Abstract
Predicting the biological behavior and time to recurrence (TTR) of high-grade diffuse gliomas (HGG) after maximum safe neurosurgical resection and combined radiation and chemotherapy plays a pivotal role in planning clinical follow-up, selecting potentially necessary second-line treatment and improving the quality of life for patients diagnosed with a malignant brain tumor. The current standard-of-care (SoC) for HGG includes follow-up neuroradiological imaging to detect recurrence as early as possible and relies on several clinical, neuropathological, and radiological prognostic factors, which have limited accuracy in predicting TTR. In this study, using an in-silico analysis, we aim to improve predictive power for TTR by considering the role of (i) prognostically relevant information available through diagnostics used in the current SoC, (ii) advanced image-based information not currently part of the standard diagnostic workup, such as tumor-normal tissue interface (edge) features and quantitative data specific to biopsy positions within the tumor, and (iii) information on tumor-associated macrophages. In particular, we introduced a state-of-the-art spatio-temporal model of tumor-immune interactions, emphasizing the interplay between macrophages and glioma cells. This model serves as a synthetic reality for assessing the predictive value of various features. We generated a cohort of virtual patients based on our mathematical model. Each patient's dataset includes simulated T1Gd and Fluid-attenuated inversion recovery (FLAIR) MRI volumes. T1-weighted imaging highlights anatomical structures with high contrast, providing clear detail on brain morphology, whereas FLAIR suppresses fluid signals, improving the visualization of lesions near fluid-filled spaces, which is particularly helpful for identifying peritumoral edema. Additionally, we simulated results on macrophage density and proliferative activity, either in a specified part of the tumor, namely the tumor core or edge ("localized"), or unspecified ("non-localized"). To enhance the realism of our synthetic data, we imposed different levels of noise. Our findings reveal that macrophage density at the tumor edge contributed to a high predictive value of feature importance for the selected regression model. Moreover, there are lower MSE values for the "localized" biopsy in prediction accuracy toward recurrence post-resection compared with "non-localized" specimens in the noisy data. In conclusion, the results show that localized biopsies provided more information about tumor behavior, especially at the interface of tumor and normal tissue (Edge).