Diffusion Basis Spectrum Imaging (DBSI): A Multicompartment Diffusion Model Linking Regional Microstructural Heterogeneity With Proliferation and Survival in High-Grade Gliomas.
Abstract
Background
Diffusion basis spectrum imaging (DBSI) has emerged as an advanced model capable of characterizing central nervous system tissue injury and glioma pathology. However, the associations of DBSI with key characteristics of high-grade gliomas (HGGs), including proliferation and survival outcomes, remain underexplored.
Purpose
To evaluate the associations between DBSI-derived histogram features with Ki-67 level and overall survival (OS) in HGGs. STUDY TYPE Retrospective. SUBJECTS Ninety-one patients (36 women, 53.41 ± 12.62 years) with HGGs. FIELD/SEQUENCE 3-T, spin-echo echo-planar imaging (SE-EPI) sequence with q-space Cartesian grid sampling. ASSESSMENT DBSI was used to estimate restricted fraction, hindered fraction, water fraction, anisotropic fraction, and mean isotropic apparent diffusion coefficient (iso ADC). Histogram features were extracted from both the contrast-enhancing regions (CER) and non-enhancing T2-weighted imaging (T2WI)/fluid attenuated inversion recovery (FLAIR) hyperintense regions (NEHR). Patients were stratified by Ki-67 labeling index (LI), and OS was defined as the time from surgery to death or last follow-up. STATISTICAL TESTS Receiver operating characteristic (ROC) curve analysis and Cox regression were applied to evaluate features in relation to Ki-67 LI and OS, respectively. p < 0.05 was considered significant.
Results
In HGGs, CER showed higher restricted and anisotropic fractions, whereas NEHR showed a higher hindered fraction, water fraction, and iso ADC. For Ki-67 assessment, the robust mean absolute deviation (RMAD) of restricted fraction in CER was the best single feature (AUC = 0.714), and the combination of DBSI features improved the AUC to 0.791. Furthermore, DBSI features from NEHR, together with multifocality, were significant factors associated with OS. DATA
Conclusion
DBSI-derived histogram analysis provides a noninvasive quantitative characterization of HGG heterogeneity. Proliferative activity may be primarily reflected within CER, while survival outcomes may be related to DBSI features in NEHR. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 3.