Proceedings Article | 13 March 2019
KEYWORDS: Tumors, Magnetic resonance imaging, CRTs, Feature extraction, Brain, Cancer, Receivers, Animal model studies, Medical research, Lung cancer
Glioblastoma (GBM) is a highly aggressive brain tumor with a median survival of 15 months. Unfortunately, chemo-radiation therapy (CRT), the standard-of-care treatment for GBM, fails in over 40% of the patients within 6 months of treatment, likely on account of the highly infiltrative and heterogeneous nature of the disease. Consequently, there is a need to differentiate patients who might be at high-risk of poor outcome due to treatment failure from those who may respond favorably to CRT treatment. In this work, we analyzed the lesion heterogeneity on clinical multi-parametric MRI (MP-MRI) by interrogating radiomic features from the lesion habitat" (comprising enhancing tumor, necrotic core, T2/FLAIR hyperintensities), to determine if we could non-invasively stratify patients into low-risk and high-risk categories based on their progression-free-survival (PFS). We employed a total of 124 pre-treatment MP-MRI scans (Gadolinium (Gd)-enhanced T1w, T2w, FLAIR) and dichotomized high-risk from low-risk patients using the median PFS information. Of the 124 scans, 90 studies were used for training and 34 studies were used for independent validation. For each MRI scan, necrotic core, enhancing tumor, and edematous sub-compartments were annotated by an expert. Thereafter, a total of 1008 radiomic descriptors (e.g. Haralick, Laws energy, CoLlAGe) were extracted from every sub-compartment across all three MRI protocols (Gd-T1w, T2w, FLAIR). The top 5 most discriminatory radiomic features (p _ 0.05) were selected from the training cohort using a one way analysis of variance (ANOVA) test after removing multi-collinearity for each sub-compartment across all three MRI protocols. A linear discriminant analysis (LDA) classifier was employed individually for each sub-compartment, using the top 5 features selected from the training cohort. These features were then used to differentiate between high-risk and low-risk groups in the independent validation set. We further concatenated the top 5 radiomic features from each sub-compartment to evaluate the combined impact of the tumor habitat in predicting patient outcome. The best accuracy of 72.2% on the training cohort, and 73.3% on the independent validation cohort was obtained from the enhancing tumor sub-compartment, in distinguishing the low-risk from the high-risk group. Interestingly, when top features from the 3 sub-compartments were combined together for risk-stratification, an accuracy of 82.3% was obtained on the independent validation cohort (N=34). Our preliminary results seem to suggest that radiomic features from the tumor habitat might be reflective of tumor heterogeneity and could potentially differentiate high-risk from low-risk groups in predicting patient's response to treatment.
