Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning - Baid, Ujjwal; Rane, Swapnil U.; Talbar, Sanjay; Gupta, Sudeep; Thakur, Meenakshi H.; Moiyadi, Aliasgar; Mahajan, Abhishek (Frontiers in Computational Neuroscience, 2020)
Glioblastoma multiforme (GBM) are aggressive brain tumors, which lead to poor Overall Survival (OS) of patients. For precise surgical and treatment planning OS prediction of GBM patients is highly desired by the clinicians and oncologists. Radiomics research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, first order intensity based, volume and shape based and textural radiomic features are extracted from FLAIR and T1ce MRI data. The region of interest (ROI) is further decomposed with Stationary Wavelet Transform (SWT) with low pass and high pass filtering. This helped in acquiring the directional information. The efficiency of the proposed algorithm is evaluated on Brain Tumor Segmentation (BraTS) challenge training, testing and validation dataset. The proposed approach secured third position in BraTS 2018 challenge for Overall Survival prediction task.