Abstract 4138: Prediction of progression-free survival in patients with primary glioblastoma: MRI T2 relaxivity and deep learning

Introduction: We aimed to evaluate T2 relaxation times in the follow-up of patients with glioblastoma (GBM), the most common and aggressive primary brain tumor with nearly universal recurrence following standard therapy. Methods: In this partially retrospective study, 53 patients (mean age 45.05 years ± 12.6) with non-recurrent GBM followed at our department roughly every 2 months after surgery and combined chemo-radiotherapy were included (>180 days progression-free survival [PFS] as determined by RANO,1 ≥3 MRI examinations in the PFS interval [same scanner and protocol]). Thirty-six patients additionally received Tumor-Treating Fields (TTFields) therapy. All data used were prior to progression. T2 relaxation rates (1/T2) were calculated voxel-wise assuming mono-exponential decay. Whole-brain (WB) histogram values were extracted from 1/T2 maps including skewness, kurtosis, mean, median, 15 and 85 percentile values and variance. We additionally segmented 1/T2 maps using a 5 compartment Gaussian mixture model (Python v3.8.2), producing mean, variance and voxel percentage for each component. To evaluate predictive potential with respect to PFS, we used a deep recurrent neural network (long short-term memory [LSTM] model) in Tensorflow v2.3.1 (4 timesteps); the features used included the above metrics as well as TTFields treatment status and scanner. Twenty subjects were included in the predictive model, as inclusion criteria were stricter (progression, ≤120 days between all MRIs [mean 43.6 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. Results: We found WB median 1/T2 correlated with PFS, as values decreased prior to progression. WB median 1/T2 linear regression slopes also differed in progression versus pseudo-progression, as values were relatively stable in pseudo-progression. The deep LSTM regression model achieved an R2 of 97%. The deep LSTM classification model achieved a mean macro precision of 86%, recall 85% and F1 accuracy of 85% in predicting progression within 18 months. Conclusions: We found very intriguing results with WB median 1/T2 measurements in distinguishing progression from pseudo-progression, and in suggesting progression despite otherwise unremarkable imaging. A more complex predictive model trained on pre-progression data, using a fully-automated segmentation met