Abstract 5487: A biologically-motivated mathematical model for forecasting chemoradiationresponse in high grade gliomas: Initial results
Authors: David Andrew Hormuth, Karine Al Feghali, Andrew M. Elliot, Caroline Chung, Thomas E. Yankeelov
Journal: Cancer Research (2020)

Abstract Introduction: Using patient-specific imaging data, we aimed to calibrate biologically-based mathematical models of tumor growth and treatment response to forecast the progression of high-grade glioma on an individual patient basis. Methods: Four patients with partially resected or biopsy only, histologically confirmed high grade gliomas were imaged with T2-FLAIR, diffusion weighted (DW-) MRI, and post-contrast T1-weighted MRI at baseline (prior to chemoradiation), and at 1-, 3-, and 5-months following chemoradiation. Patients received standard of care radiotherapy (RT) with concurrent and adjuvant temozolomide. The gross tumor volume (GTV) defined as enhancing tumor and clinical tumor volume (CTV) defined as non-enhancing, T2-hyperintense region were expertly segmented from the post-contrast T1-weighted and T2-FLAIR images, respectively. The apparent diffusion coefficient (ADC) estimated from DW-MRI was used to assign tumor cell number within the GTV. Within the CTV, we interpolated the tumor cell number from 2% of the carrying capacity (at the periphery of the CTV) to the number of cells calculated at the periphery of the GTV. Using this data, a 3D mechanically-coupled, reaction-diffusion model was developed to describe the spatiotemporal evolution of tumor cell number due to proliferation, diffusion, and death due to chemoradiation on an individual patient basis. For each patient, model parameters were calibrated using their cellularity measurements from the entire time course (to evaluate how well the model describes the data) and separately from a subset of the time course (to evaluate how well the model predicts future data). For the prediction scenario, we then used the calibrated parameters in a forward evaluation of the model to predict future tumor growth at the final imaging visit. The model estimates of tumor cellularity where compared directly to the measured tumor distributions at the volume and voxel levels. Results: For the model fit scenario, a strong level of voxel-wise correlation (Pearson correlation coefficients, PCC, ranging from 0.68 to 0.94) and agreement (concordance correlation coefficients, CCC, 0.76 to 0.99) were observed between the calibrated model and the measured cellularity for each patient. Additionally, Dice coefficients (measuring the degree of overlap between calibrated and observed values) ranged from 0.76 to 0.92 at all time points. For the prediction scenario, a strong of voxel-wise correlation (PCC ranging from 0.61 to 0.90) and agreement (CCC ranging from 0.72 to 0.92) were observed between the predicted and estimated cellularity for each patient. A strong level of spatial agreement was also observed with Dice values ranging from 0.65 to 0.84 at all time points. Conclusions: This preliminary study demonstrates that patient-specific imaging data can be used to calibrate a mathematical model that can accurately predict the spatio-temporal distribution of enhancing and non-enhancing glioma. Funding: NCI U01 CA174706, CPRIT RR160005. AAPM Grant 2018 Citation Format: David Andrew Hormuth, Karine Al Feghali, Andrew M. Elliot, Caroline Chung, Thomas E. Yankeelov. A biologically-motivated mathematical model for forecasting chemoradiationresponse in high grade gliomas: Initial results [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5487.