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

• URL: http://arxiv.org/abs/2512.05824v1
• Date: Fri, 05 Dec 2025 15:43:02 GMT
• Title: Multimodal Oncology Agent for IDH1 Mutation Prediction in Low-Grade Glioma
• Abstract summary: 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.<n>Results show MOA captures complementary mutation-relevant information enriched through external biomedical sources, enabling accurate IDH1 mutation prediction.
• Score: 0.5499435453529261
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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