Multiparametric MRI for differentiation of radiation necrosis from recurrent tumor in patients with treated glioblastoma

OBJECTIVE. Differentiation of radiation necrosis (RN) from recurrent tumor (RT) in treated patients with glioblastoma remains a diagnostic challenge. The purpose of this study is to evaluate the diagnostic performance of multiparametric MRI in distinguishing RN from RT in patients with glioblastoma, with the use of a combination of MR perfusion and diffusion parameters. MATERIALS AND METHODS. Patients with glioblastoma who had a new enhancing mass develop after completing standard treatment were retrospectively evaluated. Apparent diffusion coefficient (ADC), volume transfer constant (K^trans), and relative cerebral blood volume (rCBV) values were calculated from the MR images on which the enhancing lesions first appeared. Repeated measure of analysis, logistic regression, and ROC analysis were performed. RESULTS. Of a total of 70 patients evaluated, 46 (34 with RT and 12 with RN) met our inclusion criteria. Patients with RT had significantly higher mean rCBV (p < 0.001) and K^trans (p = 0.006) values and lower ADC values (p = 0.004), compared with patients with RN. The overall diagnostic accuracy was 85.8% for rCBV, 75.5% for K^trans, and 71.3% for ADC values. The logistic regression model showed a significant contribution of rCBV (p = 0.024) and K^trans (p = 0.040) as independent imaging classifiers for differentiation of RT from RN. Combined use of rCBV and K^trans at threshold values of 2.2 and 0.08 min^-1, respectively, improved the overall diagnostic accuracy to 92.8%. CONCLUSION. In patients with treated glioblastoma, rCBV outperforms ADC and K^trans as a single imaging classifier to predict recurrent tumor versus radiation necrosis; however, the combination of rCBV and K^trans may be used to improve overall diagnostic accuracy.