Predicting Cancer Patient Survival Using Dynamic Contrast Enhanced MRI

Abstract

This thesis describes the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to study the prognostic role of microvascular physiology and heterogeneity in locally advanced cancers of the cervix, bladder, and head and neck. To increase the utility of DCE-MRI parameters for prognostication and use in heterogeneity analyses, a novel model fitting approach was developed to reduce the error in two-compartment exchange model (2CXM) parameter estimates. Using this method, precision of 2CXM parameters was increased in 35 of 42 experimental conditions (improvements between 4.7% and 50%) and bias reduced in 30 of 42 conditions (reductions between 1.8% and 49%). The prognostic value of plasma flow, permeability surface area product, and contrast agent volume transfer constant were assessed in a cervix cancer dataset. Plasma flow was the most prognostic parameter (HR = 0.25, P = 0.0086), followed by the volume transfer constant (HR = 0.33, P = 0.031), then the permeability surface area product (HR = 0.43, P = 0.090). Inclusion of plasma flow in survival modelling significantly increased the ability to discriminate between patients with short and long disease-free survival, compared to clinicopathologic factors alone (P = 0.043). The universal prognostic value of microvascular heterogeneity was assessed in cervix, bladder, and head and neck datasets. Following estimation of 2CXM parameters for each patient, a selection of previously published heterogeneity biomarkers were computed and entered into a random survival forest variable selection algorithm. Two variables (vvas, Atrans) were identified as universally prognostic and significantly improved discriminative ability of survival models compared to clinicopathologic factors alone (P < 0.001). Gaussian process models were used to decompose statistical and spatial aspects of intratumoural microvascular heterogeneity. When applied to the three cancer datasets described above, statistical variance in plasma flow (P = 0.00025) was universally prognostic and showed greater discriminative ability compared with spatial scale and average microvascular function parameters. The results of this thesis demonstrate that joint fitting reduces error in DCE-MRI parameters. DCE-MRI estimates of plasma flow appear to hold greater prognostic value than the volume transfer constant and permeability surface area product, and microvascular heterogeneity has potential to provide universal prognostic value. The biomarkers vvas, Atrans, and variance in plasma flow, were identified as universally prognostic. Future work should test the reproducibility of these biomarkers for prognostication in independent datasets.

Date of Award	1 Aug 2017
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Christopher Rose (Supervisor), Catharine West (Supervisor) & Lucy Kershaw (Supervisor)