Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning.

Previous studies on pediatric DMG prognostication relied on manual tumor segmentation, which is time-consuming and has high inter-operator variability. There is a great need for non-invasive prognostic imaging tools that can be universally used. Such tools should be automatic, objective, and easy to use in multi-institutional clinical trials. We developed a fully automatic imaging tool to segment subregions of DMG and select radiomic features to predict patient overall survival (OS). Our acquired 4 sequences of MRI for each patient, at both diagnostic and post-radiation therapy from 2 institutions, were more comprehensive than previous studies. The proposed method achieved high accuracy in DMG segmentation and survival prediction, especially for patients having short OS. The proposed method will be the foundation of increasing the utility of MRI as a tool for predicting clinical outcome, stratifying patients into risk-groups for improved therapeutic management and monitoring therapeutic response with greater sensitivity and an opportunity to adapt treatment.