Differentiation between Primary Cerebral Lymphoma and Glioblastoma Using the Apparent Diffusion Coefficient: Comparison of Three Different ROI Methods

OBJECTIVE: Apparent diffusion coefficients (ADC) can help differentiate between central nervous system (CNS) lymphoma and Glioblastoma (GBM). However, overlap between ADCs for GBM and lymphoma have been reported because of various region of interest (ROI) methods. Our aim is to explore ROI method to provide the most reproducible results for differentiation. MATERIALS AND METHODS: We studied 25 CNS lymphomas and 62 GBMs with three ROI methods: (1) ROI(1), whole tumor volume; (2) ROI(2), multiple ROIs; and (3) ROI(3), a single ROI. Interobserver variability of two readers for each method was analyzed by intraclass correlation(ICC). ADCs were compared between GBM and lymphoma, using two-sample t-test. The discriminative ability was determined by ROC analysis. RESULTS: ADCs from ROI(1) showed most reproducible results (ICC >0.9). For ROI(1), ADC(mean) for lymphoma showed significantly lower values than GBM (p = 0.03). The optimal cut-off value was 0.98×10(−3) mm(2)/s with 85% sensitivity and 90% specificity. For ROI(2), ADC(min) for lymphoma was significantly lower than GBM (p = 0.02). The cut-off value was 0.69×10(−3) mm(2)/s with 87% sensitivity and 88% specificity. CONCLUSION: ADC values were significantly dependent on ROI method. ADCs from the whole tumor volume had the most reproducible results. ADC(mean) from the whole tumor volume may aid in differentiating between lymphoma and GBM. However, multi-modal imaging approaches are recommended than ADC alone for differentiation.