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James J, Law M, Sengupta S, Saunders C. Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review. World J Surg Oncol 2024; 22:127. [PMID: 38725006 PMCID: PMC11084006 DOI: 10.1186/s12957-024-03394-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 04/28/2024] [Indexed: 05/12/2024] Open
Abstract
Sentinel node biopsy (SNB) is routinely performed in people with node-negative early breast cancer to assess the axilla. SNB has no proven therapeutic benefit. Nodal status information obtained from SNB helps in prognostication and can influence adjuvant systemic and locoregional treatment choices. However, the redundancy of the nodal status information is becoming increasingly apparent. The accuracy of radiological assessment of the axilla, combined with the strong influence of tumour biology on systemic and locoregional therapy requirements, has prompted many to consider alternative options for SNB. SNB contributes significantly to decreased quality of life in early breast cancer patients. Substantial improvements in workflow and cost could accrue by removing SNB from early breast cancer treatment. We review the current viewpoints and ideas for alternative options for assessing and managing a clinically negative axilla in patients with early breast cancer (EBC). Omitting SNB in selected cases or replacing SNB with a non-invasive predictive model appear to be viable options based on current literature.
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Affiliation(s)
- Justin James
- Eastern Health, Melbourne, Australia.
- Monash University, Melbourne, Australia.
- Department of Breast and Endocrine Surgery, Maroondah Hospital, Davey Drive, Ringwood East, Melbourne, VIC, 3135, Australia.
| | - Michael Law
- Eastern Health, Melbourne, Australia
- Monash University, Melbourne, Australia
| | - Shomik Sengupta
- Eastern Health, Melbourne, Australia
- Monash University, Melbourne, Australia
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Liu W, Chen W, Xia J, Lu Z, Fu Y, Li Y, Tan Z. Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer. BMC Med Imaging 2024; 24:91. [PMID: 38627678 PMCID: PMC11020672 DOI: 10.1186/s12880-024-01255-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. METHODS Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores. RESULTS DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001). CONCLUSIONS DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.
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Affiliation(s)
- Wenci Liu
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Wubiao Chen
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Jun Xia
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Zhendong Lu
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Youwen Fu
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Yuange Li
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Zhi Tan
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China.
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Guo Y, Zhang H, Yuan L, Chen W, Zhao H, Yu QQ, Shi W. Machine learning and new insights for breast cancer diagnosis. J Int Med Res 2024; 52:3000605241237867. [PMID: 38663911 PMCID: PMC11047257 DOI: 10.1177/03000605241237867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 02/21/2024] [Indexed: 04/28/2024] Open
Abstract
Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.
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Affiliation(s)
- Ya Guo
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Heng Zhang
- Department of Laboratory Medicine, Shandong Daizhuang Hospital, Jining, Shandong Province, China
| | - Leilei Yuan
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Weidong Chen
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Haibo Zhao
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Qing-Qing Yu
- Phase I Clinical Research Centre, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Wenjie Shi
- Molecular and Experimental Surgery, University Clinic for General-, Visceral-, Vascular- and Trans-Plantation Surgery, Medical Faculty University Hospital Magdeburg, Otto-von Guericke University, Magdeburg, Germany
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Lai J, Chen Z, Liu J, Zhu C, Huang H, Yi Y, Cai G, Liao N. A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study. Int J Surg 2024; 110:2162-2177. [PMID: 38215256 PMCID: PMC11019980 DOI: 10.1097/js9.0000000000001082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/27/2023] [Indexed: 01/14/2024]
Abstract
BACKGROUND Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients. METHODS In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing. RESULTS For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05). CONCLUSION Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.
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Affiliation(s)
- Jianguo Lai
- Department of Breast Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Yuexiu District, Guangzhou, Guangdong
| | - Zijun Chen
- The Second Clinical School of Southern Medical University, Guangzhou
| | - Jie Liu
- Department of Breast Cancer, Affiliated Foshan Maternity and Child Healthcare Hospital, Southern Medical University
| | - Chao Zhu
- Department of Blood Transfusion, The First Affiliated Hospital of Nanchang University
| | - Haoxuan Huang
- Department of Urology, Third Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Ying Yi
- Department of Radiology, The First People's Hospital of Foshan, Foshan, Guangdong
| | - Gengxi Cai
- Department of Breast Surgery, The First People’s Hospital of Foshan, Foshan, Guangdong
| | - Ning Liao
- Department of Breast Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Yuexiu District, Guangzhou, Guangdong
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Zhu Y, Ma Y, Zhai Z, Liu A, Wang Y, Zhang Y, Li H, Zhao M, Han P, Yin L, He N, Wu Y, Sechopoulos I, Ye Z, Caballo M. Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study. Eur Radiol 2024; 34:2576-2589. [PMID: 37782338 DOI: 10.1007/s00330-023-10256-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 07/09/2023] [Accepted: 07/30/2023] [Indexed: 10/03/2023]
Abstract
OBJECTIVES To develop a radiomics model in contrast-enhanced cone-beam breast CT (CE-CBBCT) for preoperative prediction of axillary lymph node (ALN) status and metastatic burden of breast cancer. METHODS Two hundred and seventy-four patients who underwent CE-CBBCT examination with two scanners between 2012 and 2021 from two institutions were enrolled. The primary tumor was annotated in each patient image, from which 1781 radiomics features were extracted with PyRadiomics. After feature selection, support vector machine models were developed to predict ALN status and metastatic burden. To avoid overfitting on a specific patient subset, 100 randomly stratified splits were made to assign the patients to either training/fine-tuning or test set. Area under the receiver operating characteristic curve (AUC) of these radiomics models was compared to those obtained when training the models only with clinical features and combined clinical-radiomics descriptors. Ground truth was established by histopathology. RESULTS One hundred and eighteen patients had ALN metastasis (N + (≥ 1)). Of these, 74 had low burden (N + (1~2)) and 44 high burden (N + (≥ 3)). The remaining 156 patients had none (N0). AUC values across the 100 test repeats in predicting ALN status (N0/N + (≥ 1)) were 0.75 ± 0.05 (0.67~0.93, radiomics model), 0.68 ± 0.07 (0.53~0.85, clinical model), and 0.74 ± 0.05 (0.67~0.88, combined model). For metastatic burden prediction (N + (1~2)/N + (≥ 3)), AUC values were 0.65 ± 0.10 (0.50~0.88, radiomics model), 0.55 ± 0.10 (0.40~0.80, clinical model), and 0.64 ± 0.09 (0.50~0.90, combined model), with all the ranges spanning 0.5. In both cases, the radiomics model was significantly better than the clinical model (both p < 0.01) and comparable with the combined model (p = 0.56 and 0.64). CONCLUSIONS Radiomics features of primary tumors could have potential in predicting ALN metastasis in CE-CBBCT imaging. CLINICAL RELEVANCE STATEMENT The findings support potential clinical use of radiomics for predicting axillary lymph node metastasis in breast cancer patients and addressing the limited axilla coverage of cone-beam breast CT. KEY POINTS • Contrast-enhanced cone-beam breast CT-based radiomics could have potential to predict N0 vs. N + (≥ 1) and, to a limited extent, N + (1~2) vs. N + (≥ 3) from primary tumor, and this could help address the limited axilla coverage, pending future verifications on larger cohorts. • The average AUC of radiomics and combined models was significantly higher than that of clinical models but showed no significant difference between themselves. • Radiomics features descriptive of tumor texture were found informative on axillary lymph node status, highlighting a higher heterogeneity for tumor with positive axillary lymph node.
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Affiliation(s)
- Yueqiang Zhu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Yue Ma
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Zhenzhen Zhai
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Mei-Hua-Dong Road, Xiangzhou District, Zhuhai, 519000, China
| | - Aidi Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Yafei Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Haijie Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Mengran Zhao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Peng Han
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Lu Yin
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Ni He
- Department of Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dong-Feng-Dong Road, Yuexiu District, Guangzhou, 510060, China
| | - Yaopan Wu
- Department of Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dong-Feng-Dong Road, Yuexiu District, Guangzhou, 510060, China
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
- Dutch Expert Center for Screening (LRCB), PO Box 6873, Nijmegen, 6503 GJ, The Netherlands
- Technical Medicine Centre, University of Twente, PO Box 217, Enschede, 7500 AE, The Netherlands
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China.
| | - Marco Caballo
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
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Sun R, Zhang M, Yang L, Yang S, Li N, Huang Y, Song H, Wang B, Huang C, Hou F, Wang H. Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study. Insights Imaging 2024; 15:21. [PMID: 38270647 PMCID: PMC10811316 DOI: 10.1186/s13244-023-01569-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 11/09/2023] [Indexed: 01/26/2024] Open
Abstract
OBJECTIVE To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients. METHODS We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model's performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan-Meier survival curve to analyze the prognosis of BCa patients. RESULTS The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659-1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival. CONCLUSION The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively. CRITICAL RELEVANCE STATEMENT Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients. KEY POINTS • Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival.
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Affiliation(s)
- Rui Sun
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Meng Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Lei Yang
- Department of Radiology, Qingdao Center Hospital, Qingdao, 266042, Shandong, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250000, Shandong, China
| | - Na Li
- Department of Radiology, The People's Hospital of Zhangqiu Area, Jinan, 250200, Shandong, China
| | - Yonghua Huang
- Department of Radiology, The Puyang Oilfield General Hospital, Puyang, 457001, Henan, China
| | - Hongzheng Song
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Bo Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, 100080, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
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Fusco R, Granata V, Simonetti I, Setola SV, Iasevoli MAD, Tovecci F, Lamanna CMP, Izzo F, Pecori B, Petrillo A. An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies. Curr Oncol 2024; 31:403-424. [PMID: 38248112 PMCID: PMC10814313 DOI: 10.3390/curroncol31010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Maria Assunta Daniela Iasevoli
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Filippo Tovecci
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Ciro Michele Paolo Lamanna
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Biagio Pecori
- Division of Radiation Protection and Innovative Technology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
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Zhu Y, Ma Y, Zhang Y, Liu A, Wang Y, Zhao M, Li H, He N, Wu Y, Ye Z. Radiomics nomogram for predicting axillary lymph node metastasis-a potential method to address the limitation of axilla coverage in cone-beam breast CT: a bi-center retrospective study. LA RADIOLOGIA MEDICA 2023; 128:1472-1482. [PMID: 37857980 DOI: 10.1007/s11547-023-01731-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 09/21/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE Cone-beam breast CT (CBBCT) has an inherent limitation that the axilla cannot be imaged in its entirety. We aimed to develop and validate a nomogram based on clinical factors and contrast-enhanced (CE) CBBCT radiomics features to predict axillary lymph node (ALN) metastasis and complement limited axilla coverage. MATERIAL AND METHODS This retrospective study included 312 patients with breast cancer from two hospitals who underwent CE-CBBCT examination in a clinical trial (NCT01792999) during 2012-2020. Patients from TCIH comprised training set (n = 176) and validation set (n = 43), and patients from SYSUCC comprised external test set (n = 93). 3D ROIs were delineated manually and radiomics features were extracted by 3D Slicer software. RadScore was calculated and radiomics model was constructed after feature selection. Clinical model was built on independent predictors. Nomogram was developed with independent clinical predictors and RadScore. Diagnostic performance was compared among three models by ROC curve, and decision curve analysis (DCA) was used to evaluate the clinical utility of nomogram. RESULTS A total of 139 patients were ALN positive and 173 patients were negative. Twelve radiomics features remained after feature selection. Location and focality were selected as independent predictors for ALN status. The AUC of nomogram in external test set was higher than that of clinical model (0.80 vs. 0.66, p = 0.012). DCA demonstrated that the nomogram had higher overall net benefit than that of clinical model. CONCLUSION The nomogram combined CE-CBBCT-based radiomics features and clinical factors could have potential in distinguishing ALN positive from negative and addressing the limitation of axilla coverage in CBBCT.
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Affiliation(s)
- Yueqiang Zhu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Yue Ma
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Aidi Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Yafei Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Mengran Zhao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Haijie Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Ni He
- Department of Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dong-Feng-Dong Road, Yuexiu District, Guangzhou, 510060, China
| | - Yaopan Wu
- Department of Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dong-Feng-Dong Road, Yuexiu District, Guangzhou, 510060, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China.
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Wu P, Jiang Y, Xing H, Song W, Cui X, Wu XL, Xu G. Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study. Phys Med Biol 2023; 68:175023. [PMID: 37524093 DOI: 10.1088/1361-6560/acec2d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
Background. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC).Methods. The clinical and ultrasound imaging data, including brightness mode (B-mode) and color Doppler flow imaging, of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics and the decision curve analysis.Results. The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the PC, VC, test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.Conclusion. The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, ultrasound imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients. Our code is available publicly in the repository athttps://github.com/wupeiyan/MDLRN.
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Affiliation(s)
- Peiyan Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Yan Jiang
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Hanshuo Xing
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Wenbo Song
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xing Long Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Guoping Xu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
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Zhang H, Cao W, Liu L, Meng Z, Sun N, Meng Y, Fei J. Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound. J Transl Med 2023; 21:337. [PMID: 37211604 DOI: 10.1186/s12967-023-04201-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/14/2023] [Indexed: 05/23/2023] Open
Abstract
OBJECTIVES To explore an optimal model to predict the response of patients with axillary lymph node (ALN) positive breast cancer to neoadjuvant chemotherapy (NAC) with machine learning using clinical and ultrasound-based radiomic features. METHODS In this study, 1014 patients with ALN-positive breast cancer confirmed by histological examination and received preoperative NAC in the Affiliated Hospital of Qingdao University (QUH) and Qingdao Municipal Hospital (QMH) were included. Finally, 444 participants from QUH were divided into the training cohort (n = 310) and validation cohort (n = 134) based on the date of ultrasound examination. 81 participants from QMH were used to evaluate the external generalizability of our prediction models. A total of 1032 radiomic features of each ALN ultrasound image were extracted and used to establish the prediction models. The clinical model, radiomics model, and radiomics nomogram with clinical factors (RNWCF) were built. The performance of the models was assessed with respect to discrimination and clinical usefulness. RESULTS Although the radiomics model did not show better predictive efficacy than the clinical model, the RNWCF showed favorable predictive efficacy in the training cohort (AUC, 0.855; 95% CI 0.817-0.893), the validation cohort (AUC, 0.882; 95% CI 0.834-0.928), and the external test cohort (AUC, 0.858; 95% CI 0.782-0.921) compared with the clinical factor model and radiomics model. CONCLUSIONS The RNWCF, a noninvasive, preoperative prediction tool that incorporates a combination of clinical and radiomics features, showed favorable predictive efficacy for the response of node-positive breast cancer to NAC. Therefore, the RNWCF could serve as a potential noninvasive approach to assist personalized treatment strategies, guide ALN management, avoiding unnecessary ALND.
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Affiliation(s)
- Hao Zhang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Wen Cao
- Department of Medical Record Management, The Affiliated Hospital of Qingdao University, Pingdu District, Qingdao, Shandong, China
| | - Lianjuan Liu
- Department of Ultrasound, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, Shandong, China
| | - Zifan Meng
- Department of Blood Transfusion, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Ningning Sun
- Department of Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yuanyuan Meng
- Department of Cardiac Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jie Fei
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China.
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Xu M, Yang H, Yang Q, Teng P, Hao H, Liu C, Yu S, Liu G. Radiomics nomogram based on digital breast tomosynthesis: preoperative evaluation of axillary lymph node metastasis in breast carcinoma. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04859-z. [PMID: 37208454 DOI: 10.1007/s00432-023-04859-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 05/13/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE This study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma. METHODS The data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted. RESULTS The clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status. CONCLUSION The radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer.
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Affiliation(s)
- Maolin Xu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Huimin Yang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Qi Yang
- Department of Radiology, The First Hospital of Jilin University, No.71 Xinmin Street, Changchun, 130012, China.
| | - Peihong Teng
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Haifeng Hao
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Chang Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Shaonan Yu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China.
| | - Guifeng Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China.
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