Abstract 4138: Prediction of progression-free survival in patients with primary glioblastoma: MRI T2 relaxivity and deep learning
Authors: Rulseh Aaron M. , Vymazal Josef 
Published: June 2022

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 (&gt;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 method followed by deep learning, showed very promising results. The present results may find utility in the monitoring of GBM patients and may be advantageous to include in multimodal predictive models.

Reference: 1. Wen PY et al. J Clin Oncol 2010;28:1963-1972.

Citation Format: Aaron M. Rulseh, Josef Vymazal. Prediction of progression-free survival in patients with primary glioblastoma: MRI T2 relaxivity and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4138.