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

Farbod Khoraminia*, Saul Fuster, Neel Kanwal, Mitchell Olislagers, Kjersti Engan, Geert J.L.H. van Leenders, Andrew P. Stubbs, Farhan Akram, Tahlita C.M. Zuiverloon*

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

2 Citations (Scopus)
72 Downloads (Pure)


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.

Original languageEnglish
Article number4518
Issue number18
Publication statusPublished - 12 Sept 2023

Bibliographical note

Funding Information:
This work has received funding from the European Union’s Horizon 2020 Programme for Research and Innovation under the Marie Skłodowska Curie grant agreement No. 860627 (CLARIFY).

Publisher Copyright:
© 2023 by the authors.


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