Isocitrate dehydrogenase (IDH) is a pivotal molecular marker for glioma diagnosis, prognosis, and treatment planning. Multi-modal deep learning methods, which integrate features from multiple magnetic resonance imaging (MRI) sequences, have become a powerful solution for non-invasive IDH genotyping. However, existing methods still have limitations in feature extraction and fusion, which constrains their robustness. In this work, we propose a novel segmentation-guided feature alignment and fusion network (SFAF-Net) for glioma IDH genotyping, with three key innovations: 1) The Segmentation-guided Feature Alignment (SFA) module leverages tumor segmentation supervision to facilitate cross-modal feature alignment; 2) The Redundancy-Attenuated Fusion (RAF) module implements similarity-based selective fusion of modality pairs to reduce feature redundancy; 3) A randomized modality dropout mechanism within RAF enhances model robustness against input variations. Comprehensive experiments conducted on public and private datasets demonstrate that SFAF-Net outperforms state-of-the-art methods across diverse MRI sequences. Moreover, SFAF-Net supports an arbitrary number of input sequences, enabling flexible adaptation to diverse clinical scanning protocols in personalized diagnosis.
Chen et al. (Thu,) studied this question.