From Patient-Specific Mathematical Neuro-Oncology to Precision Medicine

Abstract

Gliomas are notoriously aggressive, malignant brain tumors that have variable response to treatment. These patients often have poor prognosis, informed primarily by histopathology. Mathematical neuro-oncology (MNO) is a young and burgeoning field that leverages mathematical models to predict and quantify response to therapies. These mathematical models can form the basis of modern “precision medicine” approaches to tailor therapy in a patient-specific manner. Patient-specific models (PSMs) can be used to overcome imaging limitations, improve prognostic predictions, stratify patients, and assess treatment response in silico. The information gleaned from such models can aid in the construction and efficacy of clinical trials and treatment protocols, accelerating the pace of clinical research in the war on cancer. This review focuses on the growing translation of PSM to clinical neuro-oncology. It will also provide a forward-looking view on a new era of patient-specific MNO.

Simulating the growth of hepatic metastases from carcinoma of the pancreas using LabVIEW

“…Descriptive tumor models have been used with some success in the field of neuro-oncology, and led to the new discipline of Mathematical Neuro-Oncology [7]. This field uses descriptive tumor models to predict and quantify response to therapy.…”

“…This mathematical model defines the rate of change of tumor cell density as equal to the diffusion of tumor cells out into the tissue plus the net proliferation of tumor cells [7]. The model was fully developed for neuro-oncology, and is referred to as the Proliferation-Invasion model.…”

“…The model was used to simulate surgical resection for 70 patients with glioblastoma and predicted the survival in this group [7]. In addition to predicting tumor growth the PI model has been used to identify patients who might respond to various surgical strategies, predict response to radiation therapy and to connect clinical imaging features and genetic information [11].…”

“…The developers of the Proliferation-Invasion model, from which the DP model is directly derived, use a different approach for the proliferation term [7].…”

“…Rate of change in Diffusion of Proliferation of tumor cell density tumor cells tumor cells over time This is a partial differential equation with two parameters: net rate of migration (D) and proliferation ( ), both of which are calculated in a patient-specific manner using clinical imaging [7]. From the point of view of modeling, the DP model is based on several approximations, especially in the diffusion term since the Fick equation is based on random molecular motion in a dilute solution.…”