Predicting bladder cancer molecular subtypes linked to bacillus Calmette-Guerin response from histology images using deep learning

Background and objectiveHighrisk nonmuscleinvasive bladder cancer (HRNMIBC) is treated with transurethral resection and intravesical BCG instillations, yet {approx}50% recur and 20% progress to invasive disease. Although molecular subtyping, e.g., BCG-response-subtype (BRS), is associated with progression risk and may aid risk stratification, yet is costly and time-consuming. Intratumoral heterogeneity complicates accurate subtyping. To address these challenges, we developed a deep-learning model that predicts BRS from routine hematoxylin-eosin-stained images. We verified the models area-by-area predictions against tissue-level gene-expression maps. Methods and participantsHematoxylin-eosin-stained images from 231 HR-NMIBC patients with known BRS were used to develop a deep-learning model through cross-validation, then validated in 83 independent samples. The models spatial predictions were assessed using spatial transcriptomics to map gene expression to tissue locations in five HR-NMIBC tumors. Outcome measurements and statistical analysisDiscriminative ability for BRS3 vs. BRS1/2 was measured by AUC. Spatial alignment was assessed by calculating Pearson and Spearman correlation coefficients between model predictions and BRS fractions; significance was assessed through permutation analysis. Key findings and limitationsThe trained algorithm achieved AUC of 0.79 (development) and 0.71 (external) to detect BRS3 vs BRS1/2. Tile-level correlation between model output and molecular labels was significant (Pearson r = 0.33-0.44; p [≤] 0.002). Limitations include retrospective sampling and limited spatial transcriptomic cases. Conclusions and clinical implicationsOur trained algorithm showed potential to stratify HRNMIBC patients by clinically relevant BCGresponse subtypes using routine hematoxylin-eosin-stained images and showed predicted spatial heterogeneity comparable to molecular profiling. Prospective validation is required before any clinical implementation. Patient summaryStandard pathology images contain hidden details related to tumors molecular subtype. We trained an AI model to read these routine images and identify specific bladder cancer subtypes associated with poor response to BCG therapy. This approach may help reveal molecular subtype-associated information from routine pathology images, without additional laboratory procedures.