Mathematical Modelling of Survival in Low Grade Gliomas at Malignant Transformation with XGBoost.

Motivation: Early detection of low-grade glioma (LGG) malignant transformation (MT) is vital for treatment decisions, prognosis, quality of life and patient-centered care. Goal(s): To develop non-linear machine learning models using XGBoost algorithm to predict overall survival using clinical, molecular, genetic and radiomic data at MT. Approach: 553 LGGs with histology and MRI underwent in-house tumour segmentation pipeline with radiomic feature extraction and masked disconnectome of map components. Results: XGB Classifier model predicted OS > 5 years from MT with an accuracy of 64%. Age, IDH1 mutation, 1p/19q co-deletion, regularity of tumour shape, and disconnectome-related perilesional components were most predictive of survival. Impact: Understanding malignant transformation of low-grade gliomas is crucial for research and the development of new treatment strategies. Defining the radiological features at malignant transformation allows for a timely shift in the treatment plan with potential to improve repsonse to therapy.