Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status - Soltani, Madjid; Bonakdar, Armin; Shakourifar, Nastaran; Babaei, Reza; Raahemifar, Kaamran (Frontiers in Oncology, 2021)
Cancer stands out as one of the fatal diseases people are facing all the time. Each year, a countless number of people die due to the late diagnosis of cancer or wrong treatments. Glioma, one of the most common primary brain tumors, has different aggressiveness and sub-regions, which can affect the risk of disease. Although prediction of overall survival based on multimodal magnetic resonance imaging (MRI) is challenging, in this study, we assess if and how location-based features of tumors can affect overall survival prediction. This approach is evaluated independently and in combination with radiomic features. The process is carried out on a dataset entailing MRI images of patients with glioblastoma. To assess the impact of resection status, the dataset is divided into two groups, patients reported as gross total resection and unknown resection status. Then, different machine learning algorithms are used to evaluate how location features are linked with overall survival. Results from regression models indicate that location-based features have considerable effects on patients’ overall survival independently. Additionally, classifier models show an improvement in prediction accuracy by the addition of location-based features to radiomic features.