NIMG-71. T2 RELAXIVITY AND DIFFUSION TENSOR METRICS IN THE PREDICTION OF PROGRESSION-FREE SURVIVAL IN NON-RECURRENT HIGH GRADE GLIOMA

Abstract High grade glioma (HGG) is the most common primary brain tumour; we aimed to evaluate T2 relaxivity and diffusion tensor imaging (DTI) metrics in the prediction of progression-free survival. Seventy-two non-recurrent HGG subjects (IDH: MUT=10, Unknown=20) followed at our institution after surgery and combined chemo-radiotherapy were included. All data used were prior to progression as per RANO criteria; only the first 2 years of follow up was included in the analyses. T2 relaxation rates (1/T2) were calculated voxel-wise assuming mono-exponential decay. Diffusion data were corrected from two opposite phase encode acquisitions and DTI metrics evaluated included FA, MD, p, q, and L1. Whole- and half-brain (ipsilateral/contralateral) masks were generated by brain extraction and registration; data extracted from these masks were analyzed using python 3.8 with the exception of numerous histogram features extracted using fsl tools. Radiomics features were also extracted using the pyradiomics package. The lofo-importance package was used for feature selection. A multi-level approach was used to reduce the initial 32,000 MRI features to a final number of 443; from these, the 50 highest-ranked features were used further. Within-subject feature engineering was then performed including 1–4 step forward and backward lag, lag difference, as well as area, maximum, mean and cumulative sum. Non-MRI features included available demographic, histological and treatment-related data. A LightGBM classification model was used for the prediction of progression within 90 days; the data were grouped by subject, such that each subject was present only in training or testing datasets. Hyperparameters were tuned using randomized search cross validation, and the final model using 5-fold cross validation reached a mean ROC-AUC of 0.89. Our results show the potential of quantitative MRI features derived from qualitatively unremarkable images within the progress-free interval to identify subjects at risk of imminent progression.