Patient-specific biomathematical model to predict benefit of resection in human gliomas.

Abstract

e13017 Background: Gliomas are incurable, primary brain tumors noted for their invasion of brain parenchyma. Our goal was to apply a biomathematical model to estimate the overall tumor invasiveness on an individual basis and determine whether the estimated number of residual glioma cells after resection of any extent is predictive of survival. Methods: Estimates of net rates of proliferation (ρ) and diffusion (D) of glioma cells, based on a biomathematical model of cell density, yield a ratio describing relative invasiveness (ρ/D). This metric was derived for 185 contrast enhancing gliomas from pretreatment MRIs. The residual MRI-detectable volume was combined with the ρ/D tuned to each patient's tumor to allow estimation of the number of glioma cells remaining post-resection. The patients were split into three cohorts by ρ/D values. Within each cohort, all possible cut-off values were considered as a possible threshold between low and high residual patient groups. Log-rank tests were performed for each possible threshold to determine if the Kaplan-Meyer curves were significantly different. Results: We demonstrate that for low ρ/D tumors there was no threshold of residual tumor cell population observed that could yield a significant survival benefit. Both the mid ρ/D and high ρ/D had robust thresholds of residual tumor cell population, below which patients saw significantly higher survival than patients with similar invasiveness and more residual tumor. Conclusions: These results suggest that our patient-specific biomathematical model-based estimates of tumor invasiveness and residual tumor cells have clinical utility in driving neurosurgical decision making. Given the physicians understanding of patient-specific risk factors such as tumor location, age, and physical health, an understanding of the differential surgical benefit allows for more informed cost-benefit analysis and improved quality of life. This metric enables physicians to identify a subset of patients who do not see a survival benefit from resection, making the operation potentially not worth the risks.