Spatially mapped predictions of evolving tumor response of high-grade glioma via imagedriven mathematical modeling

Abstract Timely treatment response assessment of high-grade gliomas (HGG), crucial for driving therapeutic decisions, remains a challenge; as HGGs exhibit variable response to treatment within different sub-regions. Current assessment using multi-parametric MRI (mpMRI) depends largely upon follow-up (FU) imaging timepoints for achieving diagnostic certainty, which delays therapeutic interventions. Mathematical modeling (MM) of tumor growth and treatment response can provide spatiotemporal information of HGG evolution in response to treatment, thus allowing for prospective early identification of resilient tumor subregions. AIMS: We aim to initialize and calibrate an image-driven MM framework to forecast HGG response, both at the end of chemoradiotherapy (CRT) and at 3-month FU. METHODS In a prospective clinical study, weekly mpMRIs (post-contrast T1, T2 FLAIR, and diffusion) for patients with HGG receiving CRT were used to describe tumor extent and cellularity. This data collected from baseline (pre-CRT) till week 3 (mid-CRT) was used to calibrate a model family to forecast HGG response for each individual patient at week 6 (end CRT) and at 3-month FU. RESULTS Error between the forecasted and observed responses was assessed globally using percent error in tumor volume, and at the local level by Pearson correlation coefficient (PCC). In an initial cohort of 11 patients, our MM framework predictions had a percent error in tumor volume of less than 8.6% and at week 6 RT and less than 20% at 3 months FU. The PCCs were 0.84 at week 6 RT and 0.72 at 3 months FU. CONCLUSIONS Temporal consistency across this early evaluation of the model predictions show promise of image-driven MM for HGG response forecasting to guide timely personalized assessment and adjustment of treatment.