Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review

SIMPLE SUMMARY: The diagnosis and prediction of prognosis for bladder cancer (BC) can be challenging because of the subjective nature of pathological evaluation. Artificial intelligence (AI) has emerged as a promising technology for improving the accuracy of BC diagnosis and prediction of prognosis. We reviewed all available studies that used AI to analyze images from BC tumor tissue that aimed to improve diagnosis or prediction of prognosis. Studies showed that specific tumor characteristics can be used to predict treatment response by analyzing BC tumor tissue images. Combining histopathological images with clinical information enables AI models to perform with high accuracy. In conclusion, AI has the potential to assist physicians in gaining more accurate diagnoses and treatment response predictions. Yet, important challenges should be addressed, such as ensuring reliability, interpretability, and performance—future research should address these caveats. ABSTRACT: Bladder cancer (BC) diagnosis and prediction of prognosis are hindered by subjective pathological evaluation, which may cause misdiagnosis and under-/over-treatment. Computational pathology (CPATH) can identify clinical outcome predictors, offering an objective approach to improve prognosis. However, a systematic review of CPATH in BC literature is lacking. Therefore, we present a comprehensive overview of studies that used CPATH in BC, analyzing 33 out of 2285 identified studies. Most studies analyzed regions of interest to distinguish normal versus tumor tissue and identify tumor grade/stage and tissue types (e.g., urothelium, stroma, and muscle). The cell’s nuclear area, shape irregularity, and roundness were the most promising markers to predict recurrence and survival based on selected regions of interest, with >80% accuracy. CPATH identified molecular subtypes by detecting features, e.g., papillary structures, hyperchromatic, and pleomorphic nuclei. Combining clinicopathological and image-derived features improved recurrence and survival prediction. However, due to the lack of outcome interpretability and independent test datasets, robustness and clinical applicability could not be ensured. The current literature demonstrates that CPATH holds the potential to improve BC diagnosis and prediction of prognosis. However, more robust, interpretable, accurate models and larger datasets—representative of clinical scenarios—are needed to address artificial intelligence’s reliability, robustness, and black box challenge.