Dataset Information

Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image.

ABSTRACT: We aimed to build a deep learning-based pathomics model to predict the early recurrence of non-muscle-infiltrating bladder cancer (NMIBC) in this work. A total of 147 patients from Xuzhou Central Hospital were enrolled as the training cohort, and 63 patients from Suqian Affiliated Hospital of Xuzhou Medical University were enrolled as the test cohort. Based on two consecutive phases of patch level prediction and WSI-level predictione, we built a pathomics model, with the initial model developed in the training cohort and subjected to transfer learning, and then the test cohort was validated for generalization. The features extracted from the visualization model were used for model interpretation. After migration learning, the area under the receiver operating characteristic curve for the deep learning-based pathomics model in the test cohort was 0.860 (95% CI 0.752-0.969), with good agreement between the migration training cohort and the test cohort in predicting recurrence, and the predicted values matched well with the observed values, with p values of 0.667766 and 0.140233 for the Hosmer-Lemeshow test, respectively. The good clinical application was observed using a decision curve analysis method. We developed a deep learning-based pathomics model showed promising performance in predicting recurrence within one year in NMIBC patients. Including 10 state prediction NMIBC recurrence group pathology features be visualized, which may be used to facilitate personalized management of NMIBC patients to avoid ineffective or unnecessary treatment for the benefit of patients.

SUBMITTER: Wang GY

PROVIDER: S-EPMC11327297 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image.

Wang Guang-Yue GY Zhu Jing-Fei JF Wang Qi-Chao QC Qin Jia-Xin JX Wang Xin-Lei XL Liu Xing X Liu Xin-Yu XY Chen Jun-Zhi JZ Zhu Jie-Fei JF Zhuo Shi-Chao SC Wu Di D Li Na N Chao Liu L Meng Fan-Lai FL Lu Hao H Shi Zhen-Duo ZD Jia Zhi-Gang ZG Han Cong-Hui CH

Scientific reports 20240815 1

We aimed to build a deep learning-based pathomics model to predict the early recurrence of non-muscle-infiltrating bladder cancer (NMIBC) in this work. A total of 147 patients from Xuzhou Central Hospital were enrolled as the training cohort, and 63 patients from Suqian Affiliated Hospital of Xuzhou Medical University were enrolled as the test cohort. Based on two consecutive phases of patch level prediction and WSI-level predictione, we built a pathomics model, with the initial model developed ...[more]

PMID: 39147803

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Project description:BackgroundAccurate prediction of early recurrence is essential for disease management of patients with non-muscle-invasive bladder cancer (NMIBC). We aimed to develop and validate a deep learning-based early recurrence predictive model (ERPM) and a treatment response predictive model (TRPM) on whole slide images to assist clinical decision making.MethodsIn this retrospective, multicentre study, we included consecutive patients with pathology-confirmed NMIBC who underwent transurethral resection of bladder tumour from five centres. Patients from one hospital (Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China) were assigned to training and internal validation cohorts, and patients from four other hospitals (the Third Affiliated Hospital of Sun Yat-sen University, and Zhujiang Hospital of Southern Medical University, Guangzhou, China; the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China; Shenshan Medical Centre, Shanwei, China) were assigned to four independent external validation cohorts. Based on multi-instance and ensemble learning, the ERPM was developed to make predictions on haematoxylin and eosin (H&E) staining and immunohistochemistry staining slides. Sharing the same architecture of the ERPM, the TRPM was trained and evaluated by cross validation on patients who received Bacillus Calmette-Guérin (BCG). The performance of the ERPM was mainly evaluated and compared with the clinical model, H&E-based model, and integrated model through the area under the curve. Survival analysis was performed to assess the prognostic capability of the ERPM.FindingsBetween January 1, 2017, and September 30, 2023, 4395 whole slide images of 1275 patients were included to train and validate the models. The ERPM was superior to the clinical and H&E-based model in predicting early recurrence in both internal validation cohort (area under the curve: 0.837 vs 0.645 vs 0.737) and external validation cohorts (area under the curve: 0.761-0.802 vs 0.626-0.682 vs 0.694-0.723) and was on par with the integrated model. It also stratified recurrence-free survival significantly (p < 0.0001) with a hazard ratio of 4.50 (95% CI 3.10-6.53). The TRPM performed well in predicting BCG-unresponsive NMIBC (accuracy 84.1%).InterpretationThe ERPM showed promising performance in predicting early recurrence and recurrence-free survival of patients with NMIBC after surgery and with further validation and in combination with TRPM could be used to guide the management of NMIBC.FundingNational Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, the Guangdong Provincial Clinical Research Centre for Urological Diseases, and the Science and Technology Projects in Guangzhou.

Dataset Information

Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image.

Publications

Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image.

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