Multimodal Oncology Agent for IDH1 Mutation Prediction in Low-Grade Glioma

TL;DR

This paper presents a multimodal AI agent that combines histology, clinical, and genomic data with external biomedical sources to accurately predict IDH1 mutations in low-grade glioma, outperforming existing methods.

Contribution

Introduces a novel multimodal oncology agent integrating histology, clinical, and genomic reasoning with external biomedical sources for improved mutation prediction.

Findings

F1-score of 0.826 without histology tool surpasses clinical baseline.

F1-score of 0.912 with combined modalities exceeds baselines.

External biomedical sources enrich mutation-relevant information for prediction.

Abstract

Low-grade gliomas frequently present IDH1 mutations that define clinically distinct subgroups with specific prognostic and therapeutic implications. This work introduces a Multimodal Oncology Agent (MOA) integrating a histology tool based on the TITAN foundation model for IDH1 mutation prediction in low-grade glioma, combined with reasoning over structured clinical and genomic inputs through PubMed, Google Search, and OncoKB. MOA reports were quantitatively evaluated on 488 patients from the TCGA-LGG cohort against clinical and histology baselines. MOA without the histology tool outperformed the clinical baseline, achieving an F1-score of 0.826 compared to 0.798. When fused with histology features, MOA reached the highest performance with an F1-score of 0.912, exceeding both the histology baseline at 0.894 and the fused histology-clinical baseline at 0.897. These results demonstrate that the proposed agent captures complementary mutation-relevant information enriched through external biomedical sources, enabling accurate IDH1 mutation prediction.