融合病理与多源医学数据，提升低级别胶质瘤IDH1突变预测准确率
Multimodal Oncology Agent for IDH1 Mutation Prediction in Low-Grade Glioma
- 整合病理图像与临床基因数据，通过外部医学知识增强推理
- 融合多模态信息后F1得分达0.912，优于单一模态基线
- 适合肿瘤精准医疗、病理智能辅助等场景使用
低级别胶质瘤常伴有IDH1突变，定义了具有特定预后和治疗意义的亚型。本文提出多模态肿瘤智能体（MOA），基于TITAN基础模型的病理分析工具，结合通过PubMed、Google搜索和OncoKB进行推理的结构化临床与基因组输入，实现对低级别胶质瘤中IDH1突变的预测。在TCGA-LGG队列488例患者上对MOA报告进行量化评估，结果表明：不含病理工具的MOA已优于临床基线（F1=0.826 vs 0.798）；当融合病理特征后，性能达到最优（F1=0.912），超越病理基线（0.894）和病理-临床融合基线（0.897）。结果表明，该智能体通过外部生物医学资源充分捕捉互补的突变相关特征，实现高精度的IDH1突变预测。
原文摘要 · Abstract (English)
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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