Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides

SIMPLE SUMMARY: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer. However, current techniques of diagnosis are susceptible to pathologist variability, and histopathological prognostic methods are insufficient to cover all features of muscle-invasive bladder cancer. In this work, we developed weakly supervised models based on deep learning for the diagnosis of bladder cancer and prediction of overall survival in muscle-invasive bladder cancer patients using whole slide digitized histological images in two cohorts. Encouragingly, results showed that our models can not only assist clinicians in the accurate diagnosis of bladder cancer, but also facilitate differential risk stratification in patients with muscle-invasive bladder cancer and improve personalized treatment decisions accordingly. Furthermore, the regions most relevant for diagnosis or prognosis can be further analyzed to increase the amount of information extracted from pathological images. Finally, we identified six genes closely related to cancer progression based on the predicted risk scores, which potentially led to new biomarker discoveries. ABSTRACT: (1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.