Patient-Specific Metrics of Invasiveness Reveal Significant Prognostic Benefit of Resection in a Predictable Subset of Gliomas

Abstract

ObjectMalignant gliomas are incurable, primary brain neoplasms noted for their potential to extensively invade brain parenchyma. Current methods of clinical imaging do not elucidate the full extent of brain invasion, making it difficult to predict which, if any, patients are likely to benefit from gross total resection. Our goal was to apply a mathematical modeling approach to estimate the overall tumor invasiveness on a patient-by-patient basis and determine whether gross total resection would improve survival in patients with relatively less invasive gliomas.MethodsIn 243 patients presenting with contrast-enhancing gliomas, estimates of the relative invasiveness of each patient's tumor, in terms of the ratio of net proliferation rate of the glioma cells to their net dispersal rate, were derived by applying a patient-specific mathematical model to routine pretreatment MR imaging. The effect of varying degrees of extent of resection on overall survival was assessed for cohorts of patients grouped by tumor invasiveness.ResultsWe demonstrate that patients with more diffuse tumors showed no survival benefit (P = 0.532) from gross total resection over subtotal/biopsy, while those with nodular (less diffuse) tumors showed a significant benefit (P = 0.00142) with a striking median survival benefit of over eight months compared to sub-totally resected tumors in the same cohort (an 80% improvement in survival time for GTR only seen for nodular tumors).ConclusionsThese results suggest that our patient-specific, model-based estimates of tumor invasiveness have clinical utility in surgical decision making. Quantification of relative invasiveness assessed from routinely obtained pre-operative imaging provides a practical predictor of the benefit of gross total resection.

The biology and mathematical modelling of glioma invasion: a review

“…While their ability to probe into specific outcomes from particular changes in, say, a signalling pathway is limited, phenomenological models are better able to provide insights into long-term, large-scale behaviour than models focused on smaller-scale phenomena. Indeed, phenomenological models have proven useful in characterizing glioma invasion, identifying glioma patients receiving maximal benefit from therapeutic interventions [177] and defining a more prognostic response metric for patients than is currently available [178,179].…”

Location of brain tumor intersecting white matter tracts predicts patient prognosis

“…This occurs through a complex process of adhesion, motility, and invasion 14 . Recent studies suggest that imaging can predict the benefit of gross total versus subtotal resection based on calculated invasiveness metrics 15 . Cell invasion maps also can predict response to bevacizumab at recurrence 16 .…”

CNS Anticancer Drug Discovery and Development: 2016 conference insights

“…This included phenotypic cell models driven by MRI imaging scale metrics. This approach has led to a proliferation-invasion (PI) model that utilizes patient MRI data to uniquely estimate glioma cell density using proliferation and diffusion (invasion) rate parameters [29,30]. The PI model has been extended to a proliferation-invasion-hypoxia-necrosisangiogenesis model specifically used to analyze anti-angiogenic therapy and the need for gross total resection.…”