Brain tumor segmentation and overall survival period prediction in glioblastoma multiforme using radiomic features

Glioblastoma multiforme (GBM or glioblastoma) is a fast-growing glioma that are the most invasive type of glial tumors, rapidly growing and commonly spreading into nearby brain tissue. Due to its aggressive and fast growing nature, patients suffer from high grade glioma (GBM) survive very less time as compare to other tumors. Prediction of patient survival (OS) time helps the radiologist for better systematic treatment planning and clinical decision making. The OS rate depends on the tumor size, shape, and different imaging features of brain. In this study, the OS period prediction was performed using Random Forest, SVM, XgBoost, and LGBM taking radiomic features which represents fused deep features and hand crafted features of the tumor. Efficiency of the prediction depends on the tumor volume that is segmented from the different MRI modalities. Hence the whole tumor and its sub tumor are extracted from multi-modal MR images using U-Net++ deep model and stacked together for deep features extraction using convolutional neural networks. To increase the accuracy, the features are reduced using PCA and then this radiomic feature set was used for OS period prediction. Prediction performance was evaluated for both 2-class and 3-class survival groups. The experiment was performed on well-known dataset BraTS 2017 and achieved a classification AUC value as 63% for 3-class classification and 2-class group using different classifier. Segmentation DOR is computed as 1269.29, 2033.99, and 648.00 for complete tumor, enhancing tumor, and necrotic tumor extraction, respectively. To achieve even more accuracy, bio inspired optimization methods GA and PSO are used on fused feature set. Finally, the method achieves the AUC score of 0.66 using fused feature+SVM+GA (3-class group) and 0.70 using fused feature+SVM+PSO (2-class group) which outperforms the state-of-the-art.