Key points are not available for this paper at this time.
The complexity and variability of high-resolution pathological images present significant challenges in computational pathology. While AI-driven pathology foundation models have advanced the field, they require large-scale datasets, substantial storage, and significant computational resources, as well as rigorous validation for clinical applicability. We present PathOrchestra, a versatile pathology foundation model trained on 287,424 slides from 21 tissue types across three centers. Evaluated on 112 tasks from 61 private and 51 public datasets, covering digital slide preprocessing, pan-cancer classification, lesion identification, multi-cancer subtype classification, biomarker assessment, gene expression prediction, and structured report generation. Across 27,755 whole slide images and 9,415,729 region-of-interest images, it achieved over 0.950 accuracy in 47 tasks, including pan-cancer classification, lymphoma subtyping, and bladder cancer screening. It is the first to generate structured reports for colorectal cancer and lymphoma. Overall, PathOrchestra demonstrates the clinical readiness of large-scale self-supervised pathology foundation models, achieving high accuracy and offering potential to digital medicine integration.
Yan et al. (Wed,) studied this question.