Bladder Cancer Prognosis Using Deep Neural Networks and Histopathology Images

Recent studies indicate bladder cancer is among the top 10 most common cancer in the world [1]. Bladder cancer frequently reoccurs, and prognostic judgments may vary among clinicians. Classification of histopathology slides is essential for accurate prognosis and effective treatment of bladder cancer patients, as a favorable prognosis might help to inform less aggressive treatment plans. Developing automated and accurate histopathology image analysis methods can help pathologists in determining the prognosis of bladder cancer. In this study, we introduced Bladder4Net, a deep learning pipeline to classify whole-slide histopathology images of bladder cancer into two classes: low-risk (combination of PUNLMP and low-grade tumors) and high-risk (combination of high-grade and invasive tumors). This pipeline consists of 4 convolutional neural network (CNN) based classifiers to address the difficulties of identifying PUNLMP and invasive classes. We evaluated our pipeline on 182 independent whole-slide images from the New Hampshire Bladder Cancer Study (NHBCS) [22] [23] [24] collected from 1994 to 2004 and 378 external digitized slides from The Cancer Genome Atlas (TCGA) database [26]. The weighted average F1-score of our approach was 0.91 (95% confidence interval (CI): 0.86–0.94) on the NHBCS dataset and 0.99 (95% CI: 0.97–1.00) on the TCGA dataset. Additionally, we computed Kaplan-Meier survival curves for patients predicted as high-risk versus those predicted as low-risk. For the NHBCS test set, patients predicted as high-risk had worse overall survival than those predicted as low-risk, with a Log-rank P-value of 0.004. If validated through prospective trials, our model could be used in clinical settings to improve patient care.