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    Original Research

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

    Published Online:Dec 1 2011https://doi.org/10.1148/radiol.11110686

    Abstract

    Our results suggest that histogram analysis based on apparent diffusion coefficients of entire tumor volumes can be a useful and objective diagnostic tool for grading gliomas.

    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/mm2) and high (3000 sec/mm2) 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/mm2, and the corresponding ADC maps were calculated (ADC1000 and ADC3000, 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 ADC1000 and ADC3000 both decreased with increasing tumor grade. The 50th and 75th percentiles of cumulative ADC1000 histograms showed significant differences between grades (P = .015 and .001, respectively), while the fifth and 75th percentiles of cumulative ADC3000 histograms showed such differences (P = .015 and .014, respectively). Minimum ADC and the fifth percentile for both ADC1000 (P < .001 and P = .024, respectively) and ADC3000 (P < .001 and P = .001, respectively) proved to be significant histogram parameters for differentiating high- from low-grade gliomas. The diagnostic value of the parameters derived from ADC1000 and ADC3000 were compared, and a significant difference (0.202, P = .014) was found between the areas under the ROC curve of the fifth percentiles for ADC1000 and ADC3000.

    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.

    © RSNA, 2011

    Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11110686/-/DC1

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    Article History

    Received April 3, 2011; revision requested May 5; revision received June 13; accepted June 23; final version accepted July 14.
    Published online: Dec 2011
    Published in print: Dec 2011