Towards Image - Based Personalization of Glioblastoma Therapy A Clinical and Biological Validation Study of a Novel, Deep Learning - Driven Tumor Growth Model

Abstract

Background The diffuse growth pattern of glioblastoma is one of the main challenges for improving patient survival. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel, deep learning - based growth model, aiming to close the gap between the experimental state and clinical implementation. Methods 124 patients from The Cancer Genome Archive network and 397 patients from the UCSF Glioma MRI Dataset were assessed for correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (r) parameters stemming from a Fisher-Kolmogorov growth model adjusted to the patients’ preoperative images using deep learning. Cox multivariable regression and Spearman correlation were performed to test for statistical significance. To further evaluate clinical potential, we performed the same growth modeling on preoperative MRI data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. Results The parameter ratio Dw/r (p < 0.05 in TCGA) as well as the simulated tumor volume (p < 0.05 in TCGA and UCSF) were significantly inversely correlated with overall survival. Interestingly, we observed a significant correlation between 11 signaling pathways that are associated with proliferation, and the estimated proliferation parameter r. Depending on the cutoff value for tumor cell density, we observed a significant improvement of recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Conclusion Identifying a significant correlation between computed growth parameters, and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve accuracy of personalized radiation planning in the near future.