FoundBioNet: A Foundation-Based Model for IDH Genotyping of Glioma from Multi-Parametric MRI

TL;DR

FoundBioNet is a foundation-based deep learning model that noninvasively predicts IDH mutation status in glioma patients from multi-parametric MRI, outperforming traditional methods and enhancing personalized treatment planning.

Contribution

The paper introduces FoundBioNet, a novel foundation model with tumor-aware feature encoding and cross-modality differential modules for accurate IDH genotyping from MRI.

Findings

Achieved high AUCs across multiple independent datasets.

Outperformed baseline models significantly (p <= 0.05).

Both proposed modules are essential for optimal performance.

Abstract

Accurate, noninvasive detection of isocitrate dehydrogenase (IDH) mutation is essential for effective glioma management. Traditional methods rely on invasive tissue sampling, which may fail to capture a tumor's spatial heterogeneity. While deep learning models have shown promise in molecular profiling, their performance is often limited by scarce annotated data. In contrast, foundation deep learning models offer a more generalizable approach for glioma imaging biomarkers. We propose a Foundation-based Biomarker Network (FoundBioNet) that utilizes a SWIN-UNETR-based architecture to noninvasively predict IDH mutation status from multi-parametric MRI. Two key modules are incorporated: Tumor-Aware Feature Encoding (TAFE) for extracting multi-scale, tumor-focused features, and Cross-Modality Differential (CMD) for highlighting subtle T2-FLAIR mismatch signals associated with IDH mutation. The model was trained and validated on a diverse, multi-center cohort of 1705 glioma patients from six public datasets. Our model achieved AUCs of 90.58%, 88.08%, 65.41%, and 80.31% on independent test sets from EGD, TCGA, Ivy GAP, RHUH, and UPenn, consistently outperforming baseline approaches (p <= 0.05). Ablation studies confirmed that both the TAFE and CMD modules are essential for improving predictive accuracy. By integrating large-scale pretraining and task-specific fine-tuning, FoundBioNet enables generalizable glioma characterization. This approach enhances diagnostic accuracy and interpretability, with the potential to enable more personalized patient care.