A Simple Automated Method for Detecting Recurrence in High-Grade Glioma - Yanagihara, T.K.; Grinband, J.; Rowley, J.; Cauley, K.A.; Lee, A.; Garrett, M.; Afghan, M.; Chu, A.; Wang, T.J.C. (American Journal of Neuroradiology, 2016)
SUMMARY: Our aim was to develop an automated multiparametric MR imaging analysis of routinely acquired imaging sequences to identify areas of focally recurrent high-grade glioma. Data from 141 patients treated with radiation therapy with a diagnosis of high-grade glioma were reviewed. Strict inclusion/exclusion criteria identified a homogeneous cohort of 12 patients with a nodular recurrence of high-grade glioma that was amenable to focal re-irradiation (cohort 1). T1WI, FLAIR, and DWI data were used to create subtraction maps across time points. Linear regression was performed to identify the pattern of change in these 3 imaging sequences that best correlated with recurrence. The ability of these parameters to guide treatment decisions in individual patients was assessed in a separate cohort of 4 patients who were treated with radiosurgery for recurrent high-grade glioma (cohort 2). A leave-one-out analysis of cohort 1 revealed that automated subtraction maps consistently predicted the radiologist-identified area of recurrence (median area under the receiver operating characteristic curve = 0.91). The regression model was tested in preradiosurgery MRI in cohort 2 and identified 8 recurrent lesions. Six lesions were treated with radiosurgery and were controlled on follow-up imaging, but the remaining 2 lesions were not treated and progressed, consistent with the predictions of the model. Multiparametric subtraction maps can predict areas of nodular progression in patients with previously treated high-grade gliomas. This automated method based on routine imaging sequences is a valuable tool to be prospectively validated in subsequent studies of treatment planning and posttreatment surveillance.FSLfMRI of the Brain Software LibraryGBMglioblastomaGKRSgamma knife radiosurgeryHGGhigh-grade gliomaROCreceiver operating characteristic