Hypoxia mapping in cancer using MRI

Abstract

Tissue hypoxia is present in many solid tumours and is linked with therapeutic resistance, aggressive tumour growth, and poor disease prognosis. Hypoxia-targeted therapies may improve patient outcome, but there is currently no clinically available method for selecting patients whose tumours contain hypoxia prior to therapy; indeed, previous clinical trials of such therapies have given poor results, likely due to a lack of determining which patients were likely to benefit. There is therefore an unmet clinical need to develop non-invasive and reliable tumour hypoxia biomarkers. Oxygen-enhanced (OE) MRI and dynamic contrast-enhanced (DCE) MRI measurements reflect tissue oxygenation and perfusion, respectively, and show potential for mapping intratumoural hypoxia. OE-MRI and DCE-MRI were performed on a cohort of 16 murine xenograft tumours (data were acquired in a separate study to this PhD). Temporal drift was observed in dynamic OE-MRI signals prior to oxygen inhalation. A model of drift-correction was developed, based on a time-varying applied flip-angle. This novel method of drift-correction was effective in characterising and correcting for signal drift across the cohort, and led to significant changes in the quantification of OE-MRI data in tumours. An optimised, data-driven analysis method (ODD) was developed to locate robustly identifiable patterns in combined OE/DCE-MRI data, and was applied to the same preclinical tumour cohort. Six tumour tissue categories were identified, two of which displayed OE-MRI and DCE-MRI enhancement characteristics consistent with hypoxia. ODD tissue segmentation agreed strongly with a separate, preclinically validated segmentation method (TBM), but without the requirement to define arbitrary data thresholds. A cohort of 12 non-small-cell lung cancer (NSCLC) patients underwent OE-MRI and DCE- MRI scans pre- and post-radiotherapy (data were acquired in a separate study to this PhD). Extensive work was carried out to process and prepare these data for subsequent combined OE/DCE-MRI analysis. ODD was applied to these new, clinical data and identified seven tissue categories, one of which likely corresponded to hypoxia. ODD tracked the response of NSCLC tumours to therapy at a microenvironmental level, and was shown to provide a more stable stratification of hypoxic vs. non-hypoxic tumours than TBM. Results from this thesis suggest: signal drift in OE-MRI data may be corrected using the methods proposed herein; ODD can map the spatially heterogeneous distribution of apparent tissue hypoxia, reliably stratify patients’ tumours by hypoxic status, and monitor treatment effects; and OE-MRI and ODD are readily translatable between the preclinical and clinical research settings.

Date of Award	1 Aug 2019
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Geoff Parker (Supervisor) & Julian Matthews (Supervisor)