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

• Published in: Scientific reports 2024-08, Vol.14 (1), p.18931-11
• Main Authors: Wang, Guang-Yue, Zhu, Jing-Fei, Wang, Qi-Chao, Qin, Jia-Xin, Wang, Xin-Lei, Liu, Xing, Liu, Xin-Yu, Chen, Jun-Zhi, Zhu, Jie-Fei, Zhuo, Shi-Chao, Wu, Di, Li, Na, Chao, Liu, Meng, Fan-Lai, Lu, Hao, Shi, Zhen-Duo, Jia, Zhi-Gang, Han, Cong-Hui
• Format: Article
• Language: English
• Subjects: 631/67; 692/4028; Aged; Bladder cancer; Deep Learning; Female; Humanities and Social Sciences; Humans; Male; Middle Aged; multidisciplinary; Neoplasm Invasiveness; Neoplasm Recurrence, Local - pathology; Non-Muscle Invasive Bladder Neoplasms; Observational learning; Pathology; Patients; Predictions; ROC Curve; Science; Science (multidisciplinary); Training; Transfer learning; Urinary Bladder Neoplasms - pathology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-66870-9

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.