Whole-mount histopathology as spatial ground truth for artificial intelligence in prostate cancer: a structured narrative review of techniques, models, and translational gaps.

backgroundProstate cancer is spatially heterogeneous, multifocal, and architecturally varied. Conventional biopsy and step-sectioned histopathology fragment the gland. This limits accurate assessment of dominant-lesion identity, tumor volume, multifocality, extraprostatic extension, seminal vesicle invasion, and surgical margin status. Whole-mount histopathology (WMH) preserves the prostate as a continuous cross-sectional unit and is increasingly used as the reference standard for radiology-pathology correlation and for validating artificial-intelligence (AI) models.

objectiveThis structured narrative review synthesizes evidence on the joint use of WMH and AI in prostate cancer pathology. For each clinical question, we examine what spatial information WMH preserves, what AI methods extract from that information, where the joint approach has been validated, and where validation gaps remain. The questions are index-lesion identification, tumor-volume estimation, multifocality, extraprostatic extension, seminal vesicle invasion, surgical margin assessment, Gleason grading, magnetic resonance imaging (MRI)-pathology registration, and surgical quality assurance.

findingsWMH improves index-lesion identification, volumetric reproducibility, and margin clarity, and provides a high-fidelity reference standard for training prostate-AI models. Registered to the patient's presurgical MRI, these whole-mount labels have also been used to train preoperative tools and have been studied in research cohorts for focal-therapy margin definition and extraprostatic-extension prediction, extending the value of WMH beyond retrospective staging. AI systems built on WMH show improved performance for tumor detection and Gleason classification. Most reported results, however, lack external validation, reporting against established medical-AI standards, and demonstrated robustness across scanners, stains, and patient populations. WMH is itself an imperfect ground truth, subject to orientation, registration, and interobserver variability that AI models inherit. IMPLICATIONS: WMH-grounded AI is a credible translational route, but routine adoption requires standardized large-format scanning, shared annotation protocols, external validation, and regulatory clearance for autonomous use. We identify field priorities, including cross-institutional WMH datasets, domain-shift mitigation, bias auditing, and reimbursement parity with AI-augmented radiology.