Journal: NPJ digital medicine
This study develops a machine learning–based pipeline to predict prostate cancer prognosis pre‑surgically using biopsy specimens, with a focus on generalizability and clinical usability.
Key points:
- The model uses feature extraction from whole-mount prostate histopathology and incorporates a clinically informed intermediate reasoning step, rather than relying on a black-box end-to-end approach.
- It is designed to work efficiently with limited data and to handle dual-domain shifts:
- Differences between specimen types (e.g., biopsy vs. whole-mount tissue)
- Differences across institutions.
- Trained and tested on data from multiple centers, the pipeline showed consistent performance in external validation, suggesting robust generalizability.
- Compared with the Gleason grading system, the approach demonstrated stronger robustness for prognostic assessment.
- The framework emphasizes:
- Equitability (aiming to perform fairly across different settings),
- Interpretability (making the reasoning process more transparent),
- Practical clinical applicability, especially in resource-limited or real-world environments where large, uniform datasets are not available.
Overall, the work presents a data-efficient, generalizable, and interpretable AI tool to support pre-surgical prognosis and treatment planning in prostate cancer, complementing and potentially improving upon conventional Gleason-based assessment.