Gliomas: Histogram Analysis of Apparent Diffusion Coefficient Maps with Standard- or High-b-Value Diffusion-weighted MR Imaging-Correlation with Tumor Grade

Purpose: To explore the role of histogram analysis of apparent diffusion coefficient (ADC) maps based on entire tumor volume data in determining glioma grade and to evaluate the diagnostic performance of ADC maps at standard (1000 sec/mm(2)) and high (3000 sec/mm(2)) b values. Materials and Methods: This retrospective study was approved by the institutional review board, and informed consent was waived. Twenty-seven patients with astrocytic tumors underwent diffusion-weighted magnetic resonance imaging with b values of 1000 and 3000 sec/mm(2), and the corresponding ADC maps were calculated (ADC(1000) and ADC(3000), respectively). Regions of interest containing the lesion were drawn on every section of the ADC map containing the tumor and were summated to derive volume-based data of the entire tumor. Histogram parameters were correlated with tumor grade by using repeated measurements analysis of variance, the Tukey-Kramer test for post hoc comparisons, and an unpaired Student t test. Receiver operating characteristic (ROC) curves were constructed to determine the optimum threshold for each histogram parameter, and sensitivity and specificity were assessed. Results: Minimum ADC(1000) and ADC(3000) both decreased with increasing tumor grade. The 50th and 75th percentiles of cumulative ADC(1000) histograms showed significant differences between grades (P = .015 and .001, respectively), while the fifth and 75th percentiles of cumulative ADC(3000) histograms showed such differences (P = .015 and .014, respectively). Minimum ADC and the fifth percentile for both ADC(1000) (P < .001 and P = .024, respectively) and ADC(3000) (P < .001 and P = .001, respectively) proved to lie significant histogram parameters for differentiating high-from low-grade gliomas. The diagnostic value of the parameters derived from ADC(1000) and ADC(3000) were compared, and a significant difference (0.202, P = .014) was found between the areas under the ROC curve of the fifth percentiles for ADC(1000) and ADC(3000). Conclusion: Histogram analysis of ADC maps based on entire tumor volume can be a useful tool for grading gliomas. The fifth percentile of the cumulative ADC histogram obtained at a high b value was the most promising parameter for differentiating high- from low-grade gliomas. (C) RSNA, 2011