A multi-representation deep-learning framework for accurate multicancer classification.

ABSTRACT:

Background

Accurate multicancer classification constitutes a cornerstone of modern oncology, offering critical insights into diagnosis, therapeutic decision-making, and prognostication. Numerous existing approaches, however, remain restricted to limited cancer types and typically encode genomic information into a single representational modality. The purpose of this study was to develop and evaluate a novel framework by integrating complementary, mutation-derived features to advance cancer classification.

Methods

We present GraphVar, a multi-representation deep learning framework that integrates mutation-derived imaging and numeric genomic features for multicancer classification. GraphVar generates a spatial variant map by encoding gene-level variant categories as pixel inte

SUBMITTER: He G 

PROVIDER: S-EPMC12628999 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature