Development and validation of a MRI-radiomics-based machine learning approach in High Grade Glioma to detect early recurrence

Purpose: Patients diagnosed with High Grade Gliomas (HGG) generally tend to have a relatively negative prognosis with a high risk of early tumor recurrence (TR) after post-operative radio-chemotherapy. The assessment of the pre-operative risk of early versus delayed TR can be crucial to develop a personalized surgical approach. The purpose of this article is to predict TR using MRI radiomic analysis. Methods: Data were retrospectively collected from a database. A total of 248 patients were included based on the availability of 6-month TR results: 188 were used to train the model, the others to externally validate it. After manual segmentation of the tumor, Radiomic features were extracted and different machine learning models were implemented considering a combination of T1 and T2 weighted MR sequences. Receiver Operating Characteristic (ROC) curve was calculated with relative model performance metrics (accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)) at