A. Becker1, V. K. Singh2,3, B. Mailhe3, D. J. DiCostanzo4, J. Chan5, M. Siebert3, J. Haque1, B. Lou6, N. Gupta4,7, A. Kamen3, R. Machiraju8, D. Comaniciu3, and A. Chakravarti9; 1Department of Radiation Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, 2Siemens Medical Solutions Inc USA, Princeton, NJ, 3Siemens Healthineers, Princeton, NJ, 4The Ohio State University, Columbus, OH, 5Ohio State University, Columbus, OH, 6Siemens Healthineers, Malvern, PA, 7The Ohio State University Wexner Medical Center, Department of Radiation Oncology, Columbus, OH, 8College of Engineering, The Ohio State University, Columbus, OH, 9Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH
Purpose/Objective(s):Current prognostication for glioblastoma (GBM) relies on clinical and molecular features, but still does not incorporate data from the radiation treatment plan. More innovative approaches to GBM management are imperative, as the overall survival (OS) remains less than 2 years. We successfully reported on improved personalization of therapy leveraging radiomics in lung cancer. Therefore, we hypothesize that features extracted from CT imaging (CT-radiomic) and 3D dose distribution (dosiomic) will improve prediction of patient outcome and may enable RT dose modulation for GBM. To test this, we sought to determine the predictive potential of these features using publicly available data.Materials/
Methods: We used data (clinical, molecular, post-surgical CT imaging, and RT planned dose distributions) from 180 patients with primary GBM from the Burdenko Glioblastoma Progression Dataset to extract and select CT-radiomic and dosiomic features from the treatment plan using an open source software. Feature extraction was done using clinical target volume (CTV) masks. Planning target volume (PTV) masks were used when CTV was unavailable. We identified outliers as samples with values out of the range of median +/- median absolute deviation, and removed features with more than 15% outliers, then used univariate hazard ratios (HR) to select the top-K features. Multivariable analyses were performed with Cox Proportional Hazards (PH). OS was calculated with log rank, plotted as Kaplan-Meier curves using median-dichotomized values for continuous variables, and compared with concordance index (CI). The significance level adopted for this pilot study was p</span>=0.01. Results: Ninety-two of the 180 patientswithcomplete MGMT status and dose data were selected for this study. In this cohort, the OS was not significantly associated with age groups (median: 58 years, p=0.07), sex (p=0.63), early RT start (median: 30 days, p=0.02),MGMT status (p=0.76), and IDH status (p=0.2). In a Cox PH model accounting for age, MGMT status, and time for RT start, survival difference was statistically significant (p=0.01, log-rank=9.1, CI=0.65). Overall, 1221 radiomic and dosiomic features were computed at 5 different scales. We selected the top-10 CT-radiomics and the top-10 dosiomic featureswith HR>=1.03 and HR>=1.08, respectively. Adding CT-radiomic and dosiomic features to clinic-molecular information (age, MGMT status) significantly increased the OS stratification (p<0.01, log-rank=10.4, CI=0.71).For comparison, Cox PH models with only CT-radiomics and only dosiomic features achieved respective CI of 0.59 (p<0.01) and 0.57 (p=0.07). Conclusion: Incorporating radiomic and dosiomic features from CT-image treatment plan appeared to significantly improve prognostication/stratification of GBM patients and will be used in further deep learningworkflowswith large institutional cohorts and correlation with doses for dose-modulation models.
