Deep learning-based model for prediction of early recurrence and therapy response on whole slide images in non-muscle-invasive bladder cancer: a retrospective, multicentre study

作者：Fan Jiang, Guibin Hong, Hong Zeng, Zhen Lin, Ye Liu, Xun Xu, Runnan Shen, Ye Xie, Yun Luo, Yun Wang, Mengyi Zhu, Hongkun Yang, Haoxuan Wang, Shuting Huang, Rui Chen, Tianxin Lin, Shaoxu Wu · 发表于：EClinicalMedicine · 年份：2025 · DOI：10.1016/j.eclinm.2025.103125 · 被引用次数：8 · 研究领域：Bladder and Urothelial Cancer Treatments、AI in cancer detection、Ferroptosis and cancer prognosis

Background: Accurate 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. Methods: In 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 mode...