Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer.
Journal:
BMC medical imaging
Published Date:
Jul 28, 2025
Abstract
BACKGROUND: Recent advances in histology scanning technology and Artificial Intelligence (AI) offer great opportunities to support cancer diagnosis. The inability to interpret the extracted features and model predictions is one of the major issues limiting the acceptance of AI models in clinical practice, and a clear representation of the relevance of the extracted features and model predictions is lacking. Focusing on the problem of prostate cancer (PCa) diagnosis and grading, this study aims to detect which are the most discriminant features for distinguishing malignant from non-malignant tissue and Gleason patterns, leaving the evaluation of models' classification performances as a secondary goal.
