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PURPOSE Glioblastoma managed with maximal resection and adjuvant chemoradiation has large heterogeneity at the time of tumor recurrence (progression-free survival, PFS). Upfront identification of patients with shorter than median PFS, may facilitate better personalization of treatment, such as patient stratification into clinical trials for treatment intensification and/or supportive care. The aim of the current study was to build a radiomics model that can reliably predict PFS in patients with glioblastoma at the time of initial diagnosis. EXPERIMENTAL DESIGN: A total of 66 de novo glioblastoma patients in a single institution who underwent gross total resection followed by standard adjuvant chemoradiation, were found to have pathologically confirmed recurrence. Patients who had sufficient follow-up MRI to reliably determine PFS and available pre-surgical multi-parametric MRI (mpMRI) (T1, T1Gd, T2, T2-FLAIR, DTI, DSC-MR images) were included. The patients were stratified into two classes of “short PFS (≤7 months, n=35)” and “long PFS (≥9 months, n=19)”. An extensive set of features was extracted from preoperative mpMRI scans, including shape and volumetric measures computed for different tumorous regions, and characteristics derived from histograms and gray-level co-occurrence matrix (GLCM). Predictive modeling for discrimination of patients with short from those with long PFS was performed using a 10-fold cross-validated support vector machine classifier. RESULTS An accuracy of 79.6% was achieved for prediction of short and long PFS (sensitivity=83%, specificity=74%, AUC=0.86 [95%CI: 0.76–0.96]; short-PSF was considered the positive class). The patients with shorter PFS showed markedly higher cell density in the nonenhancing tumor (DTI), higher neo-angiogenesis in the enhancing tumor (DSC-MRI, T1Gd), lower water concentration in the enhancing tumor region (T2), and higher maximum enhancement (calculated by the subtraction of T1 from T1Gd) in the edema region. CONCLUSION Quantitative assessment of preoperative mpMRI provides accurate prediction of PFS for patients with glioblastoma, which can aid in personalized treatment planning.

neuro-oncology

NIMG-35. QUANTITATIVE ESTIMATION OF PROGRESSION-FREE SURVIVAL BASED ON RADIOMICS ANALYSIS OF PREOPERATIVE MULTI-PARAMETRIC MRI IN PATIENTS WITH GLIOBLASTOMA