A Radiogenomic Machine Learning Model for Glioblastoma Post-Resection Overall Survival Group Prediction

Abstract

Novel methods are needed to better predict outcomes and tailor personalized therapies for patients with glioblastoma (GBM). To improve post-resection survival prediction in GBM patients, we developed a machine learning model that utilized 1) radiomic computational biomarkers from pre-resection multi-parametric MRI (mp-MRI) images, and 2) clinical information including tumor molecular features and extent of surgical resection. A cohort of 406 GBM patients treated with surgical resection was studied. Each patient received a pre-resection mp-MRI that included T1, contrast-enhanced T1 (T1-ce), T2, and FLAIR sequences. Three tumor subregions, i.e., enhanced tumor, tumor core, and whole tumor, were segmented with radiologists' correction. Based on survival outcomes in the literature, the cohort was categorized into three survival groups: group A (<9 mos, n = 148), B (9-21 mos, n = 181), and C (21+ mos, n = 77). We first extracted 88 radiomic features from each tumor subregion on each MR volume, and Z-score normalization was adopted. Three other patient-specific factors, including age, resection status (subtotal versus gross total), and IDH1 status, were concatenated with the radiomic features as a synthesized patient-specific feature vector. We then designed a two-step machine learning model: using the patient-specific feature vectors, the model 1) identified patients in group A with the shortest predicted survival using a balanced random forest (BRF) classifier, and 2) used a 2nd BRF classifier to segregate the remaining patients into groups B and C. For model training, a 7:3 training/test sample ratio was adopted, and 100 model versions were acquired through random validation sample assignments to study model robustness. Sensitivity, specificity, and accuracy results of each group were calculated, and an overall ROC was generated to represent the model's overall performance. The model demonstrated acceptable prediction performance with an ROC AUC value at 0.68. Individually, the model achieved good prediction accuracies for short and long-term survival prediction (Groups A and C), though we observed relatively limited accuracy in medium survival prediction (Group B). Model sensitivity in group A was promising, but was limited in the remaining two groups. Feature weight analysis showed that radiomic features from the enhanced tumor subregion were the leading variables in the BRF classifier. The developed radiogenomic machine learning model predicts GBM post-resection overall survival outcomes. Future work is necessary to further improve model sensitivity for patients with medium and long-term predicted survival.