Histogram Analysis Based on DTI and NODDI for Differentiating Atypical High-Grade Glioma from Primary Central Nervous System Lymphoma

Shanshan Zhao^1,2Xiaoyue Ma^1,2Linlin Li³Eryuan Gao^1,2Kai Zhao^1,2Mengzhu Wang⁴Guang Yang⁵Hongbin Zheng^6*Jingliang Cheng^1,2*Guohua Zhao^1,2*

• ¹Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
• ²Tianjian Laboratory of Advanced Biomedical Sciences, Zhengzhou, China
• ³Dengfeng Hospital of Traditional Chinese Medicine, Dengfeng, China
• ⁴MR Research Collaboration, Siemens Healthineers Ltd.,, Beijing, China
• ⁵Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
• ⁶The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China

Background and Purpose: Distinguishing between high-grade glioma (HGG) and primary central nervous system lymphoma (PCNSL) is of paramount clinical importance, as these entities necessitate substantially different therapeutic approaches. The differential diagnosis becomes particularly challenging when HGG presents without characteristic magnetic resonance imaging (MRI) features, making it difficult to differentiate from PCNSL. The diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) offer quantitative assessments of water molecule diffusion within tissues, thereby providing potential means to characterize microstructural differences between HGG and PCNSL. This study aims to evaluate the diagnostic efficacy of histogram analysis based on DTI and NODDI parameters in differentiating atypical HGG from PCNSL. Materials and Methods: We retrospectively reviewed patients who underwent multib-value diffusion-weighted imaging (DWI) at our institution. The multi-b-value DWI was performed using a single-shot echo-planar imaging (EPI) sequence with six bvalues (0, 500, 1000, 1500, 2000, and 2500 s/mm²) distributed across 30 directions. The DTI and NODDI model were employed to derive the parametric maps of apparent diffusion coefficient (ADC), fractional anisotropy (FA), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index (ODI). Two regions of interest (ROIs) were manually delineated within the enhancing tumor area and the peritumoral edema. Histogram features were extracted from these ROIs.Comparisons between HGG and PCNSL were performed. Receiver operating characteristic (ROC) curves were drawn, and the area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. P < 0.05 was considered statistically significant.Results: A total of 55 patients (30 with atypical HGG and 25 with PCNSL), were included in this study. Several histogram features of parameters could be used to classify the HGG and PCNSL (P < 0.05). The 75th percentile of the orientation dispersion index (ODI75th) within the enhancing tumor region demonstrated the highest diagnostic performance (AUC = 0.985). At an optimal threshold of 0.604, ODI75th yielded a sensitivity of 96%, a specificity of 93.33%, and an accuracy of 94.55% for distinguishing HGG from PCNSL.differentiate between atypical HGG and PCNSL. ODI75th within the enhancing tumor region showed the most favorable diagnostic performance.