Related Experiment Video
Updated: Nov 24, 2025
Induction of Invasive Transitional Cell Bladder Carcinoma in Immune Intact Human MUC1 Transgenic Mice: A Model for Immunotherapy Development
Published on: October 30, 2013
Deep Learning-based Recurrence Prediction in Patients with Non-muscle-invasive Bladder Cancer
Marit Lucas1, Ilaria Jansen2, Ton G van Leeuwen1
1Department of Biomedical Engineering and Physics, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Background:
Non-muscle-invasive bladder cancer (NMIBC) is characterized by frequent recurrence of the disease, which is difficult to predict.
Objective:
To combine digital histopathology slides with clinical data to predict 1- and 5-yr recurrence-free survival of NMIBC patients using deep learning.
Design, Setting, And Participants:
Data of patients undergoing a transurethral resection of a bladder tumor between 2000 and 2018 at a Dutch academic medical center were selected. Corresponding histological slides were digitized. A three-step approach was used to predict 1- and 5-yr recurrence-free survival. First, a segmentation network was used to detect the urothelium on the digital histopathology slides. Second, a selection network was trained for the selection of patches associated with recurrence. Third, a classification network, combining the information of the selection network with clinical data, was trained to give the probability of 1- and 5-yr recurrence-free survival.
Outcome Measurements And Statistical Analysis:
The accuracy of the deep learning-based model was compared with a multivariable logistic regression model using clinical data only.
Results And Limitations:
In the 1- and 5-yr follow-up cohorts, 359 and 281 patients were included with recurrence rates of 27% and 63%, respectively. The areas under the curve (AUCs) of the model combining digital histopathology slide data with clinical data were 0.62 and 0.76 for 1- and 5-yr recurrence predictions, respectively, which were higher than those of the model using digital histopathology slide data only (AUCs of 0.56 and 0.72, respectively) and the multivariable logistic regression (AUCs of 0.58 and 0.57, respectively).
Conclusions:
In our population, the deep learning-based model combining digital histopathology slides and clinical data enhances the prediction of recurrence (within 5 yr) compared with models using clinical data or image data only.
Patient Summary:
By combining histopathology images and patient record data using deep learning, the prediction of recurrence in bladder cancer patients is enhanced.
