A Proposed Paradigm Shift in Initializing Cancer Predictive Models with DCE-MRI Based PK Parameters: A Feasibility Study

Abstract

Glioblastoma multiforme is the most aggressive type of glioma and the most common malignant primary intra-axial brain tumor. In an effort to predict the evolution of the disease and optimize therapeutical decisions, several models have been proposed for simulating the growth pattern of glioma. One of the latest models incorporates cell proliferation and invasion, angiogenic net rates, oxygen consumption, and vasculature. These factors, particularly oxygenation levels, are considered fundamental factors of tumor heterogeneity and compartmentalization. This paper focuses on the initialization of the cancer cell populations and vasculature based on imaging examinations of the patient and presents a feasibility study on vasculature prediction over time. To this end, pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging using Toft’s model are used in order to feed the model. Ktrans is used as a metric of the density of endothelial cells (vasculature); at the same time, it also helps to discriminate distinct image areas of interest, under a set of assumptions. Feasibility results of applying the model to a real clinical case are presented, including a study on the effect of certain parameters on the pattern of the simulated tumor.

Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

“…However, in our case the necrotic region seemed to be overestimated by a small percentage of 15%, which can be explained by the limitation of MR imaging resolution regarding Table 1 Correlation (%) between the hypoxic, well-perfused and necrotic components using the two initialization schemes for the BU-NMF algorithm physiology, thus voxels with values close to zero that are adjacent to necrotic regions with k trans = 0 are also considered as necrotic. Similar assumptions for the k trans range were used in a recent study [19], which deals with the delineation of tumor physiological regions through the application of a cancer predictive model aiming to predict patients' glioblastoma progress.…”

Dual Parallel Sequence Progression as Vascular Pseudo-Reactivities to Tumor Necrosis and to Proliferation of Glioblastoma Cells

“…Transcriptionally active 06-methyguanine-DNA-methyltransferase effectively removes alkyl adducts and prevents thus cross-linking and resistance to alkylating drugs [28]. Oxygenation levels in particular are critical to tumor heterogeneity and compartmentalization [29].…”

In silico tumor growth validation based on human brain cancer models

“…GB cell death in 2D and 3D Interestingly, during the observation of the MR images of the GB patients included in our study, as well as by taking into account the empirical clinical knowledge, the next remark was noted: the vast majority of the GB tumor within the brain lesion is necrotic. However, as stated before [99], this necrotic pattern is common between all GB cases and can be attributed in both hypoxia formation due to nutrient-deficiency, as well as to another critical feature of the tumor growth, the intrinsic cell death probability of the cancer cells. This spontaneous GB cell death is considered to be mainly necrosis, rather than apoptosis or autophagy [261].…”

“…First-principle-based mathematical and computational models are widely used to describe the complex evolution of tumors as they can manage the multiscale nature of the biological processes involved, integrate the information from multiple biological experiments [97,98] and/or clinical examinations [99][100][101][102]. Furthermore, the models predict behaviors of the system that can guide new targeted experiments [89,103].…”

“…These models are most often parameterized integrating preexisting data reported in the literature derived from different research groups not following the same procedures that may not reflect any real tumor type. Thus, they usually fail to be close to reality due to arbitrary parametrization and lack of ground truth validation [19,98,99,121,124]. The identification of a plausible mechanistic model and its proper parameterization able to quantitatively explain a large set of data that were not used to calibrate the model and predict the clinical/experimental outcome remains a demanding challenge although recent efforts drive research focus towards this direction [100].…”