Journal: Histology and histopathology
This publication reviews how artificial intelligence is reshaping prostate cancer diagnosis, risk assessment, and management, with a focus on digital pathology and multimodal models.
Key points:
- Screening and risk stratification: AI-based risk calculators are improving prostate cancer detection while helping to reduce unnecessary biopsies.
- Pathology and grading: Deep learning—especially convolutional neural networks—can detect malignancy and assign Gleason grades with accuracy comparable to expert pathologists. These tools can also:
- Triage or flag challenging cases
- Quantify prognostic markers such as Ki-67 and cribriform patterns
- Prediction of biology and spread: Models can infer molecular alterations, microsatellite instability, and lymph node metastases directly from routine histology slides, offering a lower-cost alternative to some molecular assays.
- Multimodal and clinical decision support: Integrating digital pathology with clinical variables enables more individualized risk prediction and treatment recommendations.
- Natural language processing (NLP) and large language models: These methods can mine clinical notes for structured information and support patient education and communication.
- Current limitations:
- Most evidence is from retrospective studies with heterogeneous endpoints
- Marked performance drop at external sites due to differences in populations and slide preparation
- Limited access to large, well-annotated datasets
- Technical and preanalytical variability that undermines reproducibility
- Requirements for clinical adoption:
- Prospective, multicenter validation
- Standardization of preanalytical and analytical workflows
- Transparent reporting of model failure modes and clear human oversight
- Future directions: Approaches such as self-supervised pretraining, transformer-based image models, and integrated language–vision systems are anticipated to improve generalizability and enable more personalized prostate cancer care.