TY - JOUR
T1 - Artificial Intelligence in Digital Pathology for Bladder Cancer
T2 - Hype or Hope? A Systematic Review
AU - Khoraminia, Farbod
AU - Fuster, Saul
AU - Kanwal, Neel
AU - Olislagers, Mitchell
AU - Engan, Kjersti
AU - van Leenders, Geert J.L.H.
AU - Stubbs, Andrew P.
AU - Akram, Farhan
AU - Zuiverloon, Tahlita C.M.
N1 - 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.
PY - 2023/9/12
Y1 - 2023/9/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85172810778&partnerID=8YFLogxK
U2 - 10.3390/cancers15184518
DO - 10.3390/cancers15184518
M3 - Review article
C2 - 37760487
AN - SCOPUS:85172810778
SN - 2072-6694
VL - 15
JO - Cancers
JF - Cancers
IS - 18
M1 - 4518
ER -
