Explainable artificial intelligence in prostate and bladder cancer: A review of imaging, digital pathology, molecular profiling, and clinical data.
- 1Department of Urology, Democritus University of Thrace, Alexandroupolis, Greece.
- 2Endoscopy Unit, Democritus University of Thrace, Alexandroupolis, Greece.
- 3Department of Pathology, Democritus University of Thrace, Alexandroupolis, Greece.
Abstract
Artificial intelligence (AI) models are increasingly applied across the detection, staging, treatment selection, and surveillance of prostate and bladder cancer. Explainable AI links predictions to identifiable features, spatial regions, or architectural constraints, supporting clinical audit and informed use. This narrative review synthesizes evidence across both cancers in imaging, digital pathology, molecular and liquid-biopsy data, and clinical applications and assesses deployment readiness. Two parallel structured searches of PubMed/MEDLINE were performed, one for each cancer, covering records through November 15, 2025. Eligible studies applied machine or deep learning to a clinical or translational task and included an explainability component. Seventy studies across 4 data domains were included. Imaging was the most active domain. SHapley Additive exPlanations and gradient-weighted class activation mapping confirmed biologically plausible signal localization. Interpretability-by-design in bladder endoscopy embedded explainability without post hoc approximation. Attention heatmaps and probability maps in digital pathology linked model focus to histological features relevant to grading and invasion. Molecular models ranged from post hoc attribution to pathway-constrained architectures; a biologically structured deep neural network in prostate cancer demonstrated how architectural constraints can move explanations toward mechanistic inference. Clinical and electronic medical record-based models constituted the largest group, using SHapley Additive exPlanations for prebiopsy triage, treatment selection, recurrence prediction, and active surveillance. Post hoc explanations and interpretable-by-design models carry different evidentiary weight. Prospective evidence that explainable AI affects clinical decisions is lacking. Translation requires independent validation of fidelity, stability, and usability, prospective reader studies, standardized reporting, and investment in interpretable architectures.
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BibTeX
@article{diamantidis2026explainable,
title = {{Explainable artificial intelligence in prostate and bladder cancer: A review of imaging, digital pathology, molecular profiling, and clinical data.}},
author = {Dimitrios Diamantidis and Georgios Tsakaldimis and Nikolaos Smyrlis and Chousein Chousein and Charalampos Kafalis and Konstantina Foutzitzi and Evangelia Deligeorgiou and Nikolaos Panagiotopoulos and Stavros Lailisidis and Chrysostomos Georgellis and Stavros Giannopoulos and Stilianos Giannakopoulos and Christos Kalaitzis},
journal = {Current urology},
year = {2026},
month = aug,
doi = {10.1097/cu9.0000000000000367},
url = {https://europepmc.org/article/MED/42960345},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Explainable artificial intelligence in prostate and bladder cancer: A review of imaging, digital pathology, molecular profiling, and clinical data.
AU - Dimitrios Diamantidis
AU - Georgios Tsakaldimis
AU - Nikolaos Smyrlis
AU - Chousein Chousein
AU - Charalampos Kafalis
AU - Konstantina Foutzitzi
AU - Evangelia Deligeorgiou
AU - Nikolaos Panagiotopoulos
AU - Stavros Lailisidis
AU - Chrysostomos Georgellis
AU - Stavros Giannopoulos
AU - Stilianos Giannakopoulos
AU - Christos Kalaitzis
PY - 2026
DA - 2026/08/25
JO - Current urology
DO - 10.1097/cu9.0000000000000367
UR - https://europepmc.org/article/MED/42960345
AB - Artificial intelligence (AI) models are increasingly applied across the detection, staging, treatment selection, and surveillance of prostate and bladder cancer. Explainable AI links predictions to identifiable features, spatial regions, or architectural constraints, supporting clinical audit and informed use. This narrative review synthesizes evidence across both cancers in imaging, digital pathology, molecular and liquid-biopsy data, and clinical applications and assesses deployment readiness. Two parallel structured searches of PubMed/MEDLINE were performed, one for each cancer, covering records through November 15, 2025. Eligible studies applied machine or deep learning to a clinical or translational task and included an explainability component. Seventy studies across 4 data domains were included. Imaging was the most active domain. SHapley Additive exPlanations and gradient-weighted class activation mapping confirmed biologically plausible signal localization. Interpretability-by-design in bladder endoscopy embedded explainability without post hoc approximation. Attention heatmaps and probability maps in digital pathology linked model focus to histological features relevant to grading and invasion. Molecular models ranged from post hoc attribution to pathway-constrained architectures; a biologically structured deep neural network in prostate cancer demonstrated how architectural constraints can move explanations toward mechanistic inference. Clinical and electronic medical record-based models constituted the largest group, using SHapley Additive exPlanations for prebiopsy triage, treatment selection, recurrence prediction, and active surveillance. Post hoc explanations and interpretable-by-design models carry different evidentiary weight. Prospective evidence that explainable AI affects clinical decisions is lacking. Translation requires independent validation of fidelity, stability, and usability, prospective reader studies, standardized reporting, and investment in interpretable architectures.
SN - 1661-7649
SN - 1661-7657
ER -