Objective characterization of displacement of the frontal aslant tract in low-grade glioma: a quantitative tractography study

We investigated whether quantitative tractography-based spatial metrics can objectively characterize visual tumor-induced white matter (WM) tract displacement in low-grade glioma. Specifically, the frontal aslant tract (FAT) was evaluated using the weighted Dice similarity coefficient (wDSC) and Hausdorff distance (HD) as spatial metrics. We retrospectively analyzed 45 patients with IDH-mutated frontal low-grade glioma (LGG), in whom preoperative diffusion-weighted MRI and probabilistic tractography of the FAT were performed. Patients were classified into one of three spatial WM tract alteration pattern groups (displacement, infiltration, combination) based on expert visual assessment. The wDSC and HD were investigated in both MNI-152 normalized and native anatomical space. In normalized space, inter-hemispheric comparisons (within patients) and intra-hemispheric comparisons (across patients) of spatial metrics were performed. In native space, comparisons were performed within patients, between mirrored ipsilesional and contralesional tracts. In normalized MNI space, FAT displacement led to a decreased wDSC and increased HD during inter-hemispheric comparisons within patients, while infiltrated tracts showed no such differences. Conversely, intra-hemispheric spatial metrics, assessed across patients and independently of contralateral tract anatomy, could not differentiate FAT displacement from infiltration in MNI space. In native space, displacement of the FAT could be characterized objectively, using tract mirroring. Visual tumor-induced displacement of the FAT can be objectively confirmed with measurable geometric tract alterations using spatial metrics. While displacement could be characterized through inter-hemispheric comparisons in MNI space, native space might be more robust for detecting intra-hemispheric geometric differences. This study provides the foundation for an objective, quantitative framework to evaluate spatial WM tract alterations in glioma. These results might advance non-invasive radiomics-based tumor subtype predictions.