Prospective Pathology Foundation Models

Histopathology remains the gold standard for disease diagnosis, yet faces challenges including pathologist shortages and diagnostic model limitations. This underscores the critical need to develop deep learning-based pathology foundation models integrating prospective imaging and clinical data. Such models would enhance diagnostic accuracy and efficiency, enabling tumor grading, histo-molecular classification, and intelligent chemotherapy guidance - ultimately optimizing clinical workflows. However, a critical gap remains: the absence of prospectively validated, pan-disease pathology foundation models. Developing clinically validated models is therefore imperative.

Inclusion Criteria: 1. Aged 18-75 years old. 2. Patients with complete pathological slides and clinical information. Exclusion Criteria: 1.Patients with missing data or specimens not meeting quality control requirements for analysis.