NIMG-69. MRI T2 RELAXIVITY IN THE PREDICTION OF PROGRESSION-FREE SURVIVAL IN NEWLY-DIAGNOSED GLIOBLASTOMA: WHOLE BRAIN MEASUREMENTS AND DEEP LEARNING

Glioblastoma (GBM) is the most common and aggressive primary brain tumour; we aimed to evaluate T2 relaxivity in their follow. Fifty-one newly diagnosed GBM followed at our department after surgery and combined chemo-radiotherapy were included (> 180 days progression-free survival [PFS] as determined by RANO, ≥ 3 MRI examinations in the PFS interval). All data used were prior to progression. T2 relaxation rates (1/T2f) were calculated voxel-wise assuming mono-exponential decay. Whole-brain (WB) values were extracted including skewness, kurtosis, mean, median, 15 and 85 percentile histogram values and variance. We additionally segmented the 1/T2f volumes using a 5 compartment Gaussian mixture model, producing mean, variance and percent per component. To evaluate predictive potential with respect to PFS, we used a deep long short-term memory (LSTM) model in tensorflow (4 timesteps). Twenty subjects were included in the predictive model, as inclusion criteria differed (progression, ≤ 120 days between all MRIs [mean 44.3 days], ≥ 4 PFS MRIs). Two models were tested: a regression model (days to progression) and a classification model (±18 months PFS; models differed in output layer). Both models were run 10 times; mean results are presented. We found WB median 1/T2f correlated with PFS, as values decreased prior to progression. WB median 1/T2f linear regression slopes also differed in progression vs. pseudo-progression. The deep LSTM regression model achieved an R2 of 86%. The deep LSTM classification model achieved a mean macro precision of 85%, recall 84% and F1 accuracy of 84% in classifying progression/no progression at an 18 month cutoff. We found very intriguing results with WB median 1/T2f measurements in distinguishing progression from pseudo-progression, and in suggesting progression despite otherwise unremarkable imaging. A more complex predictive model, using a fully-automated segmentation method followed by deep learning, was very promising. Our results may find utility in the monitoring of GBM patients.