the detection of echinococcosis appeared a tendency towards a gradual decline over years (χ2 = 267.673, P < 0.001). A total of 33 230 domestic dog stool samples were tested for Echinococcus coproantigens in Tianzhu Tibetan Autonomous County, Gansu Province from 2007 to 2022, with 1 777 Echinococcus coproantigens-positive samples tested, and the positive rate of Echinococcus coproantigens appeared a tendency towards a decline in domestic dogs over years (χ2 = 2 210.428, P < 0.001), while the positive rate of Echinococcus coproantigens showed a tendency towards a rise in domestic animals from 2016 to 2022 (χ2 = 37.745, P < 0.001). The positive rate of Echinococcus coproantigens remained relatively stable in stray dogs in Tianzhu Tibetan Autonomous County, Gansu Province from 2019 to 2022 (χ2 = 0.315, P = 0.575). A total of 10 973 sheep were screened for echinococcosis in Tianzhu Tibetan Autonomous County from 2007 to 2022, with 334 sheep detected with echinococcosis, and the detection of echinococcosis appeared a tendency towards a decline in sheep over years (χ2 = 53.579, P < 0.001); however, there was no significant change in the detection of echinococcosis during the period from 2015 through 2022 (χ2 = 1.520, P = 0.218). A total of 2 400 cattle were screened for echinococcosis in Tianzhu Tibetan Autonomous County from 2017 to 2022, with 231 cattle detected with echinococcosis, and the detection of echinococcosis showed a tendency towards a decline over years (χ2 = 5.579, P < 0.05). The awareness of echinococcosis control knowledge increased from 44.37% in 2007 to 94.00% in 2022 among residents at ages of 12 years and older and from 52.50% in 2007 to 92.50% in 2022 among children at ages of 12 years and older in Tianzhu Tibetan Autonomous County, respectively. CONCLUSIONS There has been a reduction in the detection of echinococcosis in humans and domestic animals and the positive rate of Echinococcus coproantigens in dogs and a rise in the awareness of the echinococcosis control knowledge following the implementation of the central government-funded echinococcosis control programme in Tianzhu Tibetan Autonomous County, Gansu Province; however, integrated echinococcosis control measures are still required for further control of the prevalence of echinococcosis.
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Affiliation(s)
- Y Li
- Tianzhu Tibetan Autonomous County Center for Disease Control and Prevention, Wuwei, Gansu 733200, China
| | - P Zhang
- Tianzhu Tibetan Autonomous County Center for Disease Control and Prevention, Wuwei, Gansu 733200, China
| | - C Sun
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai 200025, China
| | - N Xiao
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai 200025, China
| | - Y Yang
- Tianzhu Tibetan Autonomous County Center for Disease Control and Prevention, Wuwei, Gansu 733200, China
| | - B Zhong
- Tianzhu Tibetan Autonomous County Center for Disease Control and Prevention, Wuwei, Gansu 733200, China
| | - C Fang
- Tianzhu Tibetan Autonomous County Center for Disease Control and Prevention, Wuwei, Gansu 733200, China
| | - G Kui
- Tianzhu Tibetan Autonomous County Center for Disease Control and Prevention, Wuwei, Gansu 733200, China
| | - Z Liu
- Gansu Center for Disease Control and Prevention, Lanzhou, Gansu 730000, China
| | - F Li
- Gansu Center for Disease Control and Prevention, Lanzhou, Gansu 730000, China
| | - S Yang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai 200025, China
| | - Y Feng
- Gansu Center for Disease Control and Prevention, Lanzhou, Gansu 730000, China
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Wang G, Zhuo N, Liu Z. Comment to: The modified frailty index predicts postoperative morbidity in elective hernia repair patients. Hernia 2024:10.1007/s10029-024-02970-9. [PMID: 38294578 DOI: 10.1007/s10029-024-02970-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/01/2024]
Affiliation(s)
- G Wang
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - N Zhuo
- Department of Nephrology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, 215002, China
| | - Z Liu
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China.
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Musfeldt JL, Singh S, Fan S, Gu Y, Xu X, Cheong SW, Liu Z, Vanderbilt D, Rabe KM. Structural phase purification of bulk HfO 2:Y through pressure cycling. Proc Natl Acad Sci U S A 2024; 121:e2312571121. [PMID: 38266049 PMCID: PMC10835063 DOI: 10.1073/pnas.2312571121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/05/2023] [Indexed: 01/26/2024] Open
Abstract
We combine synchrotron-based infrared absorption and Raman scattering spectroscopies with diamond anvil cell techniques and first-principles calculations to explore the properties of hafnia under compression. We find that pressure drives HfO[Formula: see text]:7%Y from the mixed monoclinic ([Formula: see text]) [Formula: see text] antipolar orthorhombic ([Formula: see text]) phase to pure antipolar orthorhombic ([Formula: see text]) phase at approximately 6.3 GPa. This transformation is irreversible, meaning that upon release, the material is kinetically trapped in the [Formula: see text] metastable state at 300 K. Compression also drives polar orthorhombic ([Formula: see text]) hafnia into the tetragonal ([Formula: see text]) phase, although the latter is not metastable upon release. These results are unified by an analysis of the energy landscape. The fact that pressure allows us to stabilize targeted metastable structures with less Y stabilizer is important to preserving the flat phonon band physics of pure HfO[Formula: see text].
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Affiliation(s)
- J L Musfeldt
- Department of Chemistry, University of Tennessee, Knoxville, TN 37996
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996
| | - Sobhit Singh
- Department of Mechanical Engineering, University of Rochester, Rochester, NY 14627
- Materials Science Program, University of Rochester, Rochester, NY 14627
| | - Shiyu Fan
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY 11973
| | - Yanhong Gu
- Department of Chemistry, University of Tennessee, Knoxville, TN 37996
| | - Xianghan Xu
- Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854
- Rutgers Center for Emergent Materials, Rutgers University, Piscataway, NJ 08854
| | - S-W Cheong
- Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854
- Rutgers Center for Emergent Materials, Rutgers University, Piscataway, NJ 08854
| | - Z Liu
- Department of Physics, University of Illinois, Chicago, IL 60607-7059
| | - David Vanderbilt
- Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854
| | - Karin M Rabe
- Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854
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Han X, Guo Y, Ye H, Chen Z, Hu Q, Wei X, Liu Z, Liang C. Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response. Breast Cancer Res 2024; 26:18. [PMID: 38287356 PMCID: PMC10823720 DOI: 10.1186/s13058-024-01776-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 01/20/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUNDS Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way. The interrelationship between the biomarker, TME phenotypes and recipients' clinical response was also revealed. METHODS In this retrospective multi-cohort investigation, five separate cohorts of breast cancer patients were recruited to measure breast cancer TME phenotypes via a radiomics signature, which was constructed and validated by integrating RNA-seq data with DCE-MRI images for predicting immunotherapy response. Initially, we constructed TME phenotypes using RNA-seq of 1089 breast cancer patients in the TCGA database. Then, parallel DCE-MRI images and RNA-seq of 94 breast cancer patients obtained from TCIA were applied to develop a radiomics-based TME phenotypes signature using random forest in machine learning. The repeatability of the radiomics signature was then validated in an internal validation set. Two additional independent external validation sets were analyzed to reassess this signature. The Immune phenotype cohort (n = 158) was divided based on CD8 cell infiltration into immune-inflamed and immune-desert phenotypes; these data were utilized to examine the relationship between the immune phenotypes and this signature. Finally, we utilized an Immunotherapy-treated cohort with 77 cases who received anti-PD-1/PD-L1 treatment to evaluate the predictive efficiency of this signature in terms of clinical outcomes. RESULTS The TME phenotypes of breast cancer were separated into two heterogeneous clusters: Cluster A, an "immune-inflamed" cluster, containing substantial innate and adaptive immune cell infiltration, and Cluster B, an "immune-desert" cluster, with modest TME cell infiltration. We constructed a radiomics signature for the TME phenotypes ([AUC] = 0.855; 95% CI 0.777-0.932; p < 0.05) and verified it in an internal validation set (0.844; 0.606-1; p < 0.05). In the known immune phenotypes cohort, the signature can identify either immune-inflamed or immune-desert tumor (0.814; 0.717-0.911; p < 0.05). In the Immunotherapy-treated cohort, patients with objective response had higher baseline radiomics scores than those with stable or progressing disease (p < 0.05); moreover, the radiomics signature achieved an AUC of 0.784 (0.643-0.926; p < 0.05) for predicting immunotherapy response. CONCLUSIONS Our imaging biomarker, a practicable radiomics signature, is beneficial for predicting the TME phenotypes and clinical response in anti-PD-1/PD-L1-treated breast cancer patients. It is particularly effective in identifying the "immune-desert" phenotype and may aid in its transformation into an "immune-inflamed" phenotype.
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Affiliation(s)
- Xiaorui Han
- School of Medicine South, China University of Technology, Guangzhou, 510006, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Huifen Ye
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510000, China
| | - Zhihong Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China
| | - Qingru Hu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510000, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
| | - Zaiyi Liu
- School of Medicine South, China University of Technology, Guangzhou, 510006, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Changhong Liang
- School of Medicine South, China University of Technology, Guangzhou, 510006, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
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Zhang X, Sheng Y, Liu Z. Using expertise as an intermediary: Unleashing the power of blockchain technology to drive future sustainable management using hidden champions. Heliyon 2024; 10:e23807. [PMID: 38226273 PMCID: PMC10788455 DOI: 10.1016/j.heliyon.2023.e23807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/10/2023] [Accepted: 12/13/2023] [Indexed: 01/17/2024] Open
Abstract
An overview of blockchain fundamentals and its potential benefits for sustainability is provided. The role of expertise as an intermediary on the blockchain to drive transparency and accountability is examined. This research examines the potential of blockchain technology in the field of economic management and to drive future sustainable development in emerging companies, which are referred to as hidden champions. This study addresses the need for transparent and responsive practices that promote social stability, economic growth, and environmental sustainability. The goals are to analyze economic functions, investigate the formation of appropriate economic patterns, facilitate equitable distribution, and support environmental protection efforts. The research method includes case studies and theoretical frameworks to collect relevant data. The results emphasize the importance of balancing competing interests, promoting security, and strengthening inclusive decision-making processes. This study emphasizes the intersection between economic development and environmental protection and highlights the role of sustainability criteria in guiding land use practices. The conclusion emphasizes that sustainable economic practices are critical for social, economic and environmental development, especially in emerging economies. Practical recommendations are provided to policymakers and stakeholders to improve economic governance frameworks and help achieve the Sustainable Development Goals.
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Affiliation(s)
- Xin Zhang
- School of Business, Applied Technology College of Soochow University, Kunshan, 215325, China
| | - Yifei Sheng
- School of Engineering, University of Manchester, Manchester, United Kingdom
| | - Z. Liu
- Energy Research Center, Energy Economics Institute, Beijing, China
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Xu H, Xu Q, Cong F, Kang J, Han C, Liu Z, Madabhushi A, Lu C. Vision Transformers for Computational Histopathology. IEEE Rev Biomed Eng 2024; 17:63-79. [PMID: 37478035 DOI: 10.1109/rbme.2023.3297604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Computational histopathology is focused on the automatic analysis of rich phenotypic information contained in gigabyte whole slide images, aiming at providing cancer patients with more accurate diagnosis, prognosis, and treatment recommendations. Nowadays deep learning is the mainstream methodological choice in computational histopathology. Transformer, as the latest technological advance in deep learning, learns feature representations and global dependencies based on self-attention mechanisms, which is increasingly gaining prevalence in this field. This article presents a comprehensive review of state-of-the-art vision transformers that have been explored in histopathological image analysis for classification, segmentation, and survival risk regression applications. We first overview preliminary concepts and components built into vision transformers. Various recent applications including whole slide image classification, histological tissue component segmentation, and survival outcome prediction with tailored transformer architectures are then discussed. We finally discuss key challenges revolving around the use of vision transformers and envisioned future perspectives. We hope that this review could provide an elaborate guideline for readers to explore vision transformers in computational histopathology, such that more advanced techniques assisting in the precise diagnosis and treatment of cancer patients could be developed.
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Xu CY, Zhang YS, Luan N, Liu XY, Qin DY, Wang HM, Xiao XP, Zhou SH, Zhang J, Zhang P, Bai YQ, Wang PP, Qi Y, Sun ZW, Liu Z, Ba L, Wang WC, Lu X, Wang M, Guo R, Sun DY, Tao LY, Zhu L. [A multi-dimensional analysis of pollen broadcasting concerns in Chinese population: a large-scale multi-center cross-sectional survey]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2024; 59:2-11. [PMID: 38212136 DOI: 10.3760/cma.j.cn115330-20231011-00137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Objective: To investigate the concern about pollen broadcasting in Chinese population from multiple dimensions and to understand the information about allergic rhinitis (AR) in China by analyzing related factors. Methods: From March 1 to September 30, 2022, a large-scale multi-center cross-sectional survey was conducted based on the Questionnaire Star platform in 21 Chinese hospitals. A total of 7 056 subjects from 7 regions in China: Northeast, North, East, Central, South, Southwest, and Northwest China were included. Basic characteristics (including social demographic characteristics and disease characteristics of AR patients), concern about pollen broadcasting, the willingness of pollen-induced AR (PiAR) patients to receive pollen broadcasting, and the treatment satisfaction rate of AR patients were collected. The chi-square test, multivariate linear regression model, and Logistic regression analysis were used to analyze the concern about pollen broadcasting in the Chinese population and related factors from multiple dimensions. Results: Among 7 056 subjects, 23.02% were concerned about pollen broadcasting. Among 3 176 self-reported AR and 1 019 PiAR patients, 25.60% and 39.16% were concerned about pollen broadcasting, respectively, which was higher than that of non-AR or non-PiAR subjects (χ2 value was 21.74 and 175.11, respectively, both P<0.001). Among AR patients, the proportion of spring and autumn allergen-positive patients concerned about pollen broadcasting was higher than that in perennial allergen-positive patients (χ2 value was 20.90 and 19.51, respectively, both P<0.001). The proportion of AR patients with asthma, sinusitis, allergic conjunctivitis, and cardiovascular and cerebrovascular diseases was higher than those without complications (χ2 value was 50.83, 21.97, 56.78, 7.62, respectively, all P<0.05). The proportion of AR patients in North China who could find pollen broadcasting locally was 31.01%, significantly higher than those in other regions (all P<0.05). Multivariate linear regression model analysis showed that among PiAR patients, those with higher per capita household income and higher AR disease cognition levels had been concerned about pollen broadcasting in the past, and those complicated with allergic conjunctivitis had stronger intention to receive pollen broadcasting (B value was 0.24, 0.13, 0.66, 0.47, respectively, all P<0.05). The higher the disease cognition level of PiAR patients, the stronger their willingness to actively participate in treatment (R2=0.72, P<0.001). Only 18.89% of AR patients felt satisfied with the treatment effect. Logistic regression analysis showed that in AR patients, the treatment satisfaction rate was significantly higher among those concerned about pollen broadcasting compared to those who were not (OR=1.83, P<0.001). Conclusions: Currently, the dissemination of pollen broadcasting in China is hindered by various factors such as disease cognition level. The treatment satisfaction among AR patients remains unsatisfactory.
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Affiliation(s)
- C Y Xu
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University Third Hospital, Beijing 100191, China
| | - Y S Zhang
- Department of Otorhinolaryngology, Yancheng No.1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng 224001, China
| | - N Luan
- Department of Otorhinolaryngology, Peking University Third Hospital Yanqing Hospital, Beijing 102100, China
| | - X Y Liu
- Department of Otorhinolaryngology, Affiliated Hospital of Gansu University of Chinese Medicine, Lanzhou 730030, China
| | - D Y Qin
- Department of Otorhinolaryngology, the First People's Hospital of Qinzhou, Qinzhou 535000, China
| | - H M Wang
- Department of Otorhinolaryngology, Chaoyang Central Hospital, Chaoyang 122000, China
| | - X P Xiao
- Department of Otorhinolaryngology, Hunan Province People Hospital, Changsha 410005, China
| | - S H Zhou
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - J Zhang
- Department of Otorhinolaryngology, Renhuai People's Hospital in Guizhou Province,Renhuai 564500, China
| | - P Zhang
- Department of Otorhinolaryngology, Aohan County Hospital, Chifeng 024300, China
| | - Y Q Bai
- Department of Otorhinolaryngology Head and Neck Surgery, Changzhi City People's Hospital, Changzhi 046000, China
| | - P P Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Y Qi
- Department of Otorhinolaryngology Head and Neck Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Z W Sun
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Aerospace General Hospital, Beijing 100076, China
| | - Z Liu
- Department of Otorhinolaryngology, Yan'an Branch of Peking University Third Hospital (Yan'an City of Traditional Chinese Medicine Hospital), Yan'an 716000, China
| | - L Ba
- Department of Otorhinolaryngology, People's Hospital of the Tibet Autonomous Region, Lhasa 850000, China
| | - W C Wang
- Department of Otorhinolaryngology, Taiyuan Center Hospital, Taiyuan 030000, China
| | - X Lu
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin HuanHu Hospital, Tianjin 300350, China
| | - M Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University People's Hospital, Beijing 100044, China
| | - R Guo
- Department of Otorhinolaryngology, Air Force Medical Center, Beijing 100042, China
| | - D Y Sun
- Department of Otorhinolaryngology, Daqing Oil Field General Hospital, Daqing 163001, China
| | - L Y Tao
- The Clinical Epidemiology Research Center of Peking University Third Hospital, Beijing 100191, China
| | - L Zhu
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University Third Hospital, Beijing 100191, China
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Li DY, Liu Z, Hu ZS, Li J, Liu CW, Xu YJ, Qiu Y, Zhu ZZ. [Effect of different observations on evaluation of cosmetic shoulder balance in adolescent idiopathic scoliosis patients with thoracic curve]. Zhonghua Yi Xue Za Zhi 2024; 104:22-30. [PMID: 38178764 DOI: 10.3760/cma.j.cn112137-20230830-00337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
Objective: To investigate the correlations between cosmetic and radiographic parameters of shoulder balance, as well as the variations in cosmetic shoulder balance observed from different perspectives, among patients with adolescent idiopathic scoliosis (AIS) characterized by thoracic curves. Methods: A total of 43 patients with thoracic curves treated from July to October in 2022 in Nanjing Drum Tower Hospital were recruited in this study. There were 9 males and 34 females with a mean age of (14.3±1.5) years. All participants underwent comprehensive radiographic assessments and were photographed both from posterior and anterior views, focusing on the shoulder region as well as a higher level (maintaining a consistent vertical distance of 180 cm from the ground). Six cosmetic parameters were measured on the photographs: shoulder angle(α1), axilla angle(α2), shoulder area index 1(SAI1), shoulder area index 2 (SAI2), inner shoulder height (SHi) and outer shoulder height (SHo). Eight radiographic parameters were measured on the radiographs: radiographic shoulder height difference (RSHD), first rib angle (FRA), clavicle-rib cage intersection (CRCI), coracoid process height (CPH), T1 tilt, clavicle angle(CA), clavicle chest cage angle difference (CCAD) and Cobb angle. Differences among bilateral cosmetic indicators from different perspectives were analyzed and compared, and their correlation with bilateral radiographic indicators was studied. Results: There was no significant differences between anterior cosmetic parameters and posterior cosmetic parameters at the same level of observation(all P>0.05). However, when observing SHi, SHo, α1, and α2 at the shoulder level, it became evident that they exhibited significantly higher values compared to the corresponding higher level on the same side of the patients' bodies. This contrast was observed in both the dorsal [SHo: (0.11±1.20) cm vs (-0.44±1.39) cm, P=0.005; SHi: (0.64±0.86) cm vs (0.32±0.56) cm, P=0.003; α1:-0.47°±2.27° vs -0.77°±2.49°, P=0.014; α2:-3.06°±3.23° vs -2.21°±3.03°, P=0.034] and ventral [SHo: (0.12±1.29) cm vs (-0.48±1.35) cm, P=0.007; SHi: (0.61±0.88) cm vs (0.30±0.59) cm, P=0.006; α1:-0.46°±2.18° vs -0.69°±2.35°, P=0.018; α2:-3.26°±3.12° vs -2.05°±2.97°, P=0.029] aspects of the patients. SHi and SHo were more sensitive to this difference of height. The correlation coefficients between radiographic parameters and cosmetic aspects at the shoulder level varied from 0.374 to 0.767. Similarly, the correlation coefficients between radiographic parameters and cosmetic factors at the higher level ranged from 0.273 to 0.579 (all P<0.05). Conclusions: The cosmetic parameters had significant difference between different perspective of observation, the cosmetic parameters are needed to be observed at the shoulder level in the evaluation of patients' shoulder balance.
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Affiliation(s)
- D Y Li
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China
| | - Z Liu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Z S Hu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - J Li
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - C W Liu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China
| | - Y J Xu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Y Qiu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Z Z Zhu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
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Shu SB, Bao HD, Zhang X, Gu Q, Liu Z, Zhu ZZ, Qiu Y. [Clinical study of the Cobb+1 to Cobb fusion strategy for Lenke 5C adolescent idiopathic scoliosis patients with the lower lumbar apex]. Zhonghua Yi Xue Za Zhi 2024; 104:10-15. [PMID: 38178762 DOI: 10.3760/cma.j.cn112137-20230916-00476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
Objective: To investigate the indications and surgical outcome of Cobb+1 to Cobb fusion strategy in Lenke 5C adolescent idiopathic scoliosis (AIS) patients with the lower lumbar apex. Methods: The clinical data of Lenke 5C AIS patients treated in Nanjing Drum Tower Hospital from August 2015 to December 2018 were retrospectively analyzed. The patients were followed-up for at least 2 years after surgery and treated with selective Cobb+1 to Cobb fusion strategy. The patients were divided into the normal lumbar apex group (apex location of the main curve was between T12 and L1) and the lower lumbar apex group (apex location of the main curve was below the disc of L1/L2). The occurrence of proximal decompensation in the two groups was compared. In addition, according to whether the patients had proximal decompensation at the last follow-up, the patients in the lower lumbar apex group were further divided into proximal decompensation group and non-decompensation group. The radiographic parameters and Scoliosis Research Society-22 (SRS-22) scores of the two groups were compared. Results: A total of 52 patients (19 cases in the normal lumbar apex group and 33 cases in the lower lumbar apex group), aged (15.3±1.6) years, were followed up for 2-5 (3.2±1.2) years. Six patients (6/19) in the normal lumbar apex group and 5 cases (15.2%) in the lower lumbar apex group showed proximal decompensation during follow-up, and the incidence was significantly higher in the normal lumbar apex group (P=0.034). Within the lower lumbar apex group, the patients with proximal decompensation (n=5) showed similar Risser grade, baseline thoracic Cobb angle, and main Cobb angle as those without proximal decompensation(n=28), and the differences were all not statistically significant (all P>0.05). However, the baseline thoracic/lumbar apical vertebra translation (AVT) ratio was significantly larger in patients with proximal decompensation (0.6±0.2 vs 0.4±0.2, P=0.042), but the postoperative upper instrumented vertebra (UIV) tilt angle was similar (4.5°±2.3° vs 6.2°±3.4°, P=0.312). Conclusion: Cobb+1 to Cobb fusion strategy, selecting UIV at 1 level above upper end vertebra (UEV), could be performed in Lenke 5C patients with the lower lumbar apex location. In addition, UIV could be selected at UEV+1 in patients with small baseline thoracic curve.
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Affiliation(s)
- S B Shu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - H D Bao
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - X Zhang
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Q Gu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Z Liu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Z Z Zhu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Y Qiu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
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Meng L, Sun Y, Zhao X, Meng DM, Liu Z, Adams DC, McDonagh DL, Rasmussen M. Effects of phenylephrine on systemic and cerebral circulations in humans: a systematic review with mechanistic explanations. Anaesthesia 2024; 79:71-85. [PMID: 37948131 DOI: 10.1111/anae.16172] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2023] [Indexed: 11/12/2023]
Abstract
We conducted a systematic review of the literature reporting phenylephrine-induced changes in blood pressure, cardiac output, cerebral blood flow and cerebral tissue oxygen saturation as measured by near-infrared spectroscopy in humans. We used the proportion change of the group mean values reported by the original studies in our analysis. Phenylephrine elevates blood pressure whilst concurrently inducing a reduction in cardiac output. Furthermore, despite increasing cerebral blood flow, it decreases cerebral tissue oxygen saturation. The extent of phenylephrine's influence on cardiac output (r = -0.54 and p = 0.09 in awake humans; r = -0.55 and p = 0.007 in anaesthetised humans), cerebral blood flow (r = 0.65 and p = 0.002 in awake humans; r = 0.80 and p = 0.003 in anaesthetised humans) and cerebral tissue oxygen saturation (r = -0.72 and p = 0.03 in awake humans; r = -0.24 and p = 0.48 in anaesthetised humans) appears closely linked to the magnitude of phenylephrine-induced blood pressure changes. When comparing the effects of phenylephrine in awake and anaesthetised humans, we found no evidence of a significant difference in cardiac output, cerebral blood flow or cerebral tissue oxygen saturation. There was also no evidence of a significant difference in effect on systemic and cerebral circulations whether phenylephrine was given by bolus or infusion. We explore the underlying mechanisms driving the phenylephrine-induced cardiac output reduction, cerebral blood flow increase and cerebral tissue oxygen saturation decrease. Individualised treatment approaches, close monitoring and consideration of potential risks and benefits remain vital to the safe and effective use of phenylephrine in acute care.
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Affiliation(s)
- L Meng
- Department of Anesthesia, Indiana University School of Medicine, IA, Indianapolis, USA
| | - Y Sun
- Department of Anesthesiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - X Zhao
- Department of Anesthesiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - D M Meng
- Choate Rosemary Hall School, CT, Wallingford, USA
| | - Z Liu
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, IA, Indianapolis, USA
| | - D C Adams
- Department of Anesthesia, Indiana University School of Medicine, IA, Indianapolis, USA
| | - D L McDonagh
- Departments of Anesthesiology and Pain Management, Neurological Surgery, Neurology and Neurotherapeutics, UT Southwestern Medical Center, TX, Dallas, USA
| | - M Rasmussen
- Department of Anesthesiology, Section of Neuroanesthesia, Aarhus University Hospital, Aarhus, Denmark
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Al-Zeer B, Amat M, Ambrose C, Ammar H, An Y, Andriaccio L, Ansu K, Apostolidi A, Arai N, Araki H, Araki S, Arbi A, Arechiga O, Armstrong S, Arnold T, Aronoff S, Arriaga W, Arroyo J, Arteaga D, Asahara S, Asai A, Asai N, Asano S, Asawa M, Asmee MF, Aucella F, Augustin M, Avery A, Awad A, Awang IY, Awazawa M, Axler A, Ayub W, Azhari Z, Baccaro R, Badin C, Bagwell B, Bahlmann-Kroll E, Bahtar AZ, Baigent C, Bains D, Bajaj H, Baker R, Baldini E, Banas B, Banerjee D, Banno S, Bansal S, Barberi S, Barnes S, Barnini C, Barot C, Barrett K, Barrios R, Bartolomei Mecatti B, Barton I, Barton J, Basily W, Bavanandan S, Baxter A, Becker L, Beddhu S, Beige J, Beigh S, Bell S, Benck U, Beneat A, Bennett A, Bennett D, Benyon S, Berdeprado J, Bergler T, Bergner A, Berry M, Bevilacqua M, Bhairoo J, Bhandari S, Bhandary N, Bhatt A, Bhattarai M, Bhavsar M, Bian W, Bianchini F, Bianco S, Bilous R, Bilton J, Bilucaglia D, Bird C, Birudaraju D, Biscoveanu M, Blake C, Bleakley N, Bocchicchia K, Bodine S, Bodington R, Boedecker S, Bolduc M, Bolton S, Bond C, Boreky F, Boren K, Bouchi R, Bough L, Bovan D, Bowler C, Bowman L, Brar N, Braun C, Breach A, Breitenfeldt M, Brenner S, Brettschneider B, Brewer A, Brewer G, Brindle V, Brioni E, Brown C, Brown H, Brown L, Brown R, Brown S, Browne D, Bruce K, Brueckmann M, Brunskill N, Bryant M, Brzoska M, Bu Y, Buckman C, Budoff M, Bullen M, Burke A, Burnette S, Burston C, Busch M, Bushnell J, Butler S, Büttner C, Byrne C, Caamano A, Cadorna J, Cafiero C, Cagle M, Cai J, Calabrese K, Calvi C, Camilleri B, Camp S, Campbell D, Campbell R, Cao H, Capelli I, Caple M, Caplin B, Cardone A, Carle J, Carnall V, Caroppo M, Carr S, Carraro G, Carson M, Casares P, Castillo C, Castro C, Caudill B, Cejka V, Ceseri M, Cham L, Chamberlain A, Chambers J, Chan CBT, Chan JYM, Chan YC, Chang E, Chang E, Chant T, Chavagnon T, Chellamuthu P, Chen F, Chen J, Chen P, Chen TM, Chen Y, Chen Y, Cheng C, Cheng H, Cheng MC, Cherney D, Cheung AK, Ching CH, Chitalia N, Choksi R, Chukwu C, Chung K, Cianciolo G, Cipressa L, Clark S, Clarke H, Clarke R, Clarke S, Cleveland B, Cole E, Coles H, Condurache L, Connor A, Convery K, Cooper A, Cooper N, Cooper Z, Cooperman L, Cosgrove L, Coutts P, Cowley A, Craik R, Cui G, Cummins T, Dahl N, Dai H, Dajani L, D'Amelio A, Damian E, Damianik K, Danel L, Daniels C, Daniels T, Darbeau S, Darius H, Dasgupta T, Davies J, Davies L, Davis A, Davis J, Davis L, Dayanandan R, Dayi S, Dayrell R, De Nicola L, Debnath S, Deeb W, Degenhardt S, DeGoursey K, Delaney M, Deo R, DeRaad R, Derebail V, Dev D, Devaux M, Dhall P, Dhillon G, Dienes J, Dobre M, Doctolero E, Dodds V, Domingo D, Donaldson D, Donaldson P, Donhauser C, Donley V, Dorestin S, Dorey S, Doulton T, Draganova D, Draxlbauer K, Driver F, Du H, Dube F, Duck T, Dugal T, Dugas J, Dukka H, Dumann H, Durham W, Dursch M, Dykas R, Easow R, Eckrich E, Eden G, Edmerson E, Edwards H, Ee LW, Eguchi J, Ehrl Y, Eichstadt K, Eid W, Eilerman B, Ejima Y, Eldon H, Ellam T, Elliott L, Ellison R, Emberson J, Epp R, Er A, Espino-Obrero M, Estcourt S, Estienne L, Evans G, Evans J, Evans S, Fabbri G, Fajardo-Moser M, Falcone C, Fani F, Faria-Shayler P, Farnia F, Farrugia D, Fechter M, Fellowes D, Feng F, Fernandez J, Ferraro P, Field A, Fikry S, Finch J, Finn H, Fioretto P, Fish R, Fleischer A, Fleming-Brown D, Fletcher L, Flora R, Foellinger C, Foligno N, Forest S, Forghani Z, Forsyth K, Fottrell-Gould D, Fox P, Frankel A, Fraser D, Frazier R, Frederick K, Freking N, French H, Froment A, Fuchs B, Fuessl L, Fujii H, Fujimoto A, Fujita A, Fujita K, Fujita Y, Fukagawa M, Fukao Y, Fukasawa A, Fuller T, Funayama T, Fung E, Furukawa M, Furukawa Y, Furusho M, Gabel S, Gaidu J, Gaiser S, Gallo K, Galloway C, Gambaro G, Gan CC, Gangemi C, Gao M, Garcia K, Garcia M, Garofalo C, Garrity M, Garza A, Gasko S, Gavrila M, Gebeyehu B, Geddes A, Gentile G, George A, George J, Gesualdo L, Ghalli F, Ghanem A, Ghate T, Ghavampour S, Ghazi A, Gherman A, Giebeln-Hudnell U, Gill B, Gillham S, Girakossyan I, Girndt M, Giuffrida A, Glenwright M, Glider T, Gloria R, Glowski D, Goh BL, Goh CB, Gohda T, Goldenberg R, Goldfaden R, Goldsmith C, Golson B, Gonce V, Gong Q, Goodenough B, Goodwin N, Goonasekera M, Gordon A, Gordon J, Gore A, Goto H, Goto S, Goto S, Gowen D, Grace A, Graham J, Grandaliano G, Gray M, Green JB, Greene T, Greenwood G, Grewal B, Grifa R, Griffin D, Griffin S, Grimmer P, Grobovaite E, Grotjahn S, Guerini A, Guest C, Gunda S, Guo B, Guo Q, Haack S, Haase M, Haaser K, Habuki K, Hadley A, Hagan S, Hagge S, Haller H, Ham S, Hamal S, Hamamoto Y, Hamano N, Hamm M, Hanburry A, Haneda M, Hanf C, Hanif W, Hansen J, Hanson L, Hantel S, Haraguchi T, Harding E, Harding T, Hardy C, Hartner C, Harun Z, Harvill L, Hasan A, Hase H, Hasegawa F, Hasegawa T, Hashimoto A, Hashimoto C, Hashimoto M, Hashimoto S, Haskett S, Hauske SJ, Hawfield A, Hayami T, Hayashi M, Hayashi S, Haynes R, Hazara A, Healy C, Hecktman J, Heine G, Henderson H, Henschel R, Hepditch A, Herfurth K, Hernandez G, Hernandez Pena A, Hernandez-Cassis C, Herrington WG, Herzog C, Hewins S, Hewitt D, Hichkad L, Higashi S, Higuchi C, Hill C, Hill L, Hill M, Himeno T, Hing A, Hirakawa Y, Hirata K, Hirota Y, Hisatake T, Hitchcock S, Hodakowski A, Hodge W, Hogan R, Hohenstatt U, Hohenstein B, Hooi L, Hope S, Hopley M, Horikawa S, Hosein D, Hosooka T, Hou L, Hou W, Howie L, Howson A, Hozak M, Htet Z, Hu X, Hu Y, Huang J, Huda N, Hudig L, Hudson A, Hugo C, Hull R, Hume L, Hundei W, Hunt N, Hunter A, Hurley S, Hurst A, Hutchinson C, Hyo T, Ibrahim FH, Ibrahim S, Ihana N, Ikeda T, Imai A, Imamine R, Inamori A, Inazawa H, Ingell J, Inomata K, Inukai Y, Ioka M, Irtiza-Ali A, Isakova T, Isari W, Iselt M, Ishiguro A, Ishihara K, Ishikawa T, Ishimoto T, Ishizuka K, Ismail R, Itano S, Ito H, Ito K, Ito M, Ito Y, Iwagaitsu S, Iwaita Y, Iwakura T, Iwamoto M, Iwasa M, Iwasaki H, Iwasaki S, Izumi K, Izumi K, Izumi T, Jaafar SM, Jackson C, Jackson Y, Jafari G, Jahangiriesmaili M, Jain N, Jansson K, Jasim H, Jeffers L, Jenkins A, Jesky M, Jesus-Silva J, Jeyarajah D, Jiang Y, Jiao X, Jimenez G, Jin B, Jin Q, Jochims J, Johns B, Johnson C, Johnson T, Jolly S, Jones L, Jones L, Jones S, Jones T, Jones V, Joseph M, Joshi S, Judge P, Junejo N, Junus S, Kachele M, Kadowaki T, Kadoya H, Kaga H, Kai H, Kajio H, Kaluza-Schilling W, Kamaruzaman L, Kamarzarian A, Kamimura Y, Kamiya H, Kamundi C, Kan T, Kanaguchi Y, Kanazawa A, Kanda E, Kanegae S, Kaneko K, Kaneko K, Kang HY, Kano T, Karim M, Karounos D, Karsan W, Kasagi R, Kashihara N, Katagiri H, Katanosaka A, Katayama A, Katayama M, Katiman E, Kato K, Kato M, Kato N, Kato S, Kato T, Kato Y, Katsuda Y, Katsuno T, Kaufeld J, Kavak Y, Kawai I, Kawai M, Kawai M, Kawase A, Kawashima S, Kazory A, Kearney J, Keith B, Kellett J, Kelley S, Kershaw M, Ketteler M, Khai Q, Khairullah Q, Khandwala H, Khoo KKL, Khwaja A, Kidokoro K, Kielstein J, Kihara M, Kimber C, Kimura S, Kinashi H, Kingston H, Kinomura M, Kinsella-Perks E, Kitagawa M, Kitajima M, Kitamura S, Kiyosue A, Kiyota M, Klauser F, Klausmann G, Kmietschak W, Knapp K, Knight C, Knoppe A, Knott C, Kobayashi M, Kobayashi R, Kobayashi T, Koch M, Kodama S, Kodani N, Kogure E, Koizumi M, Kojima H, Kojo T, Kolhe N, Komaba H, Komiya T, Komori H, Kon SP, Kondo M, Kondo M, Kong W, Konishi M, Kono K, Koshino M, Kosugi T, Kothapalli B, Kozlowski T, Kraemer B, Kraemer-Guth A, Krappe J, Kraus D, Kriatselis C, Krieger C, Krish P, Kruger B, Ku Md Razi KR, Kuan Y, Kubota S, Kuhn S, Kumar P, Kume S, Kummer I, Kumuji R, Küpper A, Kuramae T, Kurian L, Kuribayashi C, Kurien R, Kuroda E, Kurose T, Kutschat A, Kuwabara N, Kuwata H, La Manna G, Lacey M, Lafferty K, LaFleur P, Lai V, Laity E, Lambert A, Landray MJ, Langlois M, Latif F, Latore E, Laundy E, Laurienti D, Lawson A, Lay M, Leal I, Leal I, Lee AK, Lee J, Lee KQ, Lee R, Lee SA, Lee YY, Lee-Barkey Y, Leonard N, Leoncini G, Leong CM, Lerario S, Leslie A, Levin A, Lewington A, Li J, Li N, Li X, Li Y, Liberti L, Liberti ME, Liew A, Liew YF, Lilavivat U, Lim SK, Lim YS, Limon E, Lin H, Lioudaki E, Liu H, Liu J, Liu L, Liu Q, Liu WJ, Liu X, Liu Z, Loader D, Lochhead H, Loh CL, Lorimer A, Loudermilk L, Loutan J, Low CK, Low CL, Low YM, Lozon Z, Lu Y, Lucci D, Ludwig U, Luker N, Lund D, Lustig R, Lyle S, Macdonald C, MacDougall I, Machicado R, MacLean D, Macleod P, Madera A, Madore F, Maeda K, Maegawa H, Maeno S, Mafham M, Magee J, Maggioni AP, Mah DY, Mahabadi V, Maiguma M, Makita Y, Makos G, Manco L, Mangiacapra R, Manley J, Mann P, Mano S, Marcotte G, Maris J, Mark P, Markau S, Markovic M, Marshall C, Martin M, Martinez C, Martinez S, Martins G, Maruyama K, Maruyama S, Marx K, Maselli A, Masengu A, Maskill A, Masumoto S, Masutani K, Matsumoto M, Matsunaga T, Matsuoka N, Matsushita M, Matthews M, Matthias S, Matvienko E, Maurer M, Maxwell P, Mayne KJ, Mazlan N, Mazlan SA, Mbuyisa A, McCafferty K, McCarroll F, McCarthy T, McClary-Wright C, McCray K, McDermott P, McDonald C, McDougall R, McHaffie E, McIntosh K, McKinley T, McLaughlin S, McLean N, McNeil L, Measor A, Meek J, Mehta A, Mehta R, Melandri M, Mené P, Meng T, Menne J, Merritt K, Merscher S, Meshykhi C, Messa P, Messinger L, Miftari N, Miller R, Miller Y, Miller-Hodges E, Minatoguchi M, Miners M, Minutolo R, Mita T, Miura Y, Miyaji M, Miyamoto S, Miyatsuka T, Miyazaki M, Miyazawa I, Mizumachi R, Mizuno M, Moffat S, Mohamad Nor FS, Mohamad Zaini SN, Mohamed Affandi FA, Mohandas C, Mohd R, Mohd Fauzi NA, Mohd Sharif NH, Mohd Yusoff Y, Moist L, Moncada A, Montasser M, Moon A, Moran C, Morgan N, Moriarty J, Morig G, Morinaga H, Morino K, Morisaki T, Morishita Y, Morlok S, Morris A, Morris F, Mostafa S, Mostefai Y, Motegi M, Motherwell N, Motta D, Mottl A, Moys R, Mozaffari S, Muir J, Mulhern J, Mulligan S, Munakata Y, Murakami C, Murakoshi M, Murawska A, Murphy K, Murphy L, Murray S, Murtagh H, Musa MA, Mushahar L, Mustafa R, Mustafar R, Muto M, Nadar E, Nagano R, Nagasawa T, Nagashima E, Nagasu H, Nagelberg S, Nair H, Nakagawa Y, Nakahara M, Nakamura J, Nakamura R, Nakamura T, Nakaoka M, Nakashima E, Nakata J, Nakata M, Nakatani S, Nakatsuka A, Nakayama Y, Nakhoul G, Nangaku M, Naverrete G, Navivala A, Nazeer I, Negrea L, Nethaji C, Newman E, Ng SYA, Ng TJ, Ngu LLS, Nimbkar T, Nishi H, Nishi M, Nishi S, Nishida Y, Nishiyama A, Niu J, Niu P, Nobili G, Nohara N, Nojima I, Nolan J, Nosseir H, Nozawa M, Nunn M, Nunokawa S, Oda M, Oe M, Oe Y, Ogane K, Ogawa W, Ogihara T, Oguchi G, Ohsugi M, Oishi K, Okada Y, Okajyo J, Okamoto S, Okamura K, Olufuwa O, Oluyombo R, Omata A, Omori Y, Ong LM, Ong YC, Onyema J, Oomatia A, Oommen A, Oremus R, Orimo Y, Ortalda V, Osaki Y, Osawa Y, Osmond Foster J, O'Sullivan A, Otani T, Othman N, Otomo S, O'Toole J, Owen L, Ozawa T, Padiyar A, Page N, Pajak S, Paliege A, Pandey A, Pandey R, Pariani H, Park J, Parrigon M, Passauer J, Patecki M, Patel M, Patel R, Patel T, Patel Z, Paul R, Paul R, Paulsen L, Pavone L, Peixoto A, Peji J, Peng BC, Peng K, Pennino L, Pereira E, Perez E, Pergola P, Pesce F, Pessolano G, Petchey W, Petr EJ, Pfab T, Phelan P, Phillips R, Phillips T, Phipps M, Piccinni G, Pickett T, Pickworth S, Piemontese M, Pinto D, Piper J, Plummer-Morgan J, Poehler D, Polese L, Poma V, Pontremoli R, Postal A, Pötz C, Power A, Pradhan N, Pradhan R, Preiss D, Preiss E, Preston K, Prib N, Price L, Provenzano C, Pugay C, Pulido R, Putz F, Qiao Y, Quartagno R, Quashie-Akponeware M, Rabara R, Rabasa-Lhoret R, Radhakrishnan D, Radley M, Raff R, Raguwaran S, Rahbari-Oskoui F, Rahman M, Rahmat K, Ramadoss S, Ramanaidu S, Ramasamy S, Ramli R, Ramli S, Ramsey T, Rankin A, Rashidi A, Raymond L, Razali WAFA, Read K, Reiner H, Reisler A, Reith C, Renner J, Rettenmaier B, Richmond L, Rijos D, Rivera R, Rivers V, Robinson H, Rocco M, Rodriguez-Bachiller I, Rodriquez R, Roesch C, Roesch J, Rogers J, Rohnstock M, Rolfsmeier S, Roman M, Romo A, Rosati A, Rosenberg S, Ross T, Rossello X, Roura M, Roussel M, Rovner S, Roy S, Rucker S, Rump L, Ruocco M, Ruse S, Russo F, Russo M, Ryder M, Sabarai A, Saccà C, Sachson R, Sadler E, Safiee NS, Sahani M, Saillant A, Saini J, Saito C, Saito S, Sakaguchi K, Sakai M, Salim H, Salviani C, Sammons E, Sampson A, Samson F, Sandercock P, Sanguila S, Santorelli G, Santoro D, Sarabu N, Saram T, Sardell R, Sasajima H, Sasaki T, Satko S, Sato A, Sato D, Sato H, Sato H, Sato J, Sato T, Sato Y, Satoh M, Sawada K, Schanz M, Scheidemantel F, Schemmelmann M, Schettler E, Schettler V, Schlieper GR, Schmidt C, Schmidt G, Schmidt U, Schmidt-Gurtler H, Schmude M, Schneider A, Schneider I, Schneider-Danwitz C, Schomig M, Schramm T, Schreiber A, Schricker S, Schroppel B, Schulte-Kemna L, Schulz E, Schumacher B, Schuster A, Schwab A, Scolari F, Scott A, Seeger W, Seeger W, Segal M, Seifert L, Seifert M, Sekiya M, Sellars R, Seman MR, Shah S, Shah S, Shainberg L, Shanmuganathan M, Shao F, Sharma K, Sharpe C, Sheikh-Ali M, Sheldon J, Shenton C, Shepherd A, Shepperd M, Sheridan R, Sheriff Z, Shibata Y, Shigehara T, Shikata K, Shimamura K, Shimano H, Shimizu Y, Shimoda H, Shin K, Shivashankar G, Shojima N, Silva R, Sim CSB, Simmons K, Sinha S, Sitter T, Sivanandam S, Skipper M, Sloan K, Sloan L, Smith R, Smyth J, Sobande T, Sobata M, Somalanka S, Song X, Sonntag F, Sood B, Sor SY, Soufer J, Sparks H, Spatoliatore G, Spinola T, Squyres S, Srivastava A, Stanfield J, Staplin N, Staylor K, Steele A, Steen O, Steffl D, Stegbauer J, Stellbrink C, Stellbrink E, Stevens W, Stevenson A, Stewart-Ray V, Stickley J, Stoffler D, Stratmann B, Streitenberger S, Strutz F, Stubbs J, Stumpf J, Suazo N, Suchinda P, Suckling R, Sudin A, Sugamori K, Sugawara H, Sugawara K, Sugimoto D, Sugiyama H, Sugiyama H, Sugiyama T, Sullivan M, Sumi M, Suresh N, Sutton D, Suzuki H, Suzuki R, Suzuki Y, Suzuki Y, Suzuki Y, Swanson E, Swift P, Syed S, Szerlip H, Taal M, Taddeo M, Tailor C, Tajima K, Takagi M, Takahashi K, Takahashi K, Takahashi M, Takahashi T, Takahira E, Takai T, Takaoka M, Takeoka J, Takesada A, Takezawa M, Talbot M, Taliercio J, Talsania T, Tamori Y, Tamura R, Tamura Y, Tan CHH, Tan EZZ, Tanabe A, Tanabe K, Tanaka A, Tanaka A, Tanaka N, Tang S, Tang Z, Tanigaki K, Tarlac M, Tatsuzawa A, Tay JF, Tay LL, Taylor J, Taylor K, Taylor K, Te A, Tenbusch L, Teng KS, Terakawa A, Terry J, Tham ZD, Tholl S, Thomas G, Thong KM, Tietjen D, Timadjer A, Tindall H, Tipper S, Tobin K, Toda N, Tokuyama A, Tolibas M, Tomita A, Tomita T, Tomlinson J, Tonks L, Topf J, Topping S, Torp A, Torres A, Totaro F, Toth P, Toyonaga Y, Tripodi F, Trivedi K, Tropman E, Tschope D, Tse J, Tsuji K, Tsunekawa S, Tsunoda R, Tucky B, Tufail S, Tuffaha A, Turan E, Turner H, Turner J, Turner M, Tuttle KR, Tye YL, Tyler A, Tyler J, Uchi H, Uchida H, Uchida T, Uchida T, Udagawa T, Ueda S, Ueda Y, Ueki K, Ugni S, Ugwu E, Umeno R, Unekawa C, Uozumi K, Urquia K, Valleteau A, Valletta C, van Erp R, Vanhoy C, Varad V, Varma R, Varughese A, Vasquez P, Vasseur A, Veelken R, Velagapudi C, Verdel K, Vettoretti S, Vezzoli G, Vielhauer V, Viera R, Vilar E, Villaruel S, Vinall L, Vinathan J, Visnjic M, Voigt E, von-Eynatten M, Vourvou M, Wada J, Wada J, Wada T, Wada Y, Wakayama K, Wakita Y, Wallendszus K, Walters T, Wan Mohamad WH, Wang L, Wang W, Wang X, Wang X, Wang Y, Wanner C, Wanninayake S, Watada H, Watanabe K, Watanabe K, Watanabe M, Waterfall H, Watkins D, Watson S, Weaving L, Weber B, Webley Y, Webster A, Webster M, Weetman M, Wei W, Weihprecht H, Weiland L, Weinmann-Menke J, Weinreich T, Wendt R, Weng Y, Whalen M, Whalley G, Wheatley R, Wheeler A, Wheeler J, Whelton P, White K, Whitmore B, Whittaker S, Wiebel J, Wiley J, Wilkinson L, Willett M, Williams A, Williams E, Williams K, Williams T, Wilson A, Wilson P, Wincott L, Wines E, Winkelmann B, Winkler M, Winter-Goodwin B, Witczak J, Wittes J, Wittmann M, Wolf G, Wolf L, Wolfling R, Wong C, Wong E, Wong HS, Wong LW, Wong YH, Wonnacott A, Wood A, Wood L, Woodhouse H, Wooding N, Woodman A, Wren K, Wu J, Wu P, Xia S, Xiao H, Xiao X, Xie Y, Xu C, Xu Y, Xue H, Yahaya H, Yalamanchili H, Yamada A, Yamada N, Yamagata K, Yamaguchi M, Yamaji Y, Yamamoto A, Yamamoto S, Yamamoto S, Yamamoto T, Yamanaka A, Yamano T, Yamanouchi Y, Yamasaki N, Yamasaki Y, Yamasaki Y, Yamashita C, Yamauchi T, Yan Q, Yanagisawa E, Yang F, Yang L, Yano S, Yao S, Yao Y, Yarlagadda S, Yasuda Y, Yiu V, Yokoyama T, Yoshida S, Yoshidome E, Yoshikawa H, Young A, Young T, Yousif V, Yu H, Yu Y, Yuasa K, Yusof N, Zalunardo N, Zander B, Zani R, Zappulo F, Zayed M, Zemann B, Zettergren P, Zhang H, Zhang L, Zhang L, Zhang N, Zhang X, Zhao J, Zhao L, Zhao S, Zhao Z, Zhong H, Zhou N, Zhou S, Zhu D, Zhu L, Zhu S, Zietz M, Zippo M, Zirino F, Zulkipli FH. Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial. Lancet Diabetes Endocrinol 2024; 12:39-50. [PMID: 38061371 PMCID: PMC7615591 DOI: 10.1016/s2213-8587(23)00321-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce progression of chronic kidney disease and the risk of cardiovascular morbidity and mortality in a wide range of patients. However, their effects on kidney disease progression in some patients with chronic kidney disease are unclear because few clinical kidney outcomes occurred among such patients in the completed trials. In particular, some guidelines stratify their level of recommendation about who should be treated with SGLT2 inhibitors based on diabetes status and albuminuria. We aimed to assess the effects of empagliflozin on progression of chronic kidney disease both overall and among specific types of participants in the EMPA-KIDNEY trial. METHODS EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA), and included individuals aged 18 years or older with an estimated glomerular filtration rate (eGFR) of 20 to less than 45 mL/min per 1·73 m2, or with an eGFR of 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher. We explored the effects of 10 mg oral empagliflozin once daily versus placebo on the annualised rate of change in estimated glomerular filtration rate (eGFR slope), a tertiary outcome. We studied the acute slope (from randomisation to 2 months) and chronic slope (from 2 months onwards) separately, using shared parameter models to estimate the latter. Analyses were done in all randomly assigned participants by intention to treat. EMPA-KIDNEY is registered at ClinicalTrials.gov, NCT03594110. FINDINGS Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and then followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroups of eGFR included 2282 (34·5%) participants with an eGFR of less than 30 mL/min per 1·73 m2, 2928 (44·3%) with an eGFR of 30 to less than 45 mL/min per 1·73 m2, and 1399 (21·2%) with an eGFR 45 mL/min per 1·73 m2 or higher. Prespecified subgroups of uACR included 1328 (20·1%) with a uACR of less than 30 mg/g, 1864 (28·2%) with a uACR of 30 to 300 mg/g, and 3417 (51·7%) with a uACR of more than 300 mg/g. Overall, allocation to empagliflozin caused an acute 2·12 mL/min per 1·73 m2 (95% CI 1·83-2·41) reduction in eGFR, equivalent to a 6% (5-6) dip in the first 2 months. After this, it halved the chronic slope from -2·75 to -1·37 mL/min per 1·73 m2 per year (relative difference 50%, 95% CI 42-58). The absolute and relative benefits of empagliflozin on the magnitude of the chronic slope varied significantly depending on diabetes status and baseline levels of eGFR and uACR. In particular, the absolute difference in chronic slopes was lower in patients with lower baseline uACR, but because this group progressed more slowly than those with higher uACR, this translated to a larger relative difference in chronic slopes in this group (86% [36-136] reduction in the chronic slope among those with baseline uACR <30 mg/g compared with a 29% [19-38] reduction for those with baseline uACR ≥2000 mg/g; ptrend<0·0001). INTERPRETATION Empagliflozin slowed the rate of progression of chronic kidney disease among all types of participant in the EMPA-KIDNEY trial, including those with little albuminuria. Albuminuria alone should not be used to determine whether to treat with an SGLT2 inhibitor. FUNDING Boehringer Ingelheim and Eli Lilly.
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Sang L, Liu Z, Huang C, Xu J, Wang H. Multiparametric MRI-based radiomics nomogram for predicting the hormone receptor status of HER2-positive breast cancer. Clin Radiol 2024; 79:60-66. [PMID: 37838543 DOI: 10.1016/j.crad.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/28/2023] [Accepted: 09/12/2023] [Indexed: 10/16/2023]
Abstract
AIM To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics nomograms for predicting the hormone receptor (HR) status of HER2-positive breast cancer. MATERIALS AND METHODS Patients with HER2-positive invasive breast cancer were divided randomly into training (68 patients) and validation (30 patients) sets. All were classified as either HR-positive (HR+) or negative (HR-) at histopathology. Two radiologists outlined the three-dimensional (3D) volumetric regions of interest (VOI) on the MRI images. Features (n=1,096) were extracted from the T2-weighted imaging (WI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images separately. Dimensionality was reduced using feature screening. Binary radiomics prediction models were established using a logistic regression classifier and were validated in the validation set. To construct a nomogram, independent predictors were identified using multivariate logistic regression analysis. The predictive efficacy of the model was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS Ten radiomics features were obtained after feature dimensionality reduction based on the merged T2WI, ADC, and DCE images. The diagnostic efficacy of the radiomics signature using the three sequences was better than that of any single sequence (training set AUC: 0.797; validation set AUC: 0.75). Using multivariate logistic regression analysis, the independent predictors for identifying HR status were combined radiomics signature and peritumoural oedema. Nomograms constructed by combining the radiomics signature and peritumoural oedema showed good discrimination in both the training and validation sets (AUC: 0.815 and 0. 805, respectively). CONCLUSION A multiparametric MRI-based nomogram incorporating the radiomics signature and peritumoural oedema can assess the HR status of HER2-positive breast cancer. The resulting model can improve diagnostic accuracy, improving patient outcomes.
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Affiliation(s)
- L Sang
- Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China
| | - Z Liu
- Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China
| | - C Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co. Ltd, Beijing, China
| | - J Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co. Ltd, Beijing, China
| | - H Wang
- Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China.
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Alkhoder H, Liu Z, Reents R. The marker effects of a single-step random regression model for 4 test-day traits in German Holsteins. J Dairy Sci 2024; 107:423-437. [PMID: 37709030 DOI: 10.3168/jds.2023-23793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/22/2023] [Indexed: 09/16/2023]
Abstract
The single-step genomic model has become the golden standard for routine evaluation in livestock species, such as Holstein dairy cattle. The single-step genomic model with direct estimation of marker effects has been proven to be efficient in accurately accounting for millions of genotype records. For diverse applications including frequent genomic evaluation updates on a weekly basis, estimates of the marker effects from the single-step evaluations play a central role in genomic prediction. In this study we focused on exploring the marker effect estimates from the single-step evaluation. Phenotypic, genotypic, and pedigree data were taken from the official evaluation for German dairy breeds in April 2021. A multilactation random regression test-day model was applied to more than 242 million test-day records separately for 4 traits: milk, fat, and protein yields, and somatic cell scores (SCS). Approximately one million genotyped Holstein animals were considered in the single-step genomic evaluations including ∼21 million animals in pedigree. Deregressed multiple across-country breeding values of Holstein bulls having daughters outside Germany were integrated into the national test-day data to increase the reliability of genomic breeding values. To assess the stability and bias of the marker effects of the single-step model, test-day records of the last 4 yr were deleted, and the integrated bulls born in the last 4 yr were truncated from the complete phenotypic dataset. Estimates of the marker effects were shown to be highly correlated, with correlations ∼0.9, between the full and truncated evaluations. Regression slope values of the marker-effect estimates from the full on the truncated evaluations were all close to their expected value, being ∼1.03. Calculated using random regression coefficients of the marker effect estimates, drastically different shapes of the genetic lactation curve were seen for 2 markers on chromosome 14 for the 4 test-day traits. The contribution of individual chromosomes to the total additive genetic variances seemed to follow the polygenic inheritance mode for protein yield and SCS. However, chromosome 14 was found to make an exceptionally large contribution to the total additive genetic variance for milk and fat yields because of markers near the major gene DGAT1. For the first lactation test-day traits, we obtained ∼0 correlations of chromosomal direct genomic values between any pair of the chromosomes; no spurious correlations were found in our analysis, thanks to the large reference population. For trait milk yield, chromosomal direct genomic values appeared to have a large variation in the between-lactation correlations among the chromosomes, especially between first and second or third lactations. The optimal features of the random regression test-day model and the single-step marker model allowed us to track the differences in the shapes of genetic lactation curves down to the individual markers. Furthermore, the single-step random regression test-day model enabled us to better understand the inheritance mode of the yield traits and SCS (e.g., variable chromosomal contributions to the total additive genetic variance and to the genetic correlations between lactations).
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Affiliation(s)
- H Alkhoder
- IT-Solutions for Animal Production (vit), Heinrich-Schroeder-Weg 1, D-27283 Verden, Germany
| | - Z Liu
- IT-Solutions for Animal Production (vit), Heinrich-Schroeder-Weg 1, D-27283 Verden, Germany.
| | - R Reents
- IT-Solutions for Animal Production (vit), Heinrich-Schroeder-Weg 1, D-27283 Verden, Germany
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Cui L, Jiang E, Liu Z, Li J. Relationship between the impacted mandibular third molar and adjacent second molar' external root resorption by cone-bean computed tomography analysis. Med Oral Patol Oral Cir Bucal 2024; 29:e27-e35. [PMID: 37992149 PMCID: PMC10765336 DOI: 10.4317/medoral.26044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 08/24/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND The relationship between the impacted mandibular third molar (IMTM) and the external root resorption (ERR) of the mandibular second molar (MSM) was analysed with cone-beam computed tomography (CBCT). The risk factors affecting the ERR of the MSM were examined to provide a reference. MATERIAL AND METHODS A total of 327 patients (total: 578 teeth) admitted to the Affiliated Hospital of Yanbian University for IMTM extraction from January 2017 to December 2019 was chosen and divided according to gender and age. The correlation between the IMTM and ERR of MSM was analysed, including inclination angle, impaction direction and depth. The relationship of mandibular ascending ramus classification with ERR of MSM was also analysed. In addition, the correlation between the MTM impaction type and the severity of ERR was analysed. RESULTS The incidence of ERR of MSM in male patients was higher than in females (27.9% vs.17.6%, p = 0.018). The occurrence and the site of ERR showed statistical differences in the inclination angle [(≤20°, 3.6%) vs. (21°-40°, 27.1%) vs. (41°-60°, 27.6%) vs. (61°-80°, 25.6%) vs. (>80°, 31.7%), p <0.001], impaction direction [(Vertical, 1.1%) vs. (Mesial, 32.7%) vs. (Horizontal, 25.3%), p <0.001] and depth of MTM [(Low position, 38.6%) vs. (Median position, 32.0%) vs. (High position, 13.7%), p <0.001]. Also, there was a significant difference in the mandibular ascending ramus type [(Class I, 17.4%) vs. (Class II, 32.3%) vs. (Class III, 44.9%), p <0.001]. In addition, the severity of ERR showed statistical differences in the mesial (40.9%, p<0.05), lower impaction (54.5%, p<0.05) depth of MTM and type III of mandibular ascending ramus (63.6%, p<0.05). CONCLUSIONS The inclination angle, impaction direction, and depth of MTM were the influencing factors for the occurrence and site of ERR. Also, mandibular ascending ramus type was the impact fact. For MTM with mesioangular, lower impaction, and mandibular ascending ramus with type III, the ERR of the MSM was severer.
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Affiliation(s)
- L Cui
- Department of Stomatology Affiliated Hospital of Yanbian University No.1327 of Juzi Road, Xinxing District Yanji 133000, China
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Judge PK, Staplin N, Mayne KJ, Wanner C, Green JB, Hauske SJ, Emberson JR, Preiss D, Ng SYA, Roddick AJ, Sammons E, Zhu D, Hill M, Stevens W, Wallendszus K, Brenner S, Cheung AK, Liu ZH, Li J, Hooi LS, Liu WJ, Kadowaki T, Nangaku M, Levin A, Cherney D, Maggioni AP, Pontremoli R, Deo R, Goto S, Rossello X, Tuttle KR, Steubl D, Massey D, Landray MJ, Baigent C, Haynes R, Herrington WG, Abat S, Abd Rahman R, Abdul Cader R, Abdul Hafidz MI, Abdul Wahab MZ, Abdullah NK, Abdul-Samad T, Abe M, Abraham N, Acheampong S, Achiri P, Acosta JA, Adeleke A, Adell V, Adewuyi-Dalton R, Adnan N, Africano A, Agharazii M, Aguilar F, Aguilera A, Ahmad M, Ahmad MK, Ahmad NA, Ahmad NH, Ahmad NI, Ahmad Miswan N, Ahmad Rosdi H, Ahmed I, Ahmed S, Ahmed S, Aiello J, Aitken A, AitSadi R, Aker S, Akimoto S, Akinfolarin A, Akram S, Alberici F, Albert C, Aldrich L, Alegata M, Alexander L, Alfaress S, Alhadj Ali M, Ali A, Ali A, Alicic R, Aliu A, Almaraz R, Almasarwah R, Almeida J, Aloisi A, Al-Rabadi L, Alscher D, Alvarez P, Al-Zeer B, Amat M, Ambrose C, Ammar H, An Y, Andriaccio L, Ansu K, Apostolidi A, Arai N, Araki H, Araki S, Arbi A, Arechiga O, Armstrong S, Arnold T, Aronoff S, Arriaga W, Arroyo J, Arteaga D, Asahara S, Asai A, Asai N, Asano S, Asawa M, Asmee MF, Aucella F, Augustin M, Avery A, Awad A, Awang IY, Awazawa M, Axler A, Ayub W, Azhari Z, Baccaro R, Badin C, Bagwell B, Bahlmann-Kroll E, Bahtar AZ, Baigent C, Bains D, Bajaj H, Baker R, Baldini E, Banas B, Banerjee D, Banno S, Bansal S, Barberi S, Barnes S, Barnini C, Barot C, Barrett K, Barrios R, Bartolomei Mecatti B, Barton I, Barton J, Basily W, Bavanandan S, Baxter A, Becker L, Beddhu S, Beige J, Beigh S, Bell S, Benck U, Beneat A, Bennett A, Bennett D, Benyon S, Berdeprado J, Bergler T, Bergner A, Berry M, Bevilacqua M, Bhairoo J, Bhandari S, Bhandary N, Bhatt A, Bhattarai M, Bhavsar M, Bian W, Bianchini F, Bianco S, Bilous R, Bilton J, Bilucaglia D, Bird C, Birudaraju D, Biscoveanu M, Blake C, Bleakley N, Bocchicchia K, 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Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial. Lancet Diabetes Endocrinol 2024; 12:51-60. [PMID: 38061372 DOI: 10.1016/s2213-8587(23)00322-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND The EMPA-KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. METHODS EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. FINDINGS Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62-0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16-1·59), representing a 50% (42-58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1). INTERPRETATION In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. FUNDING Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council.
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Li SS, Liu Z, Lyu S, Wang S, Li FQ. [Public health risk and prevention and control of sporotrichosis]. Zhonghua Liu Xing Bing Xue Za Zhi 2023; 44:1999-2004. [PMID: 38129160 DOI: 10.3760/cma.j.cn112338-20230608-00357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Sporotrichosis, a fungal infection caused by Sporothrix species, can greatly lead to chronic inflammation of the skin, mucosa, and lymphatic vessels and disseminate systemically sometimes, even threatening life. It is known that Sporothrix is distributed worldwide, while in China, most of the cases were reported in northeast China and parts of south China. Sporothrix globosa is the main source of infection, and other regions may lack relevant awareness and attention to the disease, making it a public health challenge in China. Thus, it is important to understand its epidemiology and public health risks to prevent and control the disease properly.
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Affiliation(s)
- S S Li
- Dermatology Department of the Second Hospital of Jilin University, Changchun 130041, China
| | - Z Liu
- Dermatology Department of the Second Hospital of Jilin University, Changchun 130041, China
| | - S Lyu
- Dermatology Department of the Second Hospital of Jilin University, Changchun 130041, China
| | - S Wang
- Dermatology Department of the Second Hospital of Jilin University, Changchun 130041, China
| | - F Q Li
- Dermatology Department of the Second Hospital of Jilin University, Changchun 130041, China
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30
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Li P, Liu Z, Shan R, Chen ZY, Xu JN, Cao WN, Cui FQ. [Evolution and regional differences in the supportive environment for influenza vaccination among the elderly population in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:2064-2067. [PMID: 38186157 DOI: 10.3760/cma.j.cn112150-20230613-00463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Seasonal influenza leads to a significant disease burden, and older people infected with influenza are susceptible to various complications. Influenza immunization can prevent infection effectively and significantly reduce the risk of complications and severe cases. Creating a supportive environment for vaccination is crucial in advancing the influenza vaccination rate among the elderly population. In China, the present environment for supporting influenza vaccinations among the elderly is primarily comprised of policies for free vaccination and expense reimbursement, which exhibit noteworthy regional variations across cities and regions. This study systematically analyses the supportive environment and regional disparities associated with influenza vaccination among the elderly in China. It aims to comprehend the opportunities for influenza prevention and control resulting from the current background of influenza vaccination and to identify potential health inequality challenges caused by regional differences. The findings should inform the introduction of relevant national policies and programs to protect the health and well-being of the elderly population.
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Affiliation(s)
- P Li
- School of Public Health, Peking University, Beijing 100191, China
| | - Z Liu
- School of Public Health, Peking University, Beijing 100191, China
| | - R Shan
- School of Public Health, Peking University, Beijing 100191, China
| | - Z Y Chen
- School of Public Health, Peking University, Beijing 100191, China
| | - J N Xu
- School of Public Health, Peking University, Beijing 100191, China
| | - W N Cao
- School of Public Health, Peking University, Beijing 100191, China
| | - F Q Cui
- School of Public Health, Peking University, Beijing 100191, China
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31
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Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med 2023; 29:3033-3043. [PMID: 37985692 PMCID: PMC10719100 DOI: 10.1038/s41591-023-02640-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 10/12/2023] [Indexed: 11/22/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.
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Affiliation(s)
- Kai Cao
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Yingda Xia
- DAMO Academy, Alibaba Group, New York, NY, USA
| | - Jiawen Yao
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Xu Han
- Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Lukas Lambert
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Tingting Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Tang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Gang Jin
- Department of Surgery, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Hui Jiang
- Department of Pathology, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Xu Fang
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Isabella Nogues
- Department of Biostatistics, Harvard University T.H. Chan School of Public Health, Cambridge, MA, USA
| | - Xuezhou Li
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Wenchao Guo
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Yu Wang
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Wei Fang
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Mingyan Qiu
- Hupan Laboratory, Hangzhou, China
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Tomas Kovarnik
- Department of Invasive Cardiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Michal Vocka
- Department of Oncology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Yimei Lu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yingli Chen
- Department of Surgery, Shanghai Institution of Pancreatic Disease, Shanghai, China
| | - Xin Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Jian Zhou
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Chuanmiao Xie
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Rong Zhang
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Hong Lu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Gregory D Hager
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Alan L Yuille
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Le Lu
- DAMO Academy, Alibaba Group, New York, NY, USA
| | - Chengwei Shao
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China.
| | - Yu Shi
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Qi Zhang
- Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhejiang University, Hangzhou, China.
| | - Tingbo Liang
- Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhejiang University, Hangzhou, China.
| | - Ling Zhang
- DAMO Academy, Alibaba Group, New York, NY, USA.
| | - Jianping Lu
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China.
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Zhang J, Cui Z, Zhou L, Sun Y, Li Z, Liu Z, Shen D. Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning. IEEE Trans Med Imaging 2023; 42:3944-3955. [PMID: 37756174 DOI: 10.1109/tmi.2023.3319646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.
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Duan J, Zhao Y, Sun Q, Liang D, Liu Z, Chen X, Li Z. Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer. Cancer Med 2023; 12:21256-21269. [PMID: 37962087 PMCID: PMC10726892 DOI: 10.1002/cam4.6704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/08/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data. METHODS MRI-based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis. RESULTS The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin-like growth factor binding, protein localization to membranes, and cytoskeleton-dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05). CONCLUSIONS Our study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.
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Affiliation(s)
- Jingxian Duan
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Yuanshen Zhao
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Qiuchang Sun
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Dong Liang
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of SciencesShenzhenChina
- National Innovation Center for Advanced Medical DevicesShenzhenChina
- Shenzhen United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
| | - Zaiyi Liu
- Department of RadiologyGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of MedicineSouth China University of TechnologyGuangzhouChina
| | - Zhi‐Cheng Li
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of SciencesShenzhenChina
- National Innovation Center for Advanced Medical DevicesShenzhenChina
- Shenzhen United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
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Ye Y, Wu X, Wang H, Ye H, Zhao K, Yao S, Liu Z, Zhu Y, Zhang Q, Liang C. Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer. Ann Med 2023; 55:2215541. [PMID: 37224471 DOI: 10.1080/07853890.2023.2215541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 04/29/2023] [Accepted: 05/14/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND In colorectal cancer (CRC), both tumor invasion and immunological analysis at the tumor invasive margin (IM) are significantly associated with patient prognosis, but have traditionally been reported independently. We propose a new scoring system, the TGP-I score, to assess the association and interactions between tumor growth pattern (TGP) and tumor infiltrating lymphocytes at the IM and to predict its prognostic validity for CRC patient stratification. MATERIALS AND METHODS The types of TGP were assessed in hematoxylin and eosin-stained whole-slide images. The CD3+ T-cells density at the IM was automatically quantified on immunohistochemical-stained slides using a deep learning method. A discovery (N = 347) and a validation (N = 132) cohorts were used to evaluate the prognostic value of the TGP-I score for overall survival. RESULTS The TGP-I score3 (trichotomy) was an independent prognostic factor, with higher TGP-I score3 associated with worse prognosis in the discovery (unadjusted hazard ratio [HR] for high vs. low 3.62, 95% confidence interval [CI] 2.22-5.90; p < 0.001) and validation cohort (unadjusted HR for high vs. low 5.79, 95% CI 1.84-18.20; p = 0.003). The relative contribution of each parameter to predicting survival was analyzed. The TGP-I score3 had similar importance compared to tumor-node-metastasis staging (31.2% vs. 32.9%) and was stronger than other clinical parameters. CONCLUSIONS This automated workflow and the proposed TGP-I score could further provide accurate prognostic stratification and have potential value for supporting the clinical decision-making of stage I-III CRC patients.Key messagesA new scoring system, the TGP-I score, was proposed to assess the association and interactions of TGP and TILs at the tumor invasive margin.TGP-I score could be an independent predictor of prognosis for CRC patients, with higher scores being associated with worse survival.TGP-I score had similar importance compared to tumor-node-metastasis staging and was stronger than other clinical parameters.
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Affiliation(s)
- Yunrui Ye
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, P.R. China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, P.R. China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, P.R. China
| | - Xiaomei Wu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Huihui Wang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, P.R. China
| | - Huifen Ye
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, P.R. China
| | - Ke Zhao
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, P.R. China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, P.R. China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, P.R. China
| | - Zaiyi Liu
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, P.R. China
| | - Yaxi Zhu
- Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, P.R. China
| | - Qingling Zhang
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, P.R. China
| | - Changhong Liang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, P.R. China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, P.R. China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, P.R. China
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Shi Y, Gao L, Tian Y, Bai C, Chen J, Wang J, Li X, Zhang C, Sun Y, Su H, Liu Z. Penpulimab combined with anlotinib in patients with R/M HNSCC after failure of platinum-based chemotherapy: a single-arm, multicenter, phase Ⅱ study. ESMO Open 2023; 8:102194. [PMID: 38100934 PMCID: PMC10774955 DOI: 10.1016/j.esmoop.2023.102194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Treatment regimens for recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) after failure of platinum-based chemotherapy have been illustrated with limited efficacy. PATIENTS AND METHODS Here, we report a single-arm, multicenter, phase Ⅱ study of R/M HNSCC patients treated with a programmed cell death-1 antibody penpulimab (200 mg) and anlotinib (12 mg) after failing at least one line of platinum-based chemotherapy. RESULTS Of 38 patients in total, 13 (34.21%) patients achieved partial response and 16 (42.11%) patients achieved stable disease. After a median follow-up of 7.06 months (range: 4.14-15.70 months), the independent review committee-assessed objective response rate was 34.21%, the disease control rate was 76.32%. The median progression-free survival was 8.35 months (95% confidence interval 5.95-13.11 months). Twelve patients died and the median overall survival (OS) was not reached. The 12-month OS rate was 59.76%. Grade 3/4 treatment-related adverse events occurred in 47.37% of the patients. CONCLUSION Penpulimab combined with anlotinib demonstrated promising efficacy and manageable safety in R/M HNSCC patients after failure of platinum-based chemotherapy.
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Affiliation(s)
- Y Shi
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing.
| | - L Gao
- Third Ward, Department of Radiotherapy, Gansu Provincial Cancer Hospital, Lanzhou, Gansu, China
| | - Y Tian
- Department of Head and Neck Surgery, Gansu Provincial Cancer Hospital, Lanzhou
| | - C Bai
- Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing
| | - J Chen
- Thoracic Medicine Department, Hunan Cancer Hospital, Changsha
| | - J Wang
- Department of Head and Neck Surgery, Gansu Provincial Cancer Hospital, Lanzhou
| | - X Li
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou
| | - C Zhang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing
| | - Y Sun
- Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing
| | - H Su
- Department of Oncology, Tangdu Hospital, Air Force Medical University, Xi'an
| | - Z Liu
- Department of Head and Neck Oncology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
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Abdulhamid MI, Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Anderson DM, Aparin A, Aslam S, Atchison J, Averichev GS, Bairathi V, Baker W, Cap JGB, Barish K, Bhagat P, Bhasin A, Bhatta S, Bordyuzhin IG, Brandenburg JD, Brandin AV, Cai XZ, Caines H, Sánchez MCDLB, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chatterjee A, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Dale-Gau G, Das A, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Gao T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison-Smith H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Keane D, Kechechyan A, Kelsey M, Kimelman B, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Kravtsov P, Kumar L, Kumar S, Elayavalli RK, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Liu C, Liu F, Liu G, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd EM, Lu T, Lukow NS, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Matis HS, Mazer JA, McNamara G, Mi K, Minaev NG, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Odyniec G, Ogawa A, Oh S, Okorokov VA, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Ritter HG, Robertson CW, Rogachevsky OV, Aguilar MAR, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen D, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Stringfellow B, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis A, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tsai OD, Tsang CY, Tu Z, Tyler J, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang J, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu X, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang W, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Hyperon Polarization along the Beam Direction Relative to the Second and Third Harmonic Event Planes in Isobar Collisions at sqrt[s_{NN}]=200 GeV. Phys Rev Lett 2023; 131:202301. [PMID: 38039468 DOI: 10.1103/physrevlett.131.202301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/07/2023] [Accepted: 10/03/2023] [Indexed: 12/03/2023]
Abstract
The polarization of Λ and Λ[over ¯] hyperons along the beam direction has been measured relative to the second and third harmonic event planes in isobar Ru+Ru and Zr+Zr collisions at sqrt[s_{NN}]=200 GeV. This is the first experimental evidence of the hyperon polarization by the triangular flow originating from the initial density fluctuations. The amplitudes of the sine modulation for the second and third harmonic results are comparable in magnitude, increase from central to peripheral collisions, and show a mild p_{T} dependence. The azimuthal angle dependence of the polarization follows the vorticity pattern expected due to elliptic and triangular anisotropic flow, and qualitatively disagrees with most hydrodynamic model calculations based on thermal vorticity and shear induced contributions. The model results based on one of existing implementations of the shear contribution lead to a correct azimuthal angle dependence, but predict centrality and p_{T} dependence that still disagree with experimental measurements. Thus, our results provide stringent constraints on the thermal vorticity and shear-induced contributions to hyperon polarization. Comparison to previous measurements at RHIC and the LHC for the second-order harmonic results shows little dependence on the collision system size and collision energy.
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Affiliation(s)
| | - B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - G Agakishiev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - A Aitbaev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Alekseev
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
- National Research Nuclear University MEPhI, Moscow 115409
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | - A Aparin
- Joint Institute for Nuclear Research, Dubna 141 980
| | - S Aslam
- Indian Institute Technology, Patna, Bihar 801106, India
| | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | | | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - I G Bordyuzhin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | | | - A V Brandin
- National Research Nuclear University MEPhI, Moscow 115409
| | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - A Chatterjee
- National Institute of Technology Durgapur, Durgapur-713209, India
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - T G Dedovich
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A A Derevschikov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S Fazio
- University of Calabria & INFN-Cosenza, Rende 87036, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - T Gao
- Shandong University, Qingdao, Shandong 266237
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - A Hamed
- American University in Cairo, New Cairo 11835, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | | | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - C Hu
- University of Chinese Academy of Sciences, Beijing 101408
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - C Jin
- Rice University, Houston, Texas 77251
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - D Kalinkin
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - A Kechechyan
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | - B Kimelman
- University of California, Davis, California 95616
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L Kochenda
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - P Kravtsov
- National Research Nuclear University MEPhI, Moscow 115409
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Lednicky
- Joint Institute for Nuclear Research, Dubna 141 980
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - G Liu
- South China Normal University, Guangzhou, Guangdong 510631
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- The Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E M Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - V B Luong
- Joint Institute for Nuclear Research, Dubna 141 980
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | - N G Minaev
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - M M Mondal
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - D A Morozov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Mudrokh
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - G Nigmatkulov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - L V Nogach
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Sejong University, Seoul 05006, South Korea
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - A Paul
- University of California, Riverside, California 92521
| | - C Perkins
- University of California, Berkeley, California 94720
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | | | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Texas A&M University, College Station, Texas 77843
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - E Samigullin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Schmitz
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - R Seto
- University of California, Riverside, California 92521
| | - P Seyboth
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | - E Shahaliev
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - T Shao
- Fudan University, Shanghai, 200433
| | - M Sharma
- University of Jammu, Jammu 180001, India
| | - N Sharma
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | | | - D Shen
- Shandong University, Qingdao, Shandong 266237
| | - D Y Shen
- Fudan University, Shanghai, 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana 47306
- Purdue University, West Lafayette, Indiana 47907
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - D N Svirida
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - Z W Sweger
- University of California, Davis, California 95616
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A Taranenko
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - M V Tokarev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Tyler
- Texas A&M University, College Station, Texas 77843
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- University of Science and Technology of China, Hefei, Anhui 230026
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - A N Vasiliev
- National Research Nuclear University MEPhI, Moscow 115409
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Vokal
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
| | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
| | - J Wang
- Shandong University, Qingdao, Shandong 266237
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - X Wu
- University of California, Los Angeles, California 90095
| | - X Wu
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Fudan University, Shanghai, 200433
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - G Xie
- University of Chinese Academy of Sciences, Beijing 101408
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Zhang
- South China Normal University, Guangzhou, Guangdong 510631
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Shandong University, Qingdao, Shandong 266237
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai, 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
- Brookhaven National Laboratory, Upton, New York 11973
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Xu HJ, Yang Q, He P, Luo HH, Deng WM, Liu Z, Luo DH. [Value of radiomics models based on MRI diffusion weighted imaging and apparent diffusion coefficient in differentiating benign and malignant thyroid nodules]. Zhonghua Yi Xue Za Zhi 2023; 103:3279-3286. [PMID: 37926572 DOI: 10.3760/cma.j.cn112137-20230913-00453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Objective: To investigate the value of radiomics models based on magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps in distinguishing benign and malignant thyroid nodules. Methods: A cross-sectional study. Clinical data of 148 thyroid nodules (50 benign, 98 malignant) from 140 patients who underwent thyroid MRI examination in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences between January 2019 and December 2022 were retrospectively analyzed. The nodules were used as the study units, and a leave-one-out method was used to randomly divide the nodules into a training set and a test set at a 7∶3 ratio. Region of interest was segmented and radiomics features were extracted from the DWI and ADC images. In the training set, feature selection was performed using inter-observer agreement analysis, U-test, least absolute shrinkage and selection operator algorithm, and correlation analysis. Four classifiers, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR) were used to build models with the selected features, including the DWI models, ADC models, and combined models. The models were independently tested in the test set. The performance of the radiomics models in distinguishing benign and malignant thyroid nodules was evaluated using the receiver operating characteristic (ROC) curve, with pathological results as the gold standard. Results: Of the 140 patients, there were 40 males and 100 females, with a mean age of (38.4±12.2) years. After feature selection, 11 DWI features and 11 ADC features were used to build the models. In the training set, the AUC values of the combined models were higher than those of the corresponding DWI and ADC models. In the test set, the SVM combined model showed the best predictive performance, with an AUC of 0.873 (95%CI:0.740-0.954), accuracy of 75.6%, sensitivity of 46.7%, specificity of 90.0%, positive predictive value (PPV) of 70.0% and negative predictive value (NPV) of 77.1%, while the RF combined model had an AUC of 0.836 (95%CI:0.695-0.929), accuracy of 77.8%, sensitivity of 40.0%, specificity of 96.7%, PPV of 85.7% and NPV of 76.3%, the KNN combined model had an AUC of 0.832 (95%CI:0.691-0.927), accuracy of 77.8%, sensitivity of 33.3%, specificity of 100%, PPV of 100% and NPV of 75.0%, the LR combined model had an AUC of 0.813 (95%CI:0.669-0.914), accuracy of 77.8%, sensitivity of 60.0%, specificity of 86.7%, PPV of 69.2% and NPV of 81.3%. Conclusions: Radiomics models based on DWI and ADC image features can effectively distinguish benign and malignant thyroid nodules. The SVM combined model had the best prediction performance.
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Affiliation(s)
- H J Xu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - Q Yang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - P He
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - H H Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - W M Deng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - Z Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - D H Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
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Xu Z, Xie Y, Wu L, Chen M, Shi Z, Cui Y, Han C, Lin H, Liu Y, Li P, Chen X, Ding Y, Liu Z. Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics. J Magn Reson Imaging 2023; 58:1580-1589. [PMID: 36797654 DOI: 10.1002/jmri.28647] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Preoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision-making and prognosis. PURPOSE To investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease-free survival (DFS) by using machine learning methods in patients with IBC. STUDY TYPE Retrospective. POPULATION Five hundred and seventy-five women (range: 24-79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N = 386) and validation datasets (N = 189). FIELD STRENGTH/SEQUENCE Axial fat-suppressed T2-weighted turbo spin-echo sequence and dynamic contrast-enhanced with fat-suppressed T1-weighted three-dimensional gradient echo imaging. ASSESSMENT MRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best-performing of the four models to analyze the association with DFS. STATISTICAL TESTS Chi-squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan-Meier curve, log-rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P-value <0.05 was considered statistically significant. RESULTS The model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22-5.80). DATA CONCLUSION The XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zeyan Xu
- School of Medicine, South China University of Technology, Guangzhou, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yu Xie
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Lei Wu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangzhou, China
| | - Minglei Chen
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangzhou, China
| | - Yanfen Cui
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangzhou, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Huan Lin
- School of Medicine, South China University of Technology, Guangzhou, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yu Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Pinxiong Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, China
| | - Yingying Ding
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zaiyi Liu
- School of Medicine, South China University of Technology, Guangzhou, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
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Chen ZF, Liu Z. [Radiation-induced intestinal fibrosis: pathological assessment and pharmacological prevention]. Zhonghua Wei Chang Wai Ke Za Zhi 2023; 26:935-939. [PMID: 37849263 DOI: 10.3760/cma.j.cn441530-20230816-00050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
Although radiotherapy can improve the local control rate of tumors and prolong the survival period of patients, it can also lead to long-term adverse effects such as radiation-induced intestinal fibrosis. Radiation-induced intestinal fibrosis has a high incidence and poses significant challenges to treatment, severely impacting the quality of life of patients. Combining findings from domestic and international research, along with experiences of our center, this article mainly discusses the pathological changes of radiation-induced intestinal fibrosis, as well as the current status and challenges of pathological assessment and pharmacological prevention of this condition. At present, there is no definitive method to reverse the fibrotic pathological changes. Thus, the prevention of fibrosis is a crucial issue to be resolved. In the meantime, there is a lack of ideal assessment methods and effective preventive medications in clinical practice. It is necessary to enhance both basic and clinical research, thoroughly investigate the pathogenesis of the disease, and identify effective intervention targets to promote the diagnosis and treatment of radiation-induced intestinal fibrosis.
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Affiliation(s)
- Z F Chen
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou 350001, China
| | - Z Liu
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou 350001, China
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Chen R, Ye H, Wu Z, Zhou Y, Lin H, Xu Y, He L, Liang C, Liu Z, Wang G. Using the non-distortion IVIM to reduce the need for contrast agents in nasopharyngeal MRI. Magn Reson Imaging 2023; 104:115-120. [PMID: 37844785 DOI: 10.1016/j.mri.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/04/2023] [Accepted: 10/12/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND Patients with nasopharyngeal carcinoma (NPC) who undergo longitudinal follow-up contrast-enhanced MRI are at risk of developing gadolinium deposition in their neural tissue, which may potentially harm them. Therefore, for these patients, a non-contrast-enhanced method is potentially beneficial as an alternative approach to predict enhancement in T1-weighted imaging (CE-T1WI). The traditional intravoxel incoherent motion (IVIM) is one of the non-contrast-enhanced methods; however, the severe distortion and signal loss limit its application in patients with NPC. The present study aimed to investigate whether non-distortion IVIM could reduce the need of CE-T1WI in the follow-up of patients with NPC. METHODS The patients with NPC underwent Turbo Spin-echo MVXD diffusion-weighted imaging-based IVIM (non-distortion IVIM) from November 2021 to May 2022. Firstly, thirty patients with NPC were underwent both non-distortion IVIM and traditional IVIM. The distortion rate (DR) of the non-distortion IVIM was compared with the traditional IVIM. Then, twenty-one NPC patients with tumors (areas >50mm2) were included and correlation coefficient analysis was used to assess the relationship between their non-distortion IVIM and CE-T1WI. Linear regression analysis was performed to determine whether non-distortion IVIM predictors could predict CE-T1WI. RESULTS The correlation was observed between the parameter f of the non-distortion IVIM and the enhancement ratio of CE-T1WI (r = 0.543, P = 0.011). Moreover, the linear regression analysis revealed that f was an independent IVIM predictor of CE-T1WI in patients with NPC (P = 0.011). The DR of the non-distortion IVIM was significantly smaller than that of the traditional IVIM (0.12 ± 0.05 vs 0.48 ± 0.16, P < 0.001). CONCLUSIONS In patients with NPC, non-distortion IVIM showed potential clinical benefits to reduce the need for contrast agents, and it can independently predict the enhancement ratio.
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Affiliation(s)
- Rui Chen
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Huifen Ye
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Zhigang Wu
- MSC Clinical & Technical Solutions, Philips Healthcare, China
| | - Yifen Zhou
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Hui Lin
- Department of Radiation Oncology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Yongzhou Xu
- MSC Clinical & Technical Solutions, Philips Healthcare, China
| | - Lan He
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China.
| | - Guangyi Wang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China.
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Walmsley T, Unwin J, Allum F, Bari S, Boll R, Borne K, Brouard M, Bucksbaum P, Ekanayake N, Erk B, Forbes R, Howard AJ, Eng-Johnsson P, Lee JWL, Liu Z, Manschwetus B, Mason R, Passow C, Peschel J, Rivas D, Rolles D, Rörig A, Rouzée A, Vallance C, Ziaee F, Burt M. Characterizing the multi-dimensional reaction dynamics of dihalomethanes using XUV-induced Coulomb explosion imaging. J Chem Phys 2023; 159:144302. [PMID: 37823458 DOI: 10.1063/5.0172749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
Site-selective probing of iodine 4d orbitals at 13.1 nm was used to characterize the photolysis of CH2I2 and CH2BrI initiated at 202.5 nm. Time-dependent fragment ion momenta were recorded using Coulomb explosion imaging mass spectrometry and used to determine the structural dynamics of the dissociating molecules. Correlations between these fragment momenta, as well as the onset times of electron transfer reactions between them, indicate that each molecule can undergo neutral three-body photolysis. For CH2I2, the structural evolution of the neutral molecule was simultaneously characterized along the C-I and I-C-I coordinates, demonstrating the sensitivity of these measurements to nuclear motion along multiple degrees of freedom.
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Affiliation(s)
- T Walmsley
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - J Unwin
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - F Allum
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - S Bari
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - R Boll
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | - K Borne
- J. R. Macdonald Laboratory, Department of Physics, Kansas State University, Manhattan, Kansas 66506, USA
| | - M Brouard
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - P Bucksbaum
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
| | - N Ekanayake
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - B Erk
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - R Forbes
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
- Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
| | - A J Howard
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
| | - P Eng-Johnsson
- Department of Physics, Lund University, 22100 Lund, Sweden
| | - J W L Lee
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - Z Liu
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - B Manschwetus
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - R Mason
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - C Passow
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - J Peschel
- Department of Physics, Lund University, 22100 Lund, Sweden
| | - D Rivas
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | - D Rolles
- J. R. Macdonald Laboratory, Department of Physics, Kansas State University, Manhattan, Kansas 66506, USA
| | - A Rörig
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | - A Rouzée
- Max-Born-Institute, Max-Born-Straße 2A, 12489 Berlin, Germany
| | - C Vallance
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - F Ziaee
- J. R. Macdonald Laboratory, Department of Physics, Kansas State University, Manhattan, Kansas 66506, USA
| | - M Burt
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
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Ma J, Liu N, Liu Z, Liu Q, Wei F, Wang Z. [Epidemiology of pathogenic tick-borne viruses in China: a review]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:325-330. [PMID: 37926466 DOI: 10.16250/j.32.1374.2023128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Ticks are obligate, haematophagous arthropods that are distributed across the world, which may transmit more than 200 pathogens, including viruses, bacteria and parasites. A large number of tick species are widespread in China, and their transmitting tick-borne viral diseases pose a great threat to human health in endemic foci. This review describes the epidemiology of common, emerging and potentially pathogenic tick-borne viruses in China, and recommends the assessment of public health significance and pathogenicity of emerging tick-borne viruses using reverse microbial etiology, so as to provide insights into the management of emerging tick-borne diseases in China.
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Affiliation(s)
- J Ma
- College of Life Science, Jilin Agricultural University, Changchun, Jilin 130021, China
| | - N Liu
- Research Center for Infectious Diseases and Pathogenic Biology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Z Liu
- Research Center for Infectious Diseases and Pathogenic Biology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Q Liu
- Research Center for Infectious Diseases and Pathogenic Biology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - F Wei
- College of Life Science, Jilin Agricultural University, Changchun, Jilin 130021, China
| | - Z Wang
- Research Center for Infectious Diseases and Pathogenic Biology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
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Wang G, Liu Z. Relationship between hospital size, remoteness and stroke outcome. QJM 2023; 116:819. [PMID: 37498554 DOI: 10.1093/qjmed/hcad183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- G Wang
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Z Liu
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
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Wu M, Chen D, Liu Z, Chen M, Liu R, Wang J, Li X, Tao Q, Yu J. Metformin Antagonizes Radiotherapy-Induced Anti-Tumor Effects via Inhibition of cGAS-STING Pathway Mediated Immune Responses. Int J Radiat Oncol Biol Phys 2023; 117:e268. [PMID: 37785015 DOI: 10.1016/j.ijrobp.2023.06.1230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Radiotherapy induced anti-tumor effects depend on both direct tumor cell death caused by radiation and immune activation mediated by cGAS-STING pathway. Metformin (MTF), which could augment the tumoricidal efficiency of radiation, is indicated to be a radiosensitizer by basic research. However, several large prospective clinical trials proved otherwise. In present study, we intend to interrogate the effects of MTF on radiotherapy-induced anti-tumor immune responses and try to explain the inconsistent outcomings of radiotherapy combined with MTF in basic research and clinical practice. MATERIALS/METHODS To explore the effects of MTF on radiotherapy induced anti-tumor effects, tumor models were established using E0771, B16F10 and LLC cell lines in both immunocompetent and immunodeficient mice. To investigate the composition and function of immune cells in tumor microenvironments, single-cell transcriptome sequencing of CD45+ cells sorted from tumor microenvironments were carried out, and flow cytometry and multiple immunofluorescence analysis were then performed for validation. To reveal the possible mechanisms, tumor cells were subjected to radiotherapy in the presence or absence of MTF in vitro, and RNA-sequencing was then employed followed by subsequent validation with western blotting, real-time qPCR and flow cytometry. RESULTS We found that systematic administration of MTF could significantly inhibit radiotherapy-induced anti-tumor effects in immunocompetent mouse models. Single cell sequencing of CD45+ cells sorted from tumor microenvironments and further validation showed that administration of MTF dramatically attenuated the infiltration and cytotoxic capacity of CD8+ T cells after radiotherapy. cGAS-STING pathway in tumor cells was required for maximum efficiency of radiotherapy, while MTF curbed cGAS-STING pathway after radiotherapy in a dose-dependent pattern by enhancing autophagy and reducing cytoplasmic mitochondrial DNA accumulation, which contributed to compromised anti-tumor effects. CONCLUSION Our findings indicated that MTF could antagonize radiotherapy-mediated anti-tumor effects by inhibiting the activation of cGAS-STING pathway and subsequent immune responses, which may partially explain the unsatisfied outcomes of radiotherapy combined with MTF in clinical practices. Since the anti-tumor effects of radiotherapy rely not only on the tumor-killing efficiency of radiation but also on systematic immune responses, our findings suggest that cautions are needed when MTF is administrated with radiotherapy in clinical practice.
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Affiliation(s)
- M Wu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - D Chen
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Z Liu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - M Chen
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - R Liu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - X Li
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Q Tao
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Yu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
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Furuhama A, Kitazawa A, Yao J, Matos Dos Santos CE, Rathman J, Yang C, Ribeiro JV, Cross K, Myatt G, Raitano G, Benfenati E, Jeliazkova N, Saiakhov R, Chakravarti S, Foster RS, Bossa C, Battistelli CL, Benigni R, Sawada T, Wasada H, Hashimoto T, Wu M, Barzilay R, Daga PR, Clark RD, Mestres J, Montero A, Gregori-Puigjané E, Petkov P, Ivanova H, Mekenyan O, Matthews S, Guan D, Spicer J, Lui R, Uesawa Y, Kurosaki K, Matsuzaka Y, Sasaki S, Cronin MTD, Belfield SJ, Firman JW, Spînu N, Qiu M, Keca JM, Gini G, Li T, Tong W, Hong H, Liu Z, Igarashi Y, Yamada H, Sugiyama KI, Honma M. Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project. SAR QSAR Environ Res 2023; 34:983-1001. [PMID: 38047445 DOI: 10.1080/1062936x.2023.2284902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023]
Abstract
Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.
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Affiliation(s)
- A Furuhama
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
| | - A Kitazawa
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
| | - J Yao
- Key Laboratory of Fluorine and Nitrogen Chemistry and Advanced Materials (Chinese Academy of Sciences), Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences (SIOC, CAS), Shanghai, China
| | - C E Matos Dos Santos
- Department of Computational Toxicology and In Silico Innovations, Altox Ltd, São Paulo-SP, Brazil
| | - J Rathman
- MN-AM, Nuremberg, Germany/Columbus, OH, USA
| | - C Yang
- MN-AM, Nuremberg, Germany/Columbus, OH, USA
| | | | - K Cross
- In Silico Department, Instem, Conshohocken, PA, USA
| | - G Myatt
- In Silico Department, Instem, Conshohocken, PA, USA
| | - G Raitano
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS (IRFMN), Milano, Italy
| | - E Benfenati
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS (IRFMN), Milano, Italy
| | | | | | | | | | - C Bossa
- Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - C Laura Battistelli
- Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - R Benigni
- Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy
- Alpha-PreTox, Rome, Italy
| | - T Sawada
- Faculty of Regional Studies, Gifu University, Gifu, Japan
- xenoBiotic Inc, Gifu, Japan
| | - H Wasada
- Faculty of Regional Studies, Gifu University, Gifu, Japan
| | - T Hashimoto
- Faculty of Regional Studies, Gifu University, Gifu, Japan
| | - M Wu
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - R Barzilay
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - P R Daga
- Simulations Plus, Lancaster, CA, USA
| | - R D Clark
- Simulations Plus, Lancaster, CA, USA
| | | | | | | | - P Petkov
- LMC - Bourgas University, Bourgas, Bulgaria
| | - H Ivanova
- LMC - Bourgas University, Bourgas, Bulgaria
| | - O Mekenyan
- LMC - Bourgas University, Bourgas, Bulgaria
| | - S Matthews
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - D Guan
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - J Spicer
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - R Lui
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Y Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - K Kurosaki
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - Y Matsuzaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - S Sasaki
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - M T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - S J Belfield
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - J W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - N Spînu
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - M Qiu
- Evergreen AI, Inc, Toronto, Canada
| | - J M Keca
- Evergreen AI, Inc, Toronto, Canada
| | - G Gini
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
| | - T Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
| | - W Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
| | - H Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
| | - Z Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
- Integrative Toxicology, Nonclinical Drug Safety, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA
| | - Y Igarashi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan
| | - H Yamada
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan
| | - K-I Sugiyama
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
| | - M Honma
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
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Xue J, Shi R, Ma J, Liu Z, Feng G, Chen QQ, Li Y, He Y, Ji S, Shi J, Zhu X, Zhou J. Concurrent Chemoradiotherapy plus Programmed Death-1 (PD-1) Blockade for Locally Advanced Cervical Cancer: Preliminary Results of a Single-Arm, Open-Label, Phase II Trial. Int J Radiat Oncol Biol Phys 2023; 117:e542-e543. [PMID: 37785675 DOI: 10.1016/j.ijrobp.2023.06.1838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) This study aims to assess the anti-tumor activity and safety of concurrent chemoradiotherapy plus PD-1 blockade in patients with locally advanced cervical cancer. MATERIALS/METHODS This is a single-arm, open-label, prospective phase II study. The key inclusion criteria were treatment-naive patients aged 18-75 years with stage II A2-IVA (FIGO 2018) locally advanced cervical cancer. All patients were treated with concurrent chemoradiotherapy including 2 cycle cisplatin (75mg/m2, for three days, every 3 weeks[Q3W]), nedaplatin or carboplatin can be selected for patients who can't tolerate cisplatin. After CCRT, patients achieving complete response (CR), partial responses(PR), stable disease(SD) received adjuvant chemotherapy (docetaxel 75 mg/m2 day 1+ cisplatin DDP 25 mg/m2 day 1-3, Q3W) for 2 cycle. PD-1 blockade Sintilimab and Tislelizumab was administered intravenously at 200 mg every 3 weeks up to 1 year or until disease progression, unacceptable toxicity, or withdrawal of consent. The primary endpoint was objective response rate (ORR) assessed by investigators per Response Evaluation Criteria In Solid Tumours (RECIST) version 1.1. Secondary endpoints were the 12, 24-month overall survival (OS) rates, the 12, 24-month disease free survival (DFS) rates and safety. RESULTS From February 2020 to June 2022, a total of 15 patients was enrolled. Median age was 57 years (range, 36-74 years). Stage IIA1 was documented in 2 patients, stage IIA2 in two patients, stage IIIA in one patient, stage IIIC1 in eight patients, and stage IVA in two patients. And 66.7% (10/15) of patients had Metastatic lymph node. Four patients received adjuvant chemotherapy. The ORR was 100%, with 4 patients achieving CR and 11 PR. The 12 and 24-month OS rates are 93.3% and 84%, the 12 and 24-month DFS rates are 86% and 75.4%, respectively. Treatment-related adverse events (TRAEs) occurred in 86.7% (13/15) of patients. Grade 3 TRAEs are leukocyte (n = 1), thrombocytopenia (n = 1), hepatitis (n = 1), skin reaction (n = 1). No treatment-related deaths occurred. And IFN-γ was significantly elevated after radiotherapy (p = 0.0073). CONCLUSION Concurrent chemoradiotherapy plus PD-1 blockade showed promising antitumor activity and manageable toxicities in patients with locally advanced cervical cancer. Long-term outcomes are still pending to further evaluate their therapeutic effects. (ChiCTR2000032856).
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Affiliation(s)
- J Xue
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - R Shi
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - J Ma
- The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Z Liu
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - G Feng
- Department of Gynecology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - Q Q Chen
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Y Li
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - Y He
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - S Ji
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - J Shi
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - X Zhu
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - J Zhou
- Department of Radiotherapy Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
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Zhao M, Feng L, Zhao K, Cui Y, Li Z, Ke C, Yang X, Qiu Q, Lu W, Liang Y, Xie C, Wan X, Liu Z. An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer. Br J Cancer 2023; 129:1095-1104. [PMID: 37558922 PMCID: PMC10539304 DOI: 10.1038/s41416-023-02384-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Accurately assessing the risk of recurrence in patients with locally advanced rectal cancer (LARC) before treatment is important for the development of treatment strategies. The purpose of this study is to develop an MRI-based scoring system to predict the risk of recurrence in patients with LARC. METHODS This was a multicenter observational study that enrolled participants who underwent neoadjuvant chemoradiotherapy. To evaluate the risk of recurrence in these patients, we developed the mrDEC scoring system and assessed inter-reader agreement. Additionally, we plotted Kaplan-Meier curves to compare the 3-year disease-free survival (DFS) and 5-year overall survival (OS) rates among patients with different mrDEC scores. RESULTS A total of 1287 patients with LARC were included in this study. We observed substantial inter-reader agreement for mrDEC. Based on the mrDEC scores ranging from 0 to 3, the patients were categorized into four groups. The 3-year DFS rates for the groups were 91.0%, 79.5%, 65.5%, and 44.0% (P < 0.0001), respectively, and the 5-year OS rates were 92.9%, 87.1%, 74.8%, and 44.5%, respectively (P < 0.0001). CONCLUSIONS The mrDEC scoring system proved to be an effective tool for predicting the prognosis of patients with LARC and can assist clinicians in clinical decision-making.
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Affiliation(s)
- Minning Zhao
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Lili Feng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Yanfen Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Zhenhui Li
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Chenglu Ke
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xinyue Yang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Qing Qiu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Weirong Lu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanting Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - ChuanMiao Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
| | - Xiangbo Wan
- Provincial Key Laboratory of Radiation Medicine in Henan (Under construction), The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- Department of Radiation Oncology, the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
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Li S, Zhu X, Song M, Xiang Y, Zhang Y, Wang HZ, Geng J, Liu Z, Teng H, Cai Y, Li Y, Wang W. Outcomes and Failure Patterns after Chemoradiotherapy for Locally Advanced Rectal Cancer with Positive Lateral Pelvic Lymph Nodes: A Propensity Score-Matched Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e314. [PMID: 37785131 DOI: 10.1016/j.ijrobp.2023.06.2345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Locally advanced rectal cancer (LARC) combined with positive lateral pelvic lymph nodes (LPLN) tends to present worse prognosis. However, for those patients it remains unclear whether other combination high-risk factors affect the prognosis. This study aimed to use propensity score matching (PSM) to examine long-term outcomes and failure patterns in patients with positive vs. negative LPLN. MATERIALS/METHODS Patients with LARC were retrospectively divided into LPLN-positive and LPLN-negative groups. LPLN-positivity was defined as lymph node short diameter greater than or equal to 7 mm with specific morphological features. Clinical characteristics were compared between the groups using the chi-square test. PSM was applied to balance these differences. Progression-free survival (PFS) and overall survival (OS), and local-regional recurrence (LRR) and distant metastasis (DM) rates were compared between the groups using the Kaplan-Meier method and log-rank tests. RESULTS Prior to PSM, a total of 651 LARC patients were included. The LPLN-positive group had higher rates of lower location (53.1% vs. 43.0%, P = 0.025), mesorectal fascia (MRF)-positive (53.9% vs. 35.4%, P<0.001) and extramural venous invasion (EMVI)-positive (51.2% vs. 27.2%, P<0.001) disease than the LPLN-negative group. After PSM, there were 114 patients for each group along with the balanced clinical factors, and both groups had comparable surgery, pathologic complete response (pCR), and ypN stage rates. The median follow-up time was 45.9 months, 3-year OS (88.3% vs. 92.1%, P = 0.276) and LRR (5.7% vs. 2.8%, P = 0.172) rates were comparable between LPLN-positive and LPLN-negative groups. Meanwhile, despite no statistical difference, 3-year PFS (78.8% vs. 85.9%, P = 0.065) and DM (20.4% vs. 13.3%, P = 0.061) rates slightly differed between the groups. Among 10 patients with LRR, seven (70.0%) had lateral pelvic recurrence, among them, five patients were LPLN-positive, and four (80.0%) of these patients did not receive simultaneous integrated boost intensity-modulated radiotherapy (SIB- IMRT).45 patients were diagnosed with DM, 11 (40.7%) LPLN-positive and 3 (17.6%) LPLN-negative patients were diagnosed with oligometastases (P = 0.109). CONCLUSION Our study shows there is a tendency of worse PFS and DM in LPLN-positive than LPLN-negative patients, for LPLN-positive patients, oligometastases account for a large proportion of all distant metastases.
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Affiliation(s)
- S Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - X Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - M Song
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Xiang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - H Z Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - J Geng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Z Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - H Teng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Cai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - W Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
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Li S, Li Z, Wang L, Wu M, Chen X, He C, Xu Y, Dong M, Liang Y, Chen X, Liu Z. CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer. Eur Radiol 2023; 33:6861-6871. [PMID: 37171490 DOI: 10.1007/s00330-023-09688-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES The aim of this study is to evaluate the feasibility of clinicopathological characteristics and computed tomography (CT) morphological features in predicting lymph node metastasis (LNM) for patients with T1 colorectal cancer (CRC). METHODS A total of 144 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in our study. The clinicopathological characteristics and CT morphological features were assessed by two observers. Univariate and multiple logistic regression analyses were used to identify significant LNM predictive variables. Then a model was developed using the independent predictive factors. The predictive model was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate the calibration curve and relative C-index. RESULTS LNM were found in 30/144 patients (20.83%). Four independent risk factors were determined in the multiple logistic regression analysis, including presence of necrosis (adjusted odds ratio [OR] = 10.32, 95% confidence interval [CI] 1.96-54.3, p = 0.004), irregular outer border (adjusted OR = 5.94, 95% CI 1.39-25.45, p = 0.035), and heterogeneity enhancement (adjusted OR = 7.35, 95% CI 3.11-17.38, p = 0.007), as well as tumor location (adjusted ORright-sided colon = 0.05 [0.01-0.60], p = 0.018; adjusted ORrectum = 0.22 [0.06-0.83], p = 0.026). In the internal validation cohort, the model showed good calibration and good discrimination with a C-index of 0.89. CONCLUSIONS There are significant associations between lymphatic metastasis status and tumor location as well as CT morphologic features in T1 CRC, which could help the doctor make decisions for additional surgery after endoscopic resection. KEY POINTS • LNM more frequently occurs in left-sided T1 colon cancer than in right-sided T1 colon and rectal cancer. • CT morphologic features are risk factors for LNM of T1 CRC, which may be related to fundamental biological behaviors. • The combination of tumor location and CT morphologic features can more effectively assist in predicting LNM in patients with T1 CRC, and decrease the rate of unnecessary extra surgeries after endoscopic resection.
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Affiliation(s)
- Suyun Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
- School of Medicine, South China University of Technology, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Zhenhui Li
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China
| | - Li Wang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, 511400, China
| | - Mimi Wu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Xiaobo Chen
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Chutong He
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China
| | - Yao Xu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
- School of Medicine, South China University of Technology, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Mengyi Dong
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, 511400, China
| | - Yanting Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China.
- School of Medicine, South China University of Technology, Guangzhou, 510006, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
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Wang SX, Yang Y, Xie H, Yang X, Liu Z, Li H, Huang W, Luo WJ, Lei Y, Sun Y, Ma J, Chen Y, Liu LZ, Mao YP. Delta-Radiomics Guides Adaptive De-Intensification after Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma in the IMRT Era. Int J Radiat Oncol Biol Phys 2023; 117:S152-S153. [PMID: 37784386 DOI: 10.1016/j.ijrobp.2023.06.574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) In the setting of intensity-modulated radiotherapy (IMRT) and induction chemotherapy (IC), the benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma (LANPC). This study aimed to construct a delta-radiomics model for benefit prediction and patient selection for omitting concurrent chemotherapy. MATERIALS/METHODS Between December 2009 and December 2015, a total of 718 patients with LANPC treated with IC+IMRT or IC+concurrent chemoradiotherapy (CCRT) were retrospectively enrolled and randomly assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from magnetic resonance images of pre-IC and post-IC. Interclass correlation coefficients and Pearson correlation coefficients were calculated to select robust radiomic features. After univariate Cox analysis, a delta-radiomics signature was built using the LASSO-Cox regression. A nomogram incorporating the delta-radiomics signature and clinical prognostic factors was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated by Kaplan-Meier methods. The primary outcome was overall survival (OS). RESULTS The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. It yielded an area under the receiver operating characteristic curve (AUC) of 0.77 (95% confidence interval [CI] 0.71 to 0.82) for the training set and 0.71 (95% CI 0.61 to 0.81) for the validation set. The nomogram composed of the delta-radiomic signature, age, T category, N category, pre-treatment Epstein-Barr virus DNA, and treatment showed great calibration and discrimination performance with an AUC of 0.80 (95% CI 0.75 to 0.85) for the training set and 0.75 (95% CI 0.64 to 0.85) for the validation set. Risk stratification by the nomogram excluding the treatment variable resulted in two risk groups with distinct OS. Significant better outcomes were observed in the high-risk patients with IC+CCRT compared to those with IC+IMRT (5-year OS: 73.8% vs. 61.4% in the training set and 85.8% vs. 65.6% in the validation set; all log-rank p < 0.05), while comparable outcomes between IC+CCRT and IC+IMRT were shown for the low-risk patients (95.8% vs. 95.8% in the training set and 92.2% vs. 88.3% in the validation set; all log-rank p > 0.05). CONCLUSION The delta-radiomics signature was identified as an independent indicator of LANPC. Integrating clinical predictors with the delta-radiomics signature, the radiomics-based nomogram could predict individual's survival outcomes and benefits from concurrent chemotherapy after IC for LANPC. Low-risk patients with LANPC determined by the nomogram may be potential candidates for omission of concurrent chemotherapy following IC in the IMRT era.
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Affiliation(s)
- S X Wang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Y Yang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - H Xie
- Imaging Diagnosis and Interventional Center, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - X Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, Guangzhou, China
| | - Z Liu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - H Li
- Imaging Diagnosis and Interventional Center, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W Huang
- Imaging Diagnosis and Interventional Center, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W J Luo
- Department of Medical Oncology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Y Lei
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Y Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - J Ma
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Y Chen
- Department of head and neck surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - L Z Liu
- Imaging Diagnosis and Interventional Center, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y P Mao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
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