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 d

SUBMITTER: Wang GY 

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

REPOSITORIES: biostudies-literature