The article introduces TriPath, a deep-learning platform designed for the analysis of three-dimensional (3D) pathology samples to predict clinical outcomes, specifically patient prognostication for prostate cancer recurrence. Researchers argue that traditional two-dimensional (2D) pathology is limited by sampling bias and insufficient representation of intrinsically 3D tissue, while TriPath leverages volumetric imaging from modalities like open-top light-sheet microscopy (OTLS) and microcomputed tomography (microCT) to capture comprehensive morphology. Key findings indicate that 3D volume-based prognostication significantly outperforms 2D slice-based approaches and established pathologist baselines, demonstrating the clinical potential of AI-driven 3D pathology for better risk stratification. The platform uses a weakly supervised learning approach, requiring only patient-level outcome labels rather than extensive manual annotations. The study suggests that analyzing a larger tissue volume effectively mitigates sampling bias and accounts for tissue heterogeneity, improving prediction reliability.

References:

• Song A H, Williams M, Williamson D F K, et al. Analysis of 3D pathology samples using weakly supervised AI[J]. Cell, 2024, 187(10): 2502-2520. e17.