NIMG-73. PREDICTING PERITUMORAL GLIOBLASTOMA INFILTRATION USING QUANTITATIVE MAGNETIC RESONANCE FINGERPRINTING RADIOMICS

Abstract

Abstract PURPOSE Standard clinical magnetic resonance (MR) imaging of glioblastoma (GB) cannot identify malignant infiltration into the peritumoral non-enhancing region. This work implements quantitative MR fingerprinting (MRF) based radiomics to predict infiltrative regions of GB. METHODS Pre-operative MRF T1 and T2 maps along with multiparametric MR (mpMR) images (T1w, T2w, T1w-Gd, FLAIR, and ADC) from GB patients (n = 10) were analyzed. All subjects had histologically confirmed sites of non-enhancing peritumoral infiltration as identified by intra-operative 5-ALA fluorescence-guided tissue resection. These locations (n = 40) were manually annotated on pre-operative FLAIR by a board-certified neuroradiologist and labeled as “infiltration” (INF). For each patient, another FLAIR region ( > 3 cm from the enhancing tumor margin) was identified and labeled as “edema” (ED). Following image co-registration, 693 handcrafted radiomic voxel-based features were extracted from confirmed infiltration (INF) and edema (ED) voxels using a 3D 5x5x5 voxel sliding kernel. Feature selection was performed using the minimum redundancy maximal relevance (MRMR) algorithm, then INF and ED voxel features were used to train two cross-validated binary support vector machine (SVM) models for voxel-wise infiltration prediction: 1) MRF only model and 2) combined MRF and mpMRI model. Following MRMR selection, the combined model included features from T1, T1w, T2w, T1w-Gd, FLAIR, and ADC images. Model performance was evaluated using one withheld subject with multiple known infiltration sites. Balanced test accuracy and receiver operating characteristic (ROC) curve analysis was used to evaluate classification performance. RESULTS The MRF only and combined model achieved balanced test accuracies of 77.87% and 73.21% and AUCs of 0.91 and 0.97, respectively. CONCLUSIONS This study demonstrates that MRF-based radiomic features can predict GB tumor infiltration with high accuracy and provide complementary value to standard clinical MRI features. Our results indicate the high potential of employing MRF to guide personalized GB treatment strategies.