NIMG-43. DIFFUSION BASIS SPECTRUM IMAGING DISTINGUISHES HIGH-GRADE GLIOMA TREATMENT EFFECT FROM TUMOR PROGRESSION

Abstract

Abstract BACKGROUND Following standard-of-care chemoradiation therapy for high-grade glioma, it is often challenging to distinguish treatment changes from true tumor progression using conventional magnetic resonance imaging (MRI). Diffusion basis spectrum imaging (DBSI) uses a data-driven multiple-tensor modeling approach to disentangle specific histologic components and structural features present within individual imaging voxels, offering an opportunity to noninvasively differentiate treatment effect from tumor progression. This represents the first study of in vivo longitudinal DBSI for brain tumors in patients. METHODS Adult patients were prospectively recruited who met the criteria of known pathological diagnosis of high-grade glioma or glioblastoma (WHO grade III-IV) and undergoing or completing standard-of-care radiation therapy with concurrent chemotherapy. DBSI and conventional MRI data were acquired longitudinally beginning with a 4-week post-radiation therapy MRI until progression as defined by standard RANO criteria or biopsy. DBSI analyses were performed with isotropic diffusion profiles defined as restricted fraction (0 ≤ D ≤ 1.0 μm2/ms) and hindered fraction (1.0 < D ≤ 1.5 μm2/ms). RESULTS Eleven patients with contrast-enhancing high-grade gliomas were enrolled between June 2019 and March 2020, with final DBSI data obtained in November 2020. Of these, eight patients had tumor progression, two had treatment effect, and one had stable disease during the study period. Within the contrast-enhancing regions, the DBSI hindered fraction map demonstrated hypointensity for all cases of tumor progression, and hyperintensity for cases of treatment effect. DBSI-based biomarkers were present 0.7 to 12.0 months (median 2.4, IQR 2.8) before definitive diagnosis of treatment effect or tumor progression using conventional methods of MRI and/or biopsy. CONCLUSIONS DBSI hindered fraction can distinguish new contrast-enhancing lesions in the setting of high-grade glioma as treatment effect or tumor progression in advance of conventional MRI. Future work will incorporate machine learning algorithms to further improve DBSI-based predictions of tumor histopathology.