Pancancer outcome prediction via a unified weakly supervised deep learning model.

ABSTRACT: Accurate prognosis prediction is essential for guiding cancer treatment and improving patient outcomes. While recent studies have demonstrated the potential of histopathological images in survival analysis, existing models are typically developed in a cancer-specific manner, lack extensive external validation, and often rely on molecular data that are not routinely available in clinical practice. To address these limitations, we present PROGPATH, a unified model capable of integrating histopathological image features with routinely collected clinical variables to achieve pancancer prognosis prediction. PROGPATH employs a weakly supervised deep learning architecture built upon the foundation model for image encoding. Morphological features are aggregated through an attention-guided multiple

SUBMITTER: Yuan W 

PROVIDER: S-EPMC12405520 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature