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Liao NQ, Deng ZJ, Wei W, Lu JH, Li MJ, Ma L, Chen QF, Zhong JH. Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma. Comput Struct Biotechnol J 2024; 24:247-257. [PMID: 38617891 PMCID: PMC11015163 DOI: 10.1016/j.csbj.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024] Open
Abstract
Objectives Combination therapy of lenvatinib and immune checkpoint inhibitors (CLICI) has emerged as a promising approach for managing unresectable hepatocellular carcinoma (HCC). However, the response to such treatment is observed in only a subset of patients, underscoring the pressing need for reliable methods to identify potential responders. Materials & methods This was a retrospective analysis involving 120 patients with unresectable HCC. They were divided into training (n = 72) and validation (n = 48) cohorts. We developed an interpretable deep learning model using multiphase computed tomography (CT) images to predict whether patients will respond or not to CLICI treatment, based on the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1). We evaluated the models' performance and analyzed the impact of each CT phase. Critical regions influencing predictions were identified and visualized through heatmaps. Results The multiphase model outperformed the best biphase and uniphase models, achieving an area under the curve (AUC) of 0.802 (95% CI = 0.780-0.824). The portal phase images were found to significantly enhance the model's predictive accuracy. Heatmaps identified six critical features influencing treatment response, offering valuable insights to clinicians. Additionally, we have made this model accessible via a web server at http://uhccnet.com/ for ease of use. Conclusions The integration of multiphase CT images with deep learning-generated heatmaps for predicting treatment response provides a robust and practical tool for guiding CLICI therapy in patients with unresectable HCC.
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Affiliation(s)
- Nan-Qing Liao
- School of Medical, Guangxi University, Nanning, China
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Zhu-Jian Deng
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Wei Wei
- Radiology Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jia-Hui Lu
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Min-Jun Li
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Liang Ma
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Qing-Feng Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Jian-Hong Zhong
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
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Li F, Qin T, Li B, Qu S, Pan L, Zhang P, Sun Q, Cai W, Gao Q, Jiao M, Li J, Ai X, Ma J, Gale RP, Xu Z, Xiao Z. Predicting survival in patients with myelodysplastic/myeloproliferative neoplasms with SF3B1 mutation and thrombocytosis. Leukemia 2024; 38:1334-1341. [PMID: 38714876 DOI: 10.1038/s41375-024-02262-2] [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: 02/06/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 06/05/2024]
Abstract
We investigated data from 180 consecutive patients with myelodysplastic/myeloproliferative neoplasms with SF3B1 mutation and thrombocytosis (MDS/MPN-SF3B1-T) who were diagnosed according to the 2022 World Health Organization (WHO) classification of myeloid neoplasms to identify covariates associated with survival. At a median follow-up of 48 months (95% confidence interval [CI] 35-61 months), the median survival was 69 months (95% CI 59-79 months). Patients with bone marrow ring sideroblasts (RS) < 15% had shorter median overall survival (OS) than did those with bone marrow RS ≥ 15% (41 months [95% CI 32-50 months] versus 76 months [95% CI 59-93 months]; P < 0.001). According to the univariable analyses of OS, age ≥ 65 years (P < 0.001), hemoglobin concentration (Hb) < 80 g/L (P = 0.090), platelet count (PLT) ≥ 800 × 10E + 9/L (P = 0.087), bone marrow RS < 15% (P < 0.001), the Revised International Prognostic Scoring System (IPSS-R) cytogenetic category intermediate/poor/very poor (P = 0.005), SETBP1 mutation (P = 0.061) and SRSF2 mutation (P < 0.001) were associated with poor survival. Based on variables selected from univariable analyses, two separate survival prediction models, a clinical survival model, and a clinical-molecular survival model, were developed using multivariable analyses with the minimum value of the Akaike information criterion (AIC) to specifically predict outcomes in patients with MDS/MPN-SF3B1-T according to the 2022 WHO classification.
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Affiliation(s)
- Fuhui Li
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Tiejun Qin
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Bing Li
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Shiqiang Qu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Lijuan Pan
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Peihong Zhang
- Hematologic Pathology Center, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Qi Sun
- Hematologic Pathology Center, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Wenyu Cai
- Hematologic Pathology Center, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Qingyan Gao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Meng Jiao
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Junjie Li
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Xiaofei Ai
- Hematologic Pathology Center, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Jiao Ma
- Hematologic Pathology Center, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Robert Peter Gale
- Centre for Haematology, Department of Immunology and Inflammation, Imperial College of Science, Technology and Medicine, London, UK
| | - Zefeng Xu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
| | - Zhijian Xiao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
- MDS and MPN Centre, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
- Hematologic Pathology Center, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
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Zhao L, Bao J, Wang X, Qiao X, Shen J, Zhang Y, Jin P, Ji Y, Zhang J, Su Y, Ji L, Li Z, Lu J, Hu C, Shen H, Tian J, Liu J. Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study. J Magn Reson Imaging 2024; 59:2101-2112. [PMID: 37602942 DOI: 10.1002/jmri.28963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/02/2023] [Accepted: 08/02/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. PURPOSE To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). STUDY TYPE Retrospective. POPULATION Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). FIELD STRENGTH/SEQUENCES T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T. ASSESSMENT The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter. STATISTICAL TESTS Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. RESULTS The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]). DATA CONCLUSION TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Litao Zhao
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Jie Bao
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaomeng Qiao
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Junkang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yueyue Zhang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Pengfei Jin
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yanting Ji
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Department of Radiology, The Affiliated Zhangjiagang Hospital of Soochow University, Zhangjiagang, China
| | - Ji Zhang
- Department of Radiology, The People's Hospital of Taizhou, Taizhou, China
| | - Yueting Su
- Department of Radiology, The People's Hospital of Taizhou, Taizhou, China
| | - Libiao Ji
- Department of Radiology, Changshu No.1 People's Hospital, Changshu, China
| | - Zhenkai Li
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
| | - Jian Lu
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, China
| | - Jiangang Liu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, China
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, China
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Zhang J, Zhang Q, Zhao B, Shi G. Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients. Abdom Radiol (NY) 2024:10.1007/s00261-024-04331-7. [PMID: 38796795 DOI: 10.1007/s00261-024-04331-7] [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: 03/07/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 05/29/2024]
Abstract
PURPOSE Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients. METHODS This multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve. Model fitting was evaluated using calibration curves, and clinical utility was assessed through decision curve analysis (DCA). RESULTS The nomogram exhibited excellent performance. The area under the ROC curve (AUC) was 0.848 [95% confidence interval (CI), 0.793-0.893)], 0.802 (95% CI 0.688-0.889), and 0.751 (95% CI 0.652-0.833) for the training, internal validation, and external validation sets, respectively. The AUCs of the P-R curves were 0.838 (95% CI 0.756-0.895), 0.541 (95% CI 0.329-0.740), and 0.556 (95% CI 0.376-0.722) for the corresponding sets. The nomogram outperformed the clinical model and handcrafted signature across all sets (all P < 0.05). The nomogram model demonstrated good calibration and provided greater net benefit within the relevant threshold range compared to other models. CONCLUSION This study created a deep learning nomogram using CECT images and clinical data to predict NAC response in LAGC patients undergoing surgical resection, offering personalized treatment insights.
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Affiliation(s)
- Jingjing Zhang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China
| | - Qiang Zhang
- Department of Radiation Oncology, The First Hospital of Qinhuangdao, Qinhuangdao, People's Republic of China
| | - Bo Zhao
- Department of Medical Imaging, The First Hospital of Qinhuangdao, Qinhuangdao, People's Republic of China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.
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O'Sullivan NJ, Temperley HC, Horan MT, Kamran W, Corr A, O'Gorman C, Saadeh F, Meaney JM, Kelly ME. Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review. Abdom Radiol (NY) 2024:10.1007/s00261-024-04330-8. [PMID: 38744703 DOI: 10.1007/s00261-024-04330-8] [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: 10/27/2023] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 05/16/2024]
Abstract
Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77-0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies.
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Affiliation(s)
- Niall J O'Sullivan
- Department of Radiology, St. James's Hospital, Dublin, Ireland.
- School of Medicine, Trinity College Dublin, Dublin, Ireland.
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland.
| | | | - Michelle T Horan
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland
| | - Waseem Kamran
- Department of Gynaecology, St. James's Hospital, Dublin, Ireland
| | - Alison Corr
- Department of Radiology, St. James's Hospital, Dublin, Ireland
| | | | - Feras Saadeh
- Department of Gynaecology, St. James's Hospital, Dublin, Ireland
| | - James M Meaney
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- School of Medicine, Trinity College Dublin, Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland
| | - Michael E Kelly
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- Department of Surgery, St. James's Hospital, Dublin, Ireland
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Zhao YS, Lai QP, Tang H, Luo RJ, He ZW, Huang W, Wang LY, Zhang ZT, Lin SH, Qin WJ, Xu F. Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning. Front Med (Lausanne) 2024; 11:1386161. [PMID: 38784232 PMCID: PMC11112035 DOI: 10.3389/fmed.2024.1386161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
Background Fungal infections are associated with high morbidity and mortality in the intensive care unit (ICU), but their diagnosis is difficult. In this study, machine learning was applied to design and define the predictive model of ICU-acquired fungi (ICU-AF) in the early stage of fungal infections using Random Forest. Objectives This study aimed to provide evidence for the early warning and management of fungal infections. Methods We analyzed the data of patients with culture-positive fungi during their admission to seven ICUs of the First Affiliated Hospital of Chongqing Medical University from January 1, 2015, to December 31, 2019. Patients whose first culture was positive for fungi longer than 48 h after ICU admission were included in the ICU-AF cohort. A predictive model of ICU-AF was obtained using the Least Absolute Shrinkage and Selection Operator and machine learning, and the relationship between the features within the model and the disease severity and mortality of patients was analyzed. Finally, the relationships between the ICU-AF model, antifungal therapy and empirical antifungal therapy were analyzed. Results A total of 1,434 cases were included finally. We used lasso dimensionality reduction for all features and selected six features with importance ≥0.05 in the optimal model, namely, times of arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation. The area under the curve of the model for predicting ICU-AF was 0.981 in the test set, with a sensitivity of 0.960 and specificity of 0.990. The times of arterial catheter (p = 0.011, OR = 1.057, 95% CI = 1.053-1.104) and invasive mechanical ventilation (p = 0.007, OR = 1.056, 95%CI = 1.015-1.098) were independent risk factors for antifungal therapy in ICU-AF. The times of arterial catheter (p = 0.004, OR = 1.098, 95%CI = 0.855-0.970) were an independent risk factor for empirical antifungal therapy. Conclusion The most important risk factors for ICU-AF are the six time-related features of clinical parameters (arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation), which provide early warning for the occurrence of fungal infection. Furthermore, this model can help ICU physicians to assess whether empiric antifungal therapy should be administered to ICU patients who are susceptible to fungal infections.
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Affiliation(s)
- Yi-Si Zhao
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing, China
| | - Qing-Pei Lai
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hong Tang
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ren-Jie Luo
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhi-Wei He
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Huang
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liu-Yang Wang
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng-Tao Zhang
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shi-Hui Lin
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wen-Jian Qin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Fang Xu
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Jiang W, Wang H, Dong X, Yu X, Zhao Y, Chen D, Yan B, Cheng J, Zhuo S, Wang H, Yan J. Pathomics Signature for Prognosis and Chemotherapy Benefits in Stage III Colon Cancer. JAMA Surg 2024; 159:519-528. [PMID: 38416471 PMCID: PMC10902777 DOI: 10.1001/jamasurg.2023.8015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/12/2023] [Indexed: 02/29/2024]
Abstract
Importance The current TNM staging system may not provide adequate information for prognostic purposes and to assess the potential benefits of chemotherapy for patients with stage III colon cancer. Objective To develop and validate a pathomics signature to estimate prognosis and benefit from chemotherapy using hematoxylin-eosin (H-E)-stained slides. Design, Setting, and Participants This retrospective prognostic study used data from consecutive patients with histologically confirmed stage III colon cancer at 2 medical centers between January 2012 and December 2015. A total of 114 pathomics features were extracted from digital H-E-stained images from Nanfang Hospital of Southern Medical University, Guangzhou, China, and a pathomics signature was constructed using a least absolute shrinkage and selection operator Cox regression model in the training cohort. The associations of the pathomics signature with disease-free survival (DFS) and overall survival (OS) were evaluated. Patients at the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, formed the validation cohort. Data analysis was conducted from September 2022 to March 2023. Main Outcomes and Measures The prognostic accuracy of the pathomics signature as well as its association with chemotherapy response were evaluated. Results This study included 785 patients (mean [SD] age, 62.7 [11.1] years; 437 [55.7%] male). A pathomics signature was constructed based on 4 features. Multivariable analysis revealed that the pathomics signature was an independent factor associated with DFS (hazard ratio [HR], 2.46 [95% CI, 2.89-4.13]; P < .001) and OS (HR, 2.78 [95% CI, 2.34-3.31]; P < .001) in the training cohort. Incorporating the pathomics signature into pathomics nomograms resulted in better performance for the estimation of prognosis than the traditional model in a concordance index comparison in the training cohort (DFS: HR, 0.88 [95% CI, 0.86-0.89] vs HR, 0.73 [95% CI, 0.71-0.75]; P < .001; OS: HR, 0.85 [95% CI, 0.84-0.86] vs HR, 0.74 [95% CI, 0.72-0.76]; P < .001) and validation cohort (DFS: HR, 0.83 [95% CI, 0.82-0.85] vs HR, 0.70 [95% CI, 0.67-0.72]; P < .001; OS: HR, 0.80 [95% CI, 0.78-0.82] vs HR, 0.69 [0.67-0.72]; P < .001). Further analysis revealed that patients with a low pathomics signature were more likely to benefit from chemotherapy (eg, combined cohort: DFS: HR, 0.44 [95% CI, 0.28-0.69]; P = .001; OS: HR, 0.43 [95% CI, 0.29-0.64]; P < .001). Conclusions and Relevance These findings suggest that a pathomics signature could help identify patients most likely to benefit from chemotherapy in stage III colon cancer.
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Affiliation(s)
- Wei Jiang
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- School of Science, Jimei University, Xiamen, China
| | - Huaiming Wang
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Department of Colorectal Surgery & Guangdong Institute of Gastroenterology, the Sixth Affiliated Hospital, Supported by National Key Clinical Discipline, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyu Dong
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Xian Yu
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, China
| | - Yandong Zhao
- Department of Pathology, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Dexin Chen
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Botao Yan
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Jiaxin Cheng
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | | | - Hui Wang
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Department of Colorectal Surgery & Guangdong Institute of Gastroenterology, the Sixth Affiliated Hospital, Supported by National Key Clinical Discipline, Sun Yat-sen University, Guangzhou, China
| | - Jun Yan
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Gastrointestinal Surgery, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
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Adili D, Mohetaer A, Zhang W. Diagnostic accuracy of radiomics-based machine learning for neoadjuvant chemotherapy response and survival prediction in gastric cancer patients: A systematic review and meta-analysis. Eur J Radiol 2024; 173:111249. [PMID: 38382422 DOI: 10.1016/j.ejrad.2023.111249] [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: 07/24/2023] [Revised: 11/07/2023] [Accepted: 11/30/2023] [Indexed: 02/23/2024]
Abstract
BACKGROUND In recent years, researchers have explored the use of radiomics to predict neoadjuvant chemotherapy outcomes in gastric cancer (GC). Yet, a lingering debate persists regarding the accuracy of these predictions. Against this backdrop, this study was conducted to examine the accuracy of radiomics in predicting the response to neoadjuvant chemotherapy in GC patients. METHODS An exhaustive search of relevant studies was conducted in PubMed, Cochrane, Embase, and Web of Science databases up to February 21, 2023. The radiomics quality scoring (RQS) tool was employed to assess study quality. Tumor response to neoadjuvant chemotherapy and survival outcomes were examined as outcome measures. RESULTS Fourteen studies involving 3,373 GC patients who had received neoadjuvant chemotherapy were incorporated in our meta-analysis. The mean RQS score across all studies was 36.3%, ranging between 0 and 63.9%. On the validation cohort, when the modeling variables were restricted to radiomic features alone, the predictive performance was characterized by a c-index of 0.750 (95% CI: 0.710-0.790), with a sensitivity of 0.67 (95% CI: 0.58-0.75) and a specificity of 0.77 (95% CI: 0.69-0.84) for the prediction of neoadjuvant chemotherapy response. When clinical data was integrated with radiomic features as modeling variables, the predictive performance improved, yielding a c-index of 0.814 (95% CI: 0.780-0.847), a sensitivity of 0.78 [95% CI: 0.70-0.84], and a specificity of 0.73 [95% CI: 0.67-0.79]. CONCLUSIONS Radiomics holds promise to noninvasively predict neoadjuvant chemotherapy response and survival outcomes among patients with locally advanced GC. Additionally, we underscore the need for future multicenter studies and the development of imaging-sourced tools for risk stratification in this patient population.
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Affiliation(s)
- Diliyaer Adili
- Department of Gastrointestinal (Oncology) Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054 China
| | - Aibibai Mohetaer
- Department of Cardiology, The Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063 China
| | - Wenbin Zhang
- Department of Gastrointestinal (Oncology) Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054 China
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Li J, Chen X, Xu S, Wang Y, Ma F, Wu Y, Qu J. Predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer: The establishment of a spectral CT-based nomogram from prospective datasets. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108020. [PMID: 38367396 DOI: 10.1016/j.ejso.2024.108020] [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: 10/24/2023] [Revised: 02/06/2024] [Accepted: 02/11/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND To establish a spectral CT-based nomogram for predicting early neoadjuvant chemotherapy (NAC) response for locally advanced gastric cancer (LAGC). METHODS This study prospectively recruited 222 cases (177 male and 45 female patients, 9.59 ± 9.54 years) receiving NAC and radical gastrectomy. Triple enhanced spectral CT scans were performed before NAC initiation. According to post-operative tumor regression grade (TRG), patients were classified into responders (TRG = 0 + 1) or non-responders (TRG = 2 + 3), and split into a primary (156) and validation (66) dataset at 7:3 ratio chronologically. We compared clinicopathological data, follow-up information, iodine concentration (IC), normalized ICs (nICs) in arterial/venous/delayed phases (AP/VP/DP) between responders and non-responders. Independent risk factors of response were screened by multivariable logistic regression and adopted for model construction. Model was visualized by nomograms and its capability was determined through receiver operating characteristic (ROC) curves. Log-rank survival analysis was conducted to explore associations between TRG, nomogram and patients' survival. RESULTS This work identified Borrmann classification, ICDP, and nICDP were independent risk factors of response outcomes. A spectral CT-based nomogram was built accordingly and achieved an area under the curve (AUC) of 0.797 (0.692-0.879) and 0.741(0.661-0.811) for the primary and validation dataset, respectively, higher than AUC of individual parameters alone. The nomogram was related to disease-free survival in the validation dataset (Hazard ratio (HR): 5.19 [1.18-12.93], P = 0.02). CONCLUSIONS The spectral CT-based nomogram provides an efficient tool for predicting the pathologic response outcomes of GC after NAC and disease-free survival risk stratification.
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Affiliation(s)
- Jing Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China.
| | - Xuejun Chen
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China.
| | - Shuning Xu
- Department of Gastrointestinal Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China.
| | - Yi Wang
- Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China.
| | - Fei Ma
- Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China.
| | - Yue Wu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China.
| | - Jinrong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China.
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Bao Z, Du J, Zheng Y, Guo Q, Ji R. Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review. Front Oncol 2024; 14:1363812. [PMID: 38601765 PMCID: PMC11004479 DOI: 10.3389/fonc.2024.1363812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 03/18/2024] [Indexed: 04/12/2024] Open
Abstract
Background Artificial intelligence (AI) models, clinical models (CM), and the integrated model (IM) are utilized to evaluate the response to neoadjuvant chemotherapy (NACT) in patients diagnosed with gastric cancer. Objective The objective is to identify the diagnostic test of the AI model and to compare the accuracy of AI, CM, and IM through a comprehensive summary of head-to-head comparative studies. Methods PubMed, Web of Science, Cochrane Library, and Embase were systematically searched until September 5, 2023, to compile English language studies without regional restrictions. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) criteria. Forest plots were utilized to illustrate the findings of diagnostic accuracy, while Hierarchical Summary Receiver Operating Characteristic curves were generated to estimate sensitivity (SEN) and specificity (SPE). Meta-regression was applied to analyze heterogeneity across the studies. To assess the presence of publication bias, Deeks' funnel plot and an asymmetry test were employed. Results A total of 9 studies, comprising 3313 patients, were included for the AI model, with 7 head-to-head comparative studies involving 2699 patients. Across the 9 studies, the pooled SEN for the AI model was 0.75 (95% confidence interval (CI): 0.66, 0.82), and SPE was 0.77 (95% CI: 0.69, 0.84). Meta-regression was conducted, revealing that the cut-off value, approach to predicting response, and gold standard might be sources of heterogeneity. In the head-to-head comparative studies, the pooled SEN for AI was 0.77 (95% CI: 0.69, 0.84) with SPE at 0.79 (95% CI: 0.70, 0.85). For CM, the pooled SEN was 0.67 (95% CI: 0.57, 0.77) with SPE at 0.59 (95% CI: 0.54, 0.64), while for IM, the pooled SEN was 0.83 (95% CI: 0.79, 0.86) with SPE at 0.69 (95% CI: 0.56, 0.79). Notably, there was no statistical difference, except that IM exhibited higher SEN than AI, while maintaining a similar level of SPE in pairwise comparisons. In the Receiver Operating Characteristic analysis subgroup, the CT-based Deep Learning (DL) subgroup, and the National Comprehensive Cancer Network (NCCN) guideline subgroup, the AI model exhibited higher SEN but lower SPE compared to the IM. Conversely, in the training cohort subgroup and the internal validation cohort subgroup, the AI model demonstrated lower SEN but higher SPE than the IM. The subgroup analysis underscored that factors such as the number of cohorts, cohort type, cut-off value, approach to predicting response, and choice of gold standard could impact the reliability and robustness of the results. Conclusion AI has demonstrated its viability as a tool for predicting the response of GC patients to NACT Furthermore, CT-based DL model in AI was sensitive to extract tumor features and predict the response. The results of subgroup analysis also supported the above conclusions. Large-scale rigorously designed diagnostic accuracy studies and head-to-head comparative studies are anticipated. Systematic review registration PROSPERO, CRD42022377030.
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Affiliation(s)
- Zhixian Bao
- Department of Gastroenterology, Xi’an NO.1 Hospital, Xi’an, Shaanxi, China
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Jie Du
- Department of Social Medicine and Health Management, School of Public Health, Lanzhou University, Lanzhou, China
| | - Ya Zheng
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China
| | - Qinghong Guo
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China
| | - Rui Ji
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China
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Baldini G, Hosch R, Schmidt CS, Borys K, Kroll L, Koitka S, Haubold P, Pelka O, Nensa F, Haubold J. Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging. Invest Radiol 2024:00004424-990000000-00203. [PMID: 38436405 DOI: 10.1097/rli.0000000000001071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
OBJECTIVES Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT). MATERIALS AND METHODS This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs). RESULTS For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively. CONCLUSIONS The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.
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Affiliation(s)
- Giulia Baldini
- From the Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (G.B., R.H., K.B., L.K., S.K., F.N., J.H.); Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (G.B., R.H., C.S.S., K.B., L.K., S.K., O.P., F.N., J.H.); Institute for Transfusion Medicine, University Hospital Essen, Essen, Germany (C.S.S.); Department of Diagnostic and Interventional Radiology, Kliniken Essen-Mitte, Essen, Germany (P.H.); and Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany (O.P., F.N.)
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Shao H, Li N, Ling Y, Wang J, Fang Y, Jing M, Zhou Z, Zhang Y. Nomogram for predicting pathological response to neoadjuvant treatment in patients with locally advanced gastric cancer: Data from a phase III clinical trial. Cancer Med 2024; 13:e7122. [PMID: 38523553 PMCID: PMC10961599 DOI: 10.1002/cam4.7122] [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: 09/04/2023] [Accepted: 02/07/2024] [Indexed: 03/26/2024] Open
Abstract
PURPOSE This study aimed to establish a nomogram using routinely available clinicopathological parameters to predict the pathological response in patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant treatment. MATERIALS AND METHODS We conducted this study based on the ongoing Neo-CRAG trial, a prospective study focused on preoperative treatment in patients with LAGC. A total of 221 patients who underwent surgery following neoadjuvant chemotherapy (nCT) or neoadjuvant chemoradiotherapy (nCRT) at Sun Yat-sen University Cancer Center between June 2013 and July 2022 were included in the analysis. We defined complete or near-complete pathological regression and ypN0 as good response (GR), and determined the prognostic value of GR by Kaplan-Meier survival analysis. Eventually, a nomogram for predicting GR was developed based on statistically identified predictors through multivariate logistic regression analysis and internally validated by the bootstrap method. RESULTS GR was confirmed in 54 patients (54/221, 24.4%). Patients who achieved GR had a longer progression-free survival and overall survival. Then, five independent factors, including pretreatment tumor differentiation, clinical T stage, monocyte count, CA724 level, and the use of nCRT, were identified. Based on these predictors, the nomogram was established with an area under the curve (AUC) of 0.777 (95% CI, 0.705-0.850) and a bias-corrected AUC of 0.752. CONCLUSION A good pathological response after neoadjuvant treatment was associated with an improved prognosis in LAGC patients. The nomogram we established exhibits a high predictive capability for GR, offering potential value in devising personalized and precise treatment strategies for LAGC patients.
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Affiliation(s)
- Han Shao
- State Key Laboratory of Oncology in South ChinaSun Yat‐sen University Cancer CenterGuangzhouPeople's Republic of China
- Department of Radiation OncologySun Yat‐sen University Cancer CenterGuangzhouGuangdongPeople's Republic of China
| | - Nai Li
- State Key Laboratory of Oncology in South ChinaSun Yat‐sen University Cancer CenterGuangzhouPeople's Republic of China
- Department of Radiation OncologySun Yat‐sen University Cancer CenterGuangzhouGuangdongPeople's Republic of China
| | - Yi‐hong Ling
- State Key Laboratory of Oncology in South ChinaSun Yat‐sen University Cancer CenterGuangzhouPeople's Republic of China
- Department of PathologySun Yat‐sen University Cancer CenterGuangzhouGuangdongPeople's Republic of China
| | - Ji‐jin Wang
- State Key Laboratory of Oncology in South ChinaSun Yat‐sen University Cancer CenterGuangzhouPeople's Republic of China
- Department of Radiation OncologySun Yat‐sen University Cancer CenterGuangzhouGuangdongPeople's Republic of China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical UniversityShandong Academy of Medical ScienceJinanPeople's Republic of China
| | - Yi Fang
- State Key Laboratory of Oncology in South ChinaSun Yat‐sen University Cancer CenterGuangzhouPeople's Republic of China
- Department of Radiation OncologySun Yat‐sen University Cancer CenterGuangzhouGuangdongPeople's Republic of China
| | - Ming Jing
- State Key Laboratory of Oncology in South ChinaSun Yat‐sen University Cancer CenterGuangzhouPeople's Republic of China
- Department of Radiation OncologySun Yat‐sen University Cancer CenterGuangzhouGuangdongPeople's Republic of China
| | - Zhi‐wei Zhou
- State Key Laboratory of Oncology in South ChinaSun Yat‐sen University Cancer CenterGuangzhouPeople's Republic of China
- Department of Gastric SurgerySun Yat‐sen University Cancer CenterGuangzhouGuangdongPeople's Republic of China
| | - Yu‐jing Zhang
- State Key Laboratory of Oncology in South ChinaSun Yat‐sen University Cancer CenterGuangzhouPeople's Republic of China
- Department of Radiation OncologySun Yat‐sen University Cancer CenterGuangzhouGuangdongPeople's Republic of China
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Mao J, Ye W, Ma W, Liu J, Zhong W, Yuan H, Li T, Guan L, Wu D. Prediction by a multiparametric magnetic resonance imaging-based radiomics signature model of disease-free survival in patients with rectal cancer treated by surgery. Front Oncol 2024; 14:1255438. [PMID: 38454930 PMCID: PMC10917947 DOI: 10.3389/fonc.2024.1255438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 01/31/2024] [Indexed: 03/09/2024] Open
Abstract
Objective The aim of this study was to assess the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics signature model to predict disease-free survival (DFS) in patients with rectal cancer treated by surgery. Materials and methods We evaluated data of 194 patients with rectal cancer who had undergone radical surgery between April 2016 and September 2021. The mean age of all patients was 62.6 ± 9.7 years (range: 37-86 years). The study endpoint was DFS and 1132 radiomic features were extracted from preoperative MRIs, including contrast-enhanced T1- and T2-weighted imaging and apparent diffusion coefficient values. The study patients were randomly allocated to training (n=97) and validation cohorts (n=97) in a ratio of 5:5. A multivariable Cox regression model was used to generate a radiomics signature (rad score). The associations of rad score with DFS were evaluated using Kaplan-Meier analysis. Three models, namely a radiomics nomogram, radiomics signature, and clinical model, were compared using the Akaike information criterion. Result The rad score, which was composed of four MRI features, stratified rectal cancer patients into low- and high-risk groups and was associated with DFS in both the training (p = 0.0026) and validation sets (p = 0.036). Moreover, a radiomics nomogram model that combined rad score and independent clinical risk factors performed better (Harrell concordance index [C-index] =0.77) than a purely radiomics signature (C-index=0.73) or clinical model (C-index=0.70). Conclusion An MRI radiomics model that incorporates a radiomics signature and clinicopathological factors more accurately predicts DFS than does a clinical model in patients with rectal cancer.
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Affiliation(s)
- Jiwei Mao
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Wanli Ye
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Weili Ma
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Jianjiang Liu
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Wangyan Zhong
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Hang Yuan
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Ting Li
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Le Guan
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Dongping Wu
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
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Huang Y, Liao C, Shen Z, Zou Y, Xie W, Gan Q, Yao Y, Zheng J, Kong J. A bibliometric insight into neoadjuvant chemotherapy in bladder cancer: trends, collaborations, and future avenues. Front Immunol 2024; 15:1297542. [PMID: 38444854 PMCID: PMC10912866 DOI: 10.3389/fimmu.2024.1297542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/31/2024] [Indexed: 03/07/2024] Open
Abstract
Background Neoadjuvant chemotherapy (NAC) followed by radical cystectomy (RC) remains the cornerstone of treatment for muscle-invasive bladder cancer (MIBC). While platinum-based regimens have demonstrated benefits in tumor downstaging and improved long-term survival for selected patients, they may pose risks for those who are ineligible or unresponsive to chemotherapy. Objective We undertook a bibliometric analysis to elucidate the breadth of literature on NAC in bladder cancer, discern research trajectories, and underscore emerging avenues of investigation. Methods A systematic search of the Web of Science Core Collection (WoSCC) was conducted to identify articles pertaining to NAC in bladder cancer from 1999 to 2022. Advanced bibliometric tools, such as VOSviewer, CiteSpace, and SCImago Graphica, facilitated the examination and depicted the publication trends, geographic contributions, institutional affiliations, journal prominence, author collaborations, and salient keywords, emphasizing the top 25 citation bursts. Results Our analysis included 1836 publications spanning 1999 to 2022, indicating a growing trend in both annual publications and citations related to NAC in bladder cancer. The United States emerged as the predominant contributor in terms of publications, citations, and international collaborations. The University of Texas was the leading institution in publication output. "Urologic Oncology Seminars and Original Investigations" was the primary publishing journal, while "European Urology" boasted the highest impact factor. Shariat, Shahrokh F., and Grossman, H.B., were identified as the most prolific and co-cited authors, respectively. Keyword analysis revealed both frequency of occurrence and citation bursts, highlighting areas of concentrated study. Notably, the integration of immunochemotherapy is projected to experience substantial growth in forthcoming research. Conclusions Our bibliometric assessment provides a panoramic view of the research milieu surrounding neoadjuvant chemotherapy for bladder cancer, encapsulating the present state, evolving trends, and potential future directions, with a particular emphasis on the promise of immunochemotherapy.
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Affiliation(s)
- Yi Huang
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Chengxiao Liao
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Zefeng Shen
- Department of Urology, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yitong Zou
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Weibin Xie
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Qinghua Gan
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yuhui Yao
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - JunJiong Zheng
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jianqiu Kong
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
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Li X, Fan Y, Tong J, Lou M. Risk factors, prognostic factors, and nomograms for distant metastases in patients with gastroenteropancreatic neuroendocrine tumors: a population-based study. Front Endocrinol (Lausanne) 2024; 15:1264952. [PMID: 38449852 PMCID: PMC10916283 DOI: 10.3389/fendo.2024.1264952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Background Patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) have a poor prognosis for distant metastasis. Currently, there are no studies on predictive models for the risk of distant metastasis in GEP-NETs. Methods In this study, risk factors associated with metastasis in patients with GEP-NETs in the Surveillance, Epidemiology, and End Results (SEER) database were analyzed by univariate and multivariate logistic regression, and a nomogram model for metastasis risk prediction was constructed. Prognostic factors associated with distant metastasis in patients with GEP-NETs were analyzed by univariate and multivariate Cox, and a nomogram model for prognostic prediction was constructed. Finally, the performance of the nomogram model predictions is validated by internal validation set and external validation set. Results A total of 9145 patients with GEP-NETs were enrolled in this study. Univariate and multivariate logistic analysis demonstrated that T stage, N stage, tumor size, primary site, and histologic types independent risk factors associated with distant metastasis in GEP-NETs patients (p value < 0.05). Univariate and multivariate Cox analyses demonstrated that age, histologic type, tumor size, N stage, and primary site surgery were independent factors associated with the prognosis of patients with GEP-NETs (p value < 0.05). The nomogram model constructed based on metastasis risk factors and prognostic factors can predict the occurrence of metastasis and patient prognosis of GEP-NETs very effectively in the internal training and validation sets as well as in the external validation set. Conclusion In conclusion, we constructed a new distant metastasis risk nomogram model and a new prognostic nomogram model for GEP-NETs patients, which provides a decision-making reference for individualized treatment of clinical patients.
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Affiliation(s)
- Xinwei Li
- Department of Gastroenterology, Affiliated Cancer Hospital of Bengbu Medical College, Bengbu, China
| | - Yongfei Fan
- Department of Thoracic Surgery, The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, China
| | - Jichun Tong
- Department of Thoracic Surgery, The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, China
| | - Ming Lou
- Department of Thoracic Surgery, The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, China
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Yuan Z, Cui H, Xu Q, Gao J, Liang W, Cao B, Lin X, Song L, Huang J, Zhao R, Li H, Yu Z, Du J, Wang S, Chen L, Cui J, Zhao Y, Wei B. Total versus proximal gastrectomy for proximal gastric cancer after neoadjuvant chemotherapy: a multicenter retrospective propensity score-matched cohort study. Int J Surg 2024; 110:1000-1007. [PMID: 38085808 PMCID: PMC10871602 DOI: 10.1097/js9.0000000000000927] [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: 10/10/2023] [Accepted: 11/09/2023] [Indexed: 02/17/2024]
Abstract
BACKGROUND This study aimed to analyze and compare the short-term and long-term outcomes of proximal gastrectomy (PG) and total gastrectomy (TG) in patients with locally advanced proximal gastric cancer (GC) following neoadjuvant chemotherapy (NACT). METHOD A multicenter retrospective cohort study and propensity score matching (PSM) were employed. The authors examined 367 patients with proximal GC who received NACT followed by PG ( n =164) or TG ( n =203) at two Chinese medical institutions between December 2009 and December 2022. Clinical and pathological parameters, postoperative complications, and 5-year overall survival (OS) and recurrence-free survival (RFS) were compared between the two groups. The dissection status and metastasis rate of each lymph node station were assessed. RESULTS After PSM, 80 patients were enrolled in both TG and PG group, and baseline characteristics were comparable between the groups (all P >0.05). The TG group had a higher total number of lymph nodes retrieved ( P <0.001) and longer operative time ( P =0.007) compared to the PG group. The incidence of Clavien-Dindo grade II or higher postoperative complications was similar between the TG group (21.3%, 17/80) and the PG group (17.5%, 14/80) ( P =0.689). The 5-year OS rates were 68.4 for the PG group and 66.0% for the TG group ( P =0.881), while the 5-year RFS rates were 64.8 and 61.9%, respectively ( P =0.571), with no statistically significant differences. Metastasis rates at lymph node stations #4d, #5, #6, and #12a were notably low in the TG group, with values of 2.74, 0.67, 1.33, and 1.74%, respectively. CONCLUSION For proximal GC patients following NACT, PG maintains comparable curative potential and oncological efficacy to TG, making it a safe option.
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Affiliation(s)
- Zhen Yuan
- School of Medicine, Nankai University, Tianjin
- Department of General Surgery, The First Medical Center
| | - Hao Cui
- School of Medicine, Nankai University, Tianjin
- Department of General Surgery, The First Medical Center
| | - Qixuan Xu
- Department of General Surgery, The First Medical Center
| | - Jingwang Gao
- Department of General Surgery, The First Medical Center
| | - Wenquan Liang
- Department of General Surgery, The First Medical Center
| | - Bo Cao
- Department of General Surgery, The First Medical Center
| | - Xia Lin
- Department of Gastrointestinal Surgery, Three Gorges Hospital, Chongqing University
- Department of General Surgery, The First Affiliated Hospital of Army Medical University, Chongqing, People’s Republic of China
| | - Liqiang Song
- School of Medicine, Nankai University, Tianjin
- Department of General Surgery, The First Medical Center
| | - Jun Huang
- School of Medicine, Nankai University, Tianjin
- Department of General Surgery, The First Medical Center
| | - Ruiyang Zhao
- Department of General Surgery, The First Medical Center
| | - Hanghang Li
- Department of General Surgery, The First Medical Center
| | - Zhiyuan Yu
- School of Medicine, Nankai University, Tianjin
- Department of General Surgery, The First Medical Center
| | - Jiajun Du
- Department of General Surgery, The First Medical Center
| | - Shuyuan Wang
- School of Medicine, Nankai University, Tianjin
- Department of Radiotherapy, The Fifth Medical Center, Chinese PLA General Hospital, Beijing
| | - Lin Chen
- Department of General Surgery, The First Medical Center
| | - Jianxin Cui
- Department of General Surgery, The First Medical Center
| | - Yongliang Zhao
- Department of General Surgery, The First Affiliated Hospital of Army Medical University, Chongqing, People’s Republic of China
| | - Bo Wei
- Department of General Surgery, The First Medical Center
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Zhang H, Zhou B, Zhang H, Zhang Y, Lei Y, Huang B. Peritumoral Radiomics for Identification of Telomerase Reverse Transcriptase Promoter Mutation in Patients With Glioblastoma Based on Preoperative MRI. Can Assoc Radiol J 2024; 75:143-152. [PMID: 37552107 DOI: 10.1177/08465371231183309] [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] [Indexed: 08/09/2023] Open
Abstract
Purpose: To evaluate the value of intra- and peritumoral deep learning (DL) features based on multi-parametric magnetic resonance imaging (MRI) for identifying telomerase reverse transcriptase (TERT) promoter mutation in glioblastoma (GBM). Methods: In this study, we included 229 patients with GBM who underwent preoperative MRI in two hospitals between November 2016 and September 2022. We used four 2D Convolutional Neural Networks (GoogLeNet, DenseNet121, VGG16, and MobileNetV3-Large) to extract intra- and peritumoral DL features. The Mann-Whitney U test, Pearson correlation analysis, least absolute shrinkage and selection operator, and logistic regression analysis were used for feature selection and construction of DL radiomics (DLR) signatures in different regions. These multi-parametric and multi-region signatures were combined to identify TERT promoter mutation. The area under the receiver operating characteristic curve (AUC) was used to evaluate the effects of the signatures. Results: The signatures based on the DL features from the peritumoral regions with expansion distances of 2 mm, 8 mm, and 10 mm using the GoogLeNet architecture correlated with the optimal AUC values (test set: .823, .753, and .768) in the T2-weighted, T1-weighted contrast-enhanced, and T1-weighted images. Using the stacking fusion method, DLR with multi-parameter and multi-region fusion achieved the best discrimination with AUC values of .948 and .902 in the training and test sets, respectively. Conclusions: The radiomics model based on the fusion of multi-parameter MRI intra- and peritumoral DLR signatures may help to identify TERT promoter mutation in patients with GBM.
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Affiliation(s)
- Hongbo Zhang
- 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
| | - Beibei Zhou
- Department of Radiology, The Seventh Affiliated Hospital Sun Yat-sen University, Shenzhen, China
| | - Hanwen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yuze Zhang
- 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
| | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Biao Huang
- 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
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Wei Z, Liu H, Xv Y, Liao F, He Q, Xie Y, Lv F, Jiang Q, Xiao M. Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer. Heliyon 2024; 10:e24878. [PMID: 38304824 PMCID: PMC10831750 DOI: 10.1016/j.heliyon.2024.e24878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
Objective This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813-0.953) in internal validation cohort and 0.862 (95 % CI: 0.756-0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process.
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Affiliation(s)
- Zongjie Wei
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huayun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yingjie Xv
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fangtong Liao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Quanhao He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yongpeng Xie
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Liu C, Li L, Chen X, Huang C, Wang R, Liu Y, Gao J. Intratumoral and peritumoral radiomics predict pathological response after neoadjuvant chemotherapy against advanced gastric cancer. Insights Imaging 2024; 15:23. [PMID: 38270724 PMCID: PMC10811314 DOI: 10.1186/s13244-023-01584-6] [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: 11/14/2022] [Accepted: 11/25/2023] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND To investigate whether intratumoral and peritumoral radiomics may predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer. METHODS Clinical, pathological, and CT data from 231 patients with advanced gastric cancer who underwent neoadjuvant chemotherapy at our hospital between July 2014 and February 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 161) and a validation group (n = 70). The support vector machine classifier was used to establish radiomics models. A clinical model was established based on the selected clinical indicators. Finally, the radiomics and clinical models were combined to generate a radiomics-clinical model. ROC analyses were used to evaluate the prediction efficiency for each model. Calibration curves and decision curves were used to evaluate the optimal model. RESULTS A total of 91 cases were recorded with good response and 140 with poor response. The radiomics model demonstrated that the AUC was higher in the combined model than in the intratumoral and peritumoral models (training group: 0.949, 0.943, and 0.846, respectively; validation group: 0.815, 0.778, and 0.701, respectively). Age, Borrmann classification, and Lauren classification were used to construct the clinical model. Among the radiomics-clinical models, the combined-clinical model showed the highest AUC (training group: 0.960; validation group: 0.843), which significantly improved prediction efficiency. CONCLUSION The peritumoral model provided additional value in the evaluation of pathological response after neoadjuvant chemotherapy against advanced gastric cancer, and the combined-clinical model showed the highest predictive efficiency. CRITICAL RELEVANCE STATEMENT Intratumoral and peritumoral radiomics can noninvasively predict the pathological response against advanced gastric cancer after neoadjuvant chemotherapy to guide early treatment decision and provide individual treatment for patients. KEY POINTS 1. Radiomics can predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer. 2. Peritumoral radiomics has additional predictive value. 3. Radiomics-clinical models can guide early treatment decisions and improve patient prognosis.
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Affiliation(s)
- Chenchen Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Xingzhi Chen
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Yiyang Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China.
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Wang H, Zhang Y. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res 2024; 12:12. [PMID: 38273398 PMCID: PMC10809593 DOI: 10.1186/s40364-024-00561-5] [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: 10/20/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.
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Affiliation(s)
- Xingping Zhang
- School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China.
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia.
- Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China.
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Yan X, Fu X, Gui Y, Chen X, Cheng Y, Dai M, Wang W, Xiao M, Tan L, Zhang J, Shao Y, Wang H, Chang X, Lv K. Development and validation of a nomogram model based on pretreatment ultrasound and contrast-enhanced ultrasound to predict the efficacy of neoadjuvant chemotherapy in patients with borderline resectable or locally advanced pancreatic cancer. Cancer Imaging 2024; 24:13. [PMID: 38245789 PMCID: PMC10800053 DOI: 10.1186/s40644-024-00662-2] [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: 05/29/2023] [Accepted: 01/11/2024] [Indexed: 01/22/2024] Open
Abstract
OBJECTIVES To develop a nomogram using pretreatment ultrasound (US) and contrast-enhanced ultrasound (CEUS) to predict the clinical response of neoadjuvant chemotherapy (NAC) in patients with borderline resectable pancreatic cancer (BRPC) or locally advanced pancreatic cancer (LAPC). METHODS A total of 111 patients with pancreatic ductal adenocarcinoma (PDAC) treated with NAC between October 2017 and February 2022 were retrospectively enrolled. The patients were randomly divided (7:3) into training and validation cohorts. The pretreatment US and CEUS features were reviewed. Univariate and multivariate logistic regression analyses were used to determine the independent predictors of clinical response in the training cohort. Then a prediction nomogram model based on the independent predictors was constructed. The area under the curve (AUC), calibration plot, C-index and decision curve analysis (DCA) were used to assess the nomogram's performance, calibration, discrimination and clinical benefit. RESULTS The multivariate logistic regression analysis showed that the taller-than-wide shape in the longitudinal plane (odds ratio [OR]:0.20, p = 0.01), time from injection of contrast agent to peak enhancement (OR:3.64; p = 0.05) and Peaktumor/ Peaknormal (OR:1.51; p = 0.03) were independent predictors of clinical response to NAC. The predictive nomogram developed based on the above imaging features showed AUCs were 0.852 and 0.854 in the primary and validation cohorts, respectively. Good calibration was achieved in the training datasets, with C-index of 0.852. DCA verified the clinical usefulness of the nomogram. CONCLUSIONS The nomogram based on pretreatment US and CEUS can effectively predict the clinical response of NAC in patients with BRPC and LAPC; it may help guide personalized treatment.
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Affiliation(s)
- Xiaoyi Yan
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Xianshui Fu
- Department of Ultrasound, No.304 Hospital of Chinese PLA, Beijing, 100037, China
| | - Yang Gui
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Xueqi Chen
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yuejuan Cheng
- Department of Medical Oncology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Menghua Dai
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Weibin Wang
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Mengsu Xiao
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Li Tan
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jing Zhang
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yuming Shao
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Huanyu Wang
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Xiaoyan Chang
- Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Ke Lv
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
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Zhang M, Lu Y, Sun H, Hou C, Zhou Z, Liu X, Zhou Q, Li Z, Yin Y. CT-based deep learning radiomics and hematological biomarkers in the assessment of pathological complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma: A two-center study. Transl Oncol 2024; 39:101804. [PMID: 37839176 PMCID: PMC10587766 DOI: 10.1016/j.tranon.2023.101804] [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: 07/12/2023] [Revised: 09/11/2023] [Accepted: 10/09/2023] [Indexed: 10/17/2023] Open
Abstract
PURPOSE To evaluate and validate CT-based models using pre- and posttreatment deep learning radiomics features and hematological biomarkers for assessing esophageal squamous cell carcinoma (ESCC) pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT). MATERIAL AND METHODS This retrospective study recruited patients with biopsy-proven ESCC who underwent nCRT from two Chinese hospitals between May 2017 and May 2022, divided into a training set (hospital I, 111 cases), an internal validation set (hospital I, 47 cases), and an external validation set (hospital II, 33 cases). We used minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) as feature selection methods and three classifiers as model construction methods. The assessment of models was performed using area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA). RESULTS A total 190 patients were included in our study (60.8 ± 7.08 years, 133 men), and seventy-seven of them (40.5 %) achieved pCR. The logistic regression (LR)-based combined model incorporating neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio, albumin, and radscores performed well both in the internal and external validation sets with AUCs of 0.875 and 0.857 (95 % CI, 0.776-0.964; 0.731-0.984, P <0.05), respectively. DCA demonstrated that nomogram was useful for pCR prediction and produced clinical net benefits. CONCLUSION The incorporation of radscores and hematological biomarkers into LR-based model improved pCR prediction after nCRT in ESCC. Enhanced pCR predictability may improve patients selection before surgery, providing clinical application value for the use of active surveillance.
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Affiliation(s)
- Meng Zhang
- Shandong University Cancer Center, Shandong University, Jinan, Shandong, China; Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Yukun Lu
- Department of Radiation Oncology, Anyang Tumor Hospital, Anyang, Henan, China
| | - Hongfu Sun
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Chuanke Hou
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, Beijing, China
| | - Zichun Zhou
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Shandong, China
| | - Xiao Liu
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - Qichao Zhou
- Manteia Technologies Co., Ltd, Xiamen, Fujian, China
| | - Zhenjiang Li
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
| | - Yong Yin
- Shandong University Cancer Center, Shandong University, Jinan, Shandong, China; Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
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Zhang Y, Wu C, Du J, Xiao Z, Lv F, Liu Y. Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study. Abdom Radiol (NY) 2024; 49:258-270. [PMID: 37987856 DOI: 10.1007/s00261-023-04125-3] [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: 01/02/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 11/22/2023]
Abstract
PURPOSE To establish and validate a deep learning radiomics nomogram (DLRN) based on intratumoral and peritumoral regions of MR images and clinical characteristics to predict recurrence risk factors in early-stage cervical cancer and to clarify whether DLRN could be applied for risk stratification. METHODS Two hundred and twenty five pathologically confirmed early-stage cervical cancers were enrolled and made up the training cohort and internal validation cohort, and 40 patients from another center were enrolled into the external validation cohort. On the basis of region of interest (ROI) of intratumoral and different peritumoral regions, two sets of features representing deep learning and handcrafted radiomics features were created using combined images of T2-weighted MRI (T2WI) and diffusion-weighted imaging (DWI). The signature subset with the best discriminant features was chosen, and deep learning and handcrafted signatures were created using logistic regression. Integrated with independent clinical factors, a DLRN was built. The discrimination and calibration of DLNR were applied to assess its therapeutic utility. RESULTS The DLRN demonstrated satisfactory performance for predicting recurrence risk factors, with AUCs of 0.944 (95% confidence interval 0.896-0.992) and 0.885 (95% confidence interval 0.834-0.937) in the internal and external validation cohorts. Furthermore, decision curve analysis revealed that the DLRN outperformed the clinical model, deep learning signature, and radiomics signature in terms of net benefit. CONCLUSION A DLRN based on intratumoral and peritumoral regions had the potential to predict and stratify recurrence risk factors for early-stage cervical cancers and enhance the value of individualized precision treatment.
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Affiliation(s)
- Yajiao Zhang
- College of Medical Informatics, Chongqing Medical University, No.1 Medical College Road, Chongqing, China
| | - Chao Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jinglong Du
- College of Medical Informatics, Chongqing Medical University, No.1 Medical College Road, Chongqing, China
| | - Zhibo Xiao
- College of Medical Informatics, Chongqing Medical University, No.1 Medical College Road, Chongqing, China
| | - Furong Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yanbing Liu
- College of Medical Informatics, Chongqing Medical University, No.1 Medical College Road, Chongqing, China.
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Zhong H, Wang T, Liu X, Tian Y, Zhou Y. ASO Author Reflections: Deep-Learning Radiomics Nomogram Based on Enhanced CT to Predict the Effect of Neoadjuvant Chemotherapy on Metastatic Lymph Nodes in Locally Advanced Gastric Cancer. Ann Surg Oncol 2024; 31:454-455. [PMID: 37930497 DOI: 10.1245/s10434-023-14508-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Affiliation(s)
- Hao Zhong
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Tongyu Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Xiaodong Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yulong Tian
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yanbing Zhou
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
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Zheng F, Yin P, Liang K, Wang Y, Hao W, Hao Q, Hong N. Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma. Acad Radiol 2023:S1076-6332(23)00690-6. [PMID: 38151381 DOI: 10.1016/j.acra.2023.12.015] [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: 10/25/2023] [Revised: 12/09/2023] [Accepted: 12/09/2023] [Indexed: 12/29/2023]
Abstract
RATIONALE AND OBJECTIVES Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning radiomics (DLR) model to accurately predict the response to NAC in patients diagnosed with OS using preoperative MR images. METHODS We reviewed axial T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted (T1CE) of 106 patients pathologically confirmed as OS. First, the Auto3DSeg framework was utilized for automated OS segmentation. Second, using three feature extraction methods, nine risk classification models were constructed based on three classifiers. The area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, negative predictive value and positive predictive value were calculated for performance evaluation. Additionally, we developed a deep learning radiomics nomogram with clinical indicators. RESULTS The model for OS automatic segmentation achieved a Dice coefficient of 0.868 across datasets. To predict the response to NAC, the DLR model achieved the highest prediction performance with an accuracy of 93.8% and an AUC of 0.961 in the test sets. We used calibration curves to assess the predictive ability of the models and performed decision curve analysis to evaluate the clinical net benefit of the DLR model. CONCLUSION The DLR model can serve as a pragmatic prediction tool, capable of identifying patients with poor response to NAC, providing information for risk counseling, and assisting in making clinical treatment decisions. Poor responders are better advised to undergo immunotherapy and receive the best supportive care.
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Affiliation(s)
- Fei Zheng
- Department of Radiology, Peking University people' hospital, No.11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China (F.Z., P.Y., Y.W., W.H., Q.H., N.H.)
| | - Ping Yin
- Department of Radiology, Peking University people' hospital, No.11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China (F.Z., P.Y., Y.W., W.H., Q.H., N.H.)
| | - Kewei Liang
- Intelligent Manufacturing Research Institute, Visual 3D Medical Science and Technology Development, No.186 South Fourth Ring Road West, Fengtai District, Beijing, 100071, P. R. China (K.L.)
| | - Yujian Wang
- Department of Radiology, Peking University people' hospital, No.11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China (F.Z., P.Y., Y.W., W.H., Q.H., N.H.)
| | - Wenhan Hao
- Department of Radiology, Peking University people' hospital, No.11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China (F.Z., P.Y., Y.W., W.H., Q.H., N.H.)
| | - Qi Hao
- Department of Radiology, Peking University people' hospital, No.11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China (F.Z., P.Y., Y.W., W.H., Q.H., N.H.)
| | - Nan Hong
- Department of Radiology, Peking University people' hospital, No.11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China (F.Z., P.Y., Y.W., W.H., Q.H., N.H.).
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Deng J, Zhang W, Xu M, Zhou J. Imaging advances in efficacy assessment of gastric cancer neoadjuvant chemotherapy. Abdom Radiol (NY) 2023; 48:3661-3676. [PMID: 37787962 DOI: 10.1007/s00261-023-04046-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/31/2023] [Accepted: 09/03/2023] [Indexed: 10/04/2023]
Abstract
Effective neoadjuvant chemotherapy (NAC) can improve the survival of patients with locally progressive gastric cancer, but chemotherapeutics do not always exhibit good efficacy in all patients. Therefore, accurate preoperative evaluation of the effect of neoadjuvant therapy and the appropriate selection of surgery time to minimize toxicity and complications while prolonging patient survival are key issues that need to be addressed. This paper reviews the role of three imaging methods, morphological, functional, radiomics, and artificial intelligence (AI)-based imaging, in evaluating NAC pathological reactions for gastric cancer. In addition, the advantages and disadvantages of each method and the future application prospects are discussed.
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Affiliation(s)
- Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China
| | - Min Xu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China.
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China.
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China.
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Yao H, Tian L, Liu X, Li S, Chen Y, Cao J, Zhang Z, Chen Z, Feng Z, Xu Q, Zhu J, Wang Y, Guo Y, Chen W, Li C, Li P, Wang H, Luo J. Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study. J Cancer Res Clin Oncol 2023; 149:15827-15838. [PMID: 37672075 PMCID: PMC10620299 DOI: 10.1007/s00432-023-05339-0] [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: 07/24/2023] [Accepted: 08/24/2023] [Indexed: 09/07/2023]
Abstract
PURPOSE There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagnosed as renal cell carcinoma. We aimed to develop and evaluate a multichannel deep learning model for differentiating fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC). METHODS This two-center retrospective study included 320 patients from the First Affiliated Hospital of Sun Yat-Sen University (FAHSYSU) and 132 patients from the Sun Yat-Sen University Cancer Center (SYSUCC). Data from patients at FAHSYSU were divided into a development dataset (n = 267) and a hold-out dataset (n = 53). The development dataset was used to obtain the optimal combination of CT modality and input channel. The hold-out dataset and SYSUCC dataset were used for independent internal and external validation, respectively. RESULTS In the development phase, models trained on unenhanced CT images performed significantly better than those trained on enhanced CT images based on the fivefold cross-validation. The best patient-level performance, with an average area under the receiver operating characteristic curve (AUC) of 0.951 ± 0.026 (mean ± SD), was achieved using the "unenhanced CT and 7-channel" model, which was finally selected as the optimal model. In the independent internal and external validation, AUCs of 0.966 (95% CI 0.919-1.000) and 0.898 (95% CI 0.824-0.972), respectively, were obtained using the optimal model. In addition, the performance of this model was better on large tumors (≥ 40 mm) in both internal and external validation. CONCLUSION The promising results suggest that our multichannel deep learning classifier based on unenhanced whole-tumor CT images is a highly useful tool for differentiating fp-AML from RCC.
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Affiliation(s)
- Haohua Yao
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Department of Urology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Li Tian
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xi Liu
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Shurong Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuhang Chen
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jiazheng Cao
- Department of Urology, Jiangmen Central Hospital, Jiangmen, China
| | - Zhiling Zhang
- Department of Urology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Zhenhua Chen
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zihao Feng
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Quanhui Xu
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jiangquan Zhu
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yinghan Wang
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yan Guo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wei Chen
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Caixia Li
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, China
| | - Peixing Li
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, China
| | - Huanjun Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - Junhang Luo
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
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Gu W, Chen Y, Zhu H, Chen H, Yang Z, Mo S, Zhao H, Chen L, Nakajima T, Yu X, Ji S, Gu Y, Chen J, Tang W. Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study. EClinicalMedicine 2023; 65:102269. [PMID: 38106556 PMCID: PMC10725026 DOI: 10.1016/j.eclinm.2023.102269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 09/26/2023] [Accepted: 09/26/2023] [Indexed: 12/19/2023] Open
Abstract
Background Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs. Methods Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC). Findings The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91). Interpretation Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies. Funding This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).
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Affiliation(s)
- Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Faculty of Medicine, Ibaraki, Tsukuba, Japan
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yingli Chen
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Haibin Zhu
- Key Laboratory of Carcinogenesis and Translational Research, Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Haidi Chen
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Zongcheng Yang
- Department of Stomatology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, PR China
| | - Shaocong Mo
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Hongyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, China
| | - Lei Chen
- Department of Radiology, Minhang Branch, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Takahito Nakajima
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Faculty of Medicine, Ibaraki, Tsukuba, Japan
| | - XianJun Yu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Shunrong Ji
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - YaJia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jie Chen
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Head & Neck Tumors and Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wei Tang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Qin X, Xia L, Ma Q, Cheng D, Zhang C. Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically. Ren Fail 2023; 45:2271104. [PMID: 37860932 PMCID: PMC10591537 DOI: 10.1080/0886022x.2023.2271104] [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: 07/04/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023] Open
Abstract
This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropathy (IgAN) and non-IgAN.We prospectively enrolled patients with chronic kidney disease who underwent renal biopsy from May 2022 to December 2022 and performed an ultrasound and SMI the day before renal biopsy. The selected patients were randomly divided into training and testing cohorts in a 7:3 ratio. We extracted DL and radiometric features from the two-dimensional ultrasound and SMI images. A combined nomograph model was developed by combining the predictive probability of DL with clinical factors using multivariate logistic regression analysis. The proposed model's utility was evaluated using receiver operating characteristics, calibration, and decision curve analysis. In this study, 120 patients with primary glomerular disease were included, including 84 in the training and 36 in the test cohorts. In the testing cohort, the ROC of the radiomics model was 0.816 (95% CI:0.663-0.968), and the ROC of the DL model was 0.844 (95% CI:0.717-0.971). The nomogram model combined with independent clinical risk factors (IgA and hematuria) showed strong discrimination, with an ROC of 0.884 (95% CI:0.773-0.996) in the testing cohort. Decision curve analysis verified the clinical practicability of the combined nomogram. The combined nomogram model based on SMI can accurately and noninvasively distinguish IgAN from non-IgAN and help physicians make clearer patient treatment plans.
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Affiliation(s)
- Xiachuan Qin
- Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nan Chong, Sichuan Province, China
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Linlin Xia
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Qianqing Ma
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Dongliang Cheng
- Hebin Intelligent Robots Co., LTD, Hefei, Anhui Province, China
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
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Gao Z, Yu Z, Zhang X, Chen C, Pan Z, Chen X, Lin W, Chen J, Zhuge Q, Shen X. Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images. Front Oncol 2023; 13:1265366. [PMID: 37869090 PMCID: PMC10587601 DOI: 10.3389/fonc.2023.1265366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 09/15/2023] [Indexed: 10/24/2023] Open
Abstract
Background Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis. Methods In this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score. Results The segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set. Conclusion The results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.
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Affiliation(s)
- Zhihong Gao
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhuo Yu
- School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
| | - Xiang Zhang
- Wenzhou Data Management and Development Group Co., Ltd., Wenzhou, Zhejiang, China
| | - Chun Chen
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhifang Pan
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaodong Chen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Weihong Lin
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jun Chen
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qichuan Zhuge
- Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xian Shen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Xin Z, Yan W, Feng Y, Yunzhi L, Zhang Y, Wang D, Chen W, Peng J, Guo C, Chen Z, Wang X, Zhu J, Lei J. An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study. Cancer Med 2023; 12:19383-19393. [PMID: 37772478 PMCID: PMC10587964 DOI: 10.1002/cam4.6525] [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: 03/30/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND AND PURPOSE Neoadjuvant chemotherapy (NACT) has become an essential component of the comprehensive treatment of cervical squamous cell carcinoma (CSCC). However, not all patients respond to chemotherapy due to individual differences in sensitivity and tolerance to chemotherapy drugs. Therefore, accurately predicting the sensitivity of CSCC patients to NACT was vital for individual chemotherapy. This study aims to construct a machine learning radiomics model based on magnetic resonance imaging (MRI) to assess its efficacy in predicting NACT susceptibility among CSCC patients. METHODS This study included 234 patients with CSCC from two hospitals, who were divided into a training set (n = 180), a testing set (n = 20), and an external validation set (n = 34). Manual radiomic features were extracted from transverse section MRI images, and feature selection was performed using the recursive feature elimination (RFE) method. A prediction model was then generated using three machine learning algorithms, namely logistic regression, random forest, and support vector machines (SVM), for predicting NACT susceptibility. The model's performance was assessed based on the area under the receiver operating characteristic curve (AUC), accuracy, and sensitivity. RESULTS The SVM approach achieves the highest scores on both the testing set and the external validation set. In the testing set and external validation set, the AUC of the model was 0.88 and 0.764, and the accuracy was 0.90 and 0.853, the sensitivity was 0.93 and 0.962, respectively. CONCLUSIONS Machine learning radiomics models based on MRI images have achieved satisfactory performance in predicting the sensitivity of NACT in CSCC patients with high accuracy and robustness, which has great significance for the treatment and personalized medicine of CSCC patients.
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Affiliation(s)
- Zhonghong Xin
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Wanying Yan
- Infervision Medical Technology Co., LtdBeijingChina
| | - Yibo Feng
- Infervision Medical Technology Co., LtdBeijingChina
| | - Li Yunzhi
- Department of RadiologyGansu Provincial Maternity and Child‐care HospitalLanzhouChina
| | - Yaping Zhang
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Dawei Wang
- Infervision Medical Technology Co., LtdBeijingChina
| | - Weidao Chen
- Infervision Medical Technology Co., LtdBeijingChina
| | - Jianhong Peng
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Cheng Guo
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Zixian Chen
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Xiaohui Wang
- Department of Gynecology and ObstetricsThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Jun Zhu
- Department of PathologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Junqiang Lei
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
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Meng Y, Yang Y, Hu M, Zhang Z, Zhou X. Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application. Semin Cancer Biol 2023; 95:75-87. [PMID: 37499847 DOI: 10.1016/j.semcancer.2023.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 07/29/2023]
Abstract
Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.
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Affiliation(s)
- Yichen Meng
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Yue Yang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Miao Hu
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Zheng Zhang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
| | - Xuhui Zhou
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
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Wang Y, Ding Y, Liu X, Li X, Jia X, Li J, Zhang H, Song Z, Xu M, Ren J, Sun D. Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study. Cancer Imaging 2023; 23:83. [PMID: 37679806 PMCID: PMC10485937 DOI: 10.1186/s40644-023-00605-3] [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: 05/03/2023] [Accepted: 08/27/2023] [Indexed: 09/09/2023] Open
Abstract
OBJECTIVE To develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS). MATERIALS AND METHODS The most recent preoperative thin-section chest CT scans and postoperative pathological haematoxylin and eosin-stained sections were retrospectively collected from patients with a postoperative pathological diagnosis of stage I LUAD. Regions of interest were manually segmented, and radiomics features were extracted from the tumour and peritumoral regions extended by 3 voxel units, 6 voxel units, and 12 voxel units, and 2D and 3D deep learning image features were extracted by convolutional neural networks. Then, the RAdiomics Integrated with STAS model (RAISm) was constructed. The performance of RAISm was then evaluated in a development cohort and validation cohort. RESULTS A total of 226 patients from two medical centres from January 2015 to December 2018 were retrospectively included as the development cohort for the model and were randomly split into a training set (72.6%, n = 164) and a test set (27.4%, n = 62). From June 2019 to December 2019, 51 patients were included in the validation cohort. RAISm had excellent discrimination in predicting the early recurrence of stage I LUAD in the training cohort (AUC = 0.847, 95% CI 0.762-0.932) and validation cohort (AUC = 0.817, 95% CI 0.625-1.000). RAISm outperformed single modality signatures and other combinations of signatures in terms of discrimination and clinical net benefits. CONCLUSION We pioneered combining preoperative CT-based radiomics with STAS to predict stage I LUAD recurrence postoperatively and confirmed the superior effect of the model in validation cohorts, showing its potential to assist in postoperative treatment strategies.
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Affiliation(s)
- Yuhang Wang
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Yun Ding
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Xin Liu
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Xin Li
- Department of Thoracic Surgery, Tianjin Chest Hospital of Tianjin University, No. 261, Taierzhuang South Road, Jinnan District, Tianjin, 300222, China
| | - Xiaoteng Jia
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Jiuzhen Li
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Han Zhang
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Zhenchun Song
- Department of Imaging, Tianjin Chest Hospital of Tianjin University, Tianjin, China
| | - Meilin Xu
- Department of Pathology, Tianjin Chest Hospital of Tianjin University, Tianjin, China
| | - Jie Ren
- Graduate School, Tianjin Medical University, Tianjin, China
- Department of Thoracic Surgery, Tianjin Jinnan Hospital, Tianjin, China
| | - Daqiang Sun
- Graduate School, Tianjin Medical University, Tianjin, China.
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
- Department of Thoracic Surgery, Tianjin Chest Hospital of Tianjin University, No. 261, Taierzhuang South Road, Jinnan District, Tianjin, 300222, China.
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Sun Z, Shi Z, Xin Y, Zhao S, Jiang H, Li J, Li J, Jiang H. Contrast-Enhanced CT Imaging Features Combined with Clinical Factors to Predict the Efficacy and Prognosis for Transarterial Chemoembolization of Hepatocellular Carcinoma. Acad Radiol 2023; 30 Suppl 1:S81-S91. [PMID: 36803649 DOI: 10.1016/j.acra.2022.12.031] [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: 11/11/2022] [Revised: 12/15/2022] [Accepted: 12/18/2022] [Indexed: 02/19/2023]
Abstract
RATIONALE AND OBJECTIVES Accurate prediction of treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) is critical for precision treatment. This study aimed to develop a comprehensive model (DLRC) that incorporates contrast-enhanced computed tomography (CECT) images and clinical factors to predict the response to TACE in patients with HCC. MATERIALS AND METHODS A total of 399 patients with intermediate-stage HCC were included in this retrospective study. Deep learning and radiomic signatures were established based on arterial phase CECT images, Correlation analysis and the least absolute shrinkage and selection (LASSO) regression analysis were applied for features selection. The DLRC model incorporating deep learning radiomic signatures and clinical factors was developed using multivariate logistic regression. The area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the models. Kaplan-Meier survival curves based on the DLRC were plotted to assess overall survival in the follow-up cohort (n = 261). RESULTS The DLRC model was developed using 19 quantitative radiomic features, 10 deep learning features, and 3 clinical factors. The AUC of the DLRC model was 0.937 (95% confidence interval [CI], 0.912-0.962) and 0.909 (95% CI, 0.850-0.968) in the training and validation cohorts, respectively, outperforming models established with two signatures or a single signature (p < 0.05). Stratified analysis showed that the DLRC was not statistically different between subgroups (p > 0.05), and the DCA confirmed the greater net clinical benefit. In addition, multivariable cox regression revealed that DLRC model outputs were independent risk factors for the overall survival (hazard ratios: 1.20, 95% CI: 1.03-1.40; p = 0.019). CONCLUSION The DLRC model exhibited a remarkable accuracy in predicting response to TACE, and it can be utilized as a potent tool for precision treatment.
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Affiliation(s)
- Zhongqi Sun
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
| | - Zhongxing Shi
- Department of Interventional Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yanjie Xin
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
| | - Sheng Zhao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
| | - Hao Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
| | - Jinping Li
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
| | - Jiaping Li
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China.
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Li J, Zhang HL, Yin HK, Zhang HK, Wang Y, Xu SN, Ma F, Gao JB, Li HL, Qu JR. Comparison of MRI and CT-Based Radiomics and Their Combination for Early Identification of Pathological Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer. J Magn Reson Imaging 2023; 58:907-923. [PMID: 36527425 DOI: 10.1002/jmri.28570] [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: 10/10/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Current radiomics for treatment response assessment in gastric cancer (GC) have focused solely on Computed tomography (CT). The importance of multi-parametric magnetic resonance imaging (mp-MRI) radiomics in GC is less clear. PURPOSE To compare and combine CT and mp-MRI radiomics for pretreatment identification of pathological response to neoadjuvant chemotherapy in GC. STUDY TYPE Retrospective. POPULATION Two hundred twenty-five GC patients were recruited and split into training (157) and validation dataset (68) in the ratio of 7:3 randomly. FIELD/SEQUENCE T2-weighted fast spin echo (fat suppressed T2-weighted imaging [fs-T2WI]), diffusion weighted echo planar imaging (DWI), and fast gradient echo (dynamic contrast enhanced [DCE]) sequences at 3.0T. ASSESSMENT Apparent diffusion coefficient (ADC) maps were generated from DWI. CT, fs-T2WI, ADC, DCE, and mp-MRI Radiomics score (Radscores) were compared between responders and non-responders. A multimodal nomogram combining CT and mp-MRI Radscores was developed. Patients were followed up for 3-65 months (median 19) after surgery, the overall survival (OS) and progression free survival (PFS) were calculated. STATISTICAL TESTS A logistic regression classifier was applied to construct the five models. Each model's performance was evaluated using a receiver operating characteristic curve. The association of the nomogram with OS/PFS was evaluated by Kaplan-Meier survival analysis and C-index. A P value <0.05 was considered statistically significant. RESULTS CT Radscore, mp-MRI Radscore and nomogram were significantly associated with tumor regression grading. The nomogram achieved the highest area under the curves (AUCs) of 0.893 (0.834-0.937) and 0.871 (0.767-0.940) in training and validation datasets, respectively. The C-index was 0.589 for OS and 0.601 for PFS. The AUCs of the mp-MRI model were not significantly different to that of the CT model in training (0.831 vs. 0.770, P = 0.267) and validation dataset (0.797 vs. 0.746, P = 0.137). DATA CONCLUSIONS mp-MRI radiomics provides similar results to CT radiomics for early identification of pathologic response to neoadjuvant chemotherapy. The multimodal radiomics nomogram further improved the capability. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Jing Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Hui-Ling Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Hong-Kun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Hong-Kai Zhang
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Yi Wang
- Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Shu-Ning Xu
- Department of Digestive Oncology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Fei Ma
- Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hai-Liang Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Jin-Rong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
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Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. Semin Cancer Biol 2023; 93:83-96. [PMID: 37116818 DOI: 10.1016/j.semcancer.2023.04.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/12/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.
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Affiliation(s)
- Zhe Wang
- Department of Digestive Diseases 1, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning, China
| | - Yang Liu
- Department of Gastric Surgery, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning, China.
| | - Xing Niu
- China Medical University, Shenyang 110122, Liaoning, China.
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Zhang QW, Yang PP, Gao YJY, Li ZH, Yuan Y, Li SJ, Duan SF, Shao CW, Hao Q, Lu Y, Chen Q, Shen F. Assessing synchronous ovarian metastasis in gastric cancer patients using a clinical-radiomics nomogram based on baseline abdominal contrast-enhanced CT: a two-center study. Cancer Imaging 2023; 23:71. [PMID: 37488597 PMCID: PMC10367237 DOI: 10.1186/s40644-023-00584-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/09/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND To build and validate a radiomics nomogram based on preoperative CT scans and clinical data for detecting synchronous ovarian metastasis (SOM) in female gastric cancer (GC) cases. METHODS Pathologically confirmed GC cases in 2 cohorts were retrospectively enrolled. All cases had presurgical abdominal contrast-enhanced CT and pelvis contrast-enhanced MRI and pathological examinations for any suspicious ovarian lesions detected by MRI. Cohort 1 cases (n = 101) were included as the training set. Radiomics features were obtained to develop a radscore. A nomogram combining the radscore and clinical factors was built to detect SOM. The bootstrap method was carried out in cohort 1 as internal validation. External validation was carried out in cohort 2 (n = 46). Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and the confusion matrix were utilized to assess the performances of the radscore, nomogram and subjective evaluation model. RESULTS The nomogram, which combined age and the radscore, displayed a higher AUC than the radscore and subjective evaluation (0.910 vs 0.827 vs 0.773) in the training cohort. In the external validation cohort, the nomogram also had a higher AUC than the radscore and subjective evaluation (0.850 vs 0.790 vs 0.675). DCA and the confusion matrix confirmed the nomogram was superior to the radscore in both cohorts. CONCLUSIONS This pilot study showed that a nomogram model combining the radscore and clinical characteristics is useful in detecting SOM in female GC cases. It may be applied to improve clinical treatment and is superior to subjective evaluation or the radscore alone.
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Affiliation(s)
- Qian-Wen Zhang
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Pan-Pan Yang
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Yong-Jun-Yi Gao
- Department of Emergency, the Eighth Medical Center of Chinese, PLA General Hospital, 17 Heishanhu Rd, Haidian District, Beijing, 100091, China
| | - Zhi-Hui Li
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yuan Yuan
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Si-Jie Li
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Shao-Feng Duan
- GE Healthcare China, Pudong New Town, No.1 Huatuo Road, Shanghai, 210000, China
| | - Cheng-Wei Shao
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Qiang Hao
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qi Chen
- Department of Health Statistics, The Navy Medical University, Shanghai, 200433, China.
| | - Fu Shen
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China.
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Zhao LT, Liu ZY, Xie WF, Shao LZ, Lu J, Tian J, Liu JG. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments? Mil Med Res 2023; 10:29. [PMID: 37357263 DOI: 10.1186/s40779-023-00464-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023] Open
Abstract
The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.
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Affiliation(s)
- Li-Tao Zhao
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhen-Yu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Wan-Fang Xie
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Li-Zhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Jian Lu
- Department of Urology, Peking University Third Hospital, Peking University, 100191, Beijing, China.
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
| | - Jian-Gang Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, 100029, China.
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Jiang Z, Xie W, Zhou X, Pan W, Jiang S, Zhang X, Zhang M, Zhang Z, Lu Y, Wang D. A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics. Insights Imaging 2023; 14:104. [PMID: 37286810 DOI: 10.1186/s13244-023-01438-1] [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: 03/09/2023] [Accepted: 04/15/2023] [Indexed: 06/09/2023] Open
Abstract
OBJECTIVES This study aims to develop and validate a virtual biopsy model to predict microsatellite instability (MSI) status in preoperative gastric cancer (GC) patients based on clinical information and the radiomics of deep learning algorithms. METHODS A total of 223 GC patients with MSI status detected by postoperative immunohistochemical staining (IHC) were retrospectively recruited and randomly assigned to the training (n = 167) and testing (n = 56) sets in a 3:1 ratio. In the training set, 982 high-throughput radiomic features were extracted from preoperative abdominal dynamic contrast-enhanced CT (CECT) and screened. According to the deep learning multilayer perceptron (MLP), 15 optimal features were optimized to establish the radiomic feature score (Rad-score), and LASSO regression was used to screen out clinically independent predictors. Based on logistic regression, the Rad-score and clinically independent predictors were integrated to build the clinical radiomics model and visualized as a nomogram and independently verified in the testing set. The performance and clinical applicability of hybrid model in identifying MSI status were evaluated by the area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve (DCA). RESULTS The AUCs of the clinical image model in training set and testing set were 0.883 [95% CI: 0.822-0.945] and 0.802 [95% CI: 0.666-0.937], respectively. This hybrid model showed good consistency in the calibration curve and clinical applicability in the DCA curve, respectively. CONCLUSIONS Using preoperative imaging and clinical information, we developed a deep-learning-based radiomics model for the non-invasive evaluation of MSI in GC patients. This model maybe can potentially support clinical treatment decision making for GC patients.
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Affiliation(s)
- Zinian Jiang
- Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Wentao Xie
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 1677, Wutaishan Road, Qingdao, 266000, Shandong, China
| | - Xiaoming Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Wenjun Pan
- Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Sheng Jiang
- Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Xianxiang Zhang
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 1677, Wutaishan Road, Qingdao, 266000, Shandong, China
| | - Maoshen Zhang
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 1677, Wutaishan Road, Qingdao, 266000, Shandong, China
| | - Zhenqi Zhang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yun Lu
- Qingdao Medical College, Qingdao University, Qingdao, Shandong, China.
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 1677, Wutaishan Road, Qingdao, 266000, Shandong, China.
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, Shandong, China.
| | - Dongsheng Wang
- Qingdao Medical College, Qingdao University, Qingdao, Shandong, China.
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 1677, Wutaishan Road, Qingdao, 266000, Shandong, China.
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Li J, Yin H, Wang Y, Zhang H, Ma F, Li H, Qu J. Multiparametric MRI-based radiomics nomogram for early prediction of pathological response to neoadjuvant chemotherapy in locally advanced gastric cancer. Eur Radiol 2023; 33:2746-2756. [PMID: 36512039 DOI: 10.1007/s00330-022-09219-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/01/2022] [Accepted: 10/03/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES To build and validate a multi-parametric MRI (mpMRI)-based radiomics nomogram for early prediction of treatment response to neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer. METHODS Baseline MRI were retrospectively enrolled from 141 patients with gastric adenocarcinoma who received NAC followed by radical gastrectomy. According to pathologic response of tumor regression grading (TRG), patients were labeled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3) and further divided into a training (n = 85) and validation dataset (n = 56). Radiomics score (Radscore) were built from T2WI, ADC, and venous phase of dynamic-contrasted-enhanced MR imaging. Clinical information, laboratory indicators, MRI parameters, and follow-up data were also recorded. According to multivariable regression analysis, an mpMRI radiomics nomogram was built and its predictive ability was evaluated by ROC analysis. Decision curve analysis was applied to evaluate the clinical usefulness. Kaplan-Meier survival curves based on the nomogram were used to estimate the progression-free survival (PFS) and overall survival (OS) in the validation dataset. RESULTS Both single sequence-based Radscores and mpMRI radiomics nomogram were associated with pathologic response (p < 0.001). The nomogram achieved the highest diagnostic ability with area under ROC curve of 0.844 (95% CI, 0.749-0.914) and 0.820 (95% CI, 0.695-0.910) in the training and validation datasets. The hazard ratio of the nomogram for PFS and OS prediction was 2.597 (95% CI: 1.046-6.451, log-rank p = 0.023) and 2.570 (95% CI: 1.166-5.666, log-rank p = 0.011). CONCLUSIONS The mpMRI-based radiomics nomogram showed preferable performance in predicting pathologic response to NAC in LAGC. KEY POINTS • This study investigated the value of multi-parametric MRI-based radiomics in predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer. • The nomogram incorporating T2WI Radscore, ADC Radscore, and DCE Radscore as well as Borrmann classification outperformed the single sequence-based Radscore. • The nomogram also exhibited a promising prognostic ability for patient survival and enriched radiomics studies in gastric cancer.
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Affiliation(s)
- Jing Li
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127, Dongming Road, Zhengzhou, 450008, Henan, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Yi Wang
- Department of Pathology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Hongkai Zhang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127, Dongming Road, Zhengzhou, 450008, Henan, China
| | - Fei Ma
- Department of Gastrointestinal surgery, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Hailiang Li
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127, Dongming Road, Zhengzhou, 450008, Henan, China.
| | - Jinrong Qu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127, Dongming Road, Zhengzhou, 450008, Henan, China.
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Zhang Y, Wu C, Xiao Z, Lv F, Liu Y. A Deep Learning Radiomics Nomogram to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Cervical Cancer: A Two-Center Study. Diagnostics (Basel) 2023; 13:diagnostics13061073. [PMID: 36980381 PMCID: PMC10047639 DOI: 10.3390/diagnostics13061073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023] Open
Abstract
Purpose: This study aimed to establish a deep learning radiomics nomogram (DLRN) based on multiparametric MR images for predicting the response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: Patients with LACC (FIGO stage IB-IIIB) who underwent preoperative NACT were enrolled from center 1 (220 cases) and center 2 (independent external validation dataset, 65 cases). Handcrafted and deep learning-based radiomics features were extracted from T2WI, DWI and contrast-enhanced (CE)-T1WI, and radiomics signatures were built based on the optimal features. Two types of radiomics signatures and clinical features were integrated into the DLRN for prediction. The AUC, calibration curve and decision curve analysis (DCA) were employed to illustrate the performance of these models and their clinical utility. In addition, disease-free survival (DFS) was assessed by Kaplan–Meier survival curves based on the DLRN. Results: The DLRN showed favorable predictive values in differentiating responders from nonresponders to NACT with AUCs of 0.963, 0.940 and 0.910 in the three datasets, with good calibration (all p > 0.05). Furthermore, the DLRN performed better than the clinical model and handcrafted radiomics signature in all datasets (all p < 0.05) and slightly higher than the DL-based radiomics signature in the internal validation dataset (p = 0.251). DCA indicated that the DLRN has potential in clinical applications. Furthermore, the DLRN was strongly correlated with the DFS of LACC patients (HR = 0.223; p = 0.004). Conclusion: The DLRN performed well in preoperatively predicting the therapeutic response in LACC and could provide valuable information for individualized treatment.
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Affiliation(s)
- Yajiao Zhang
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China;
| | - Chao Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Zhibo Xiao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Furong Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yanbing Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China;
- Correspondence:
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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44
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Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, Yao L, Hu Y, Wang ZR, Yu HH. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview. World J Gastroenterol 2022; 28:6363-6379. [PMID: 36533112 PMCID: PMC9753055 DOI: 10.3748/wjg.v28.i45.6363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/25/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.
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Affiliation(s)
- Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - In Neng Chan
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Hao-Ming Yan
- School of Clinical Medicine, China Medical University, Shenyang 110013, Liaoning Province, China
| | - Shan Gao
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, Hubei Province, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China
| | - Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Liang Yao
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Ying Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Zhong-Ren Wang
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Hon Ho Yu
- Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
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45
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Huang W, Wang J, Wang H, Zhang Y, Zhao F, Li K, Su L, Kang F, Cao X. PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features. Front Pharmacol 2022; 13:898529. [PMID: 35571081 PMCID: PMC9092283 DOI: 10.3389/fphar.2022.898529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/11/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC). Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients' clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted. Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86-0.96) was higher than that of RM (0.82, 95% CI: 0.75-0.89) and DLM (0.90, 95% CI: 0.85-0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77-0.93) was also higher than that of RM (0.68, 95% CI: 0.52-0.84) and DLM (0.79, 95% CI: 0.67-0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models. Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes.
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Affiliation(s)
- Weicheng Huang
- School of Information Science and Technology, Northwest University, Xi'an, China.,National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, China
| | - Jingyi Wang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Haolin Wang
- School of Information Science and Technology, Northwest University, Xi'an, China.,National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, China
| | - Yuxiang Zhang
- School of Information Science and Technology, Northwest University, Xi'an, China.,National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, China
| | - Fengjun Zhao
- School of Information Science and Technology, Northwest University, Xi'an, China.,National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, China
| | - Kang Li
- School of Information Science and Technology, Northwest University, Xi'an, China.,National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, China
| | - Linzhi Su
- School of Information Science and Technology, Northwest University, Xi'an, China.,National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, China
| | - Fei Kang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xin Cao
- School of Information Science and Technology, Northwest University, Xi'an, China.,National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, China
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