University of Cambridge
Unravelling the Spatial and Temporal Heterogeneity of High-Grade Serous Ovarian Cancer Using Imaging-Based Biomarkers
Abstract
dc:description.abstractHigh-Grade Serous Ovarian Cancer (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, characterised by significant spatial and temporal heterogeneity, which has been linked to poor survival outcomes. In this challenging landscape, the quest to characterise heterogeneity through non-invasive image-based biomarkers extracted from routinely collected radiological data is paramount. These biomarkers hold the potential to improve predictions of patient diagnosis, prognosis and treatment response. Radiomics, an emerging computational approach, offers a promising non-invasive method to assess tumour heterogeneity using radiological images. The biological validation of radiomic features (e.g. through genomics, proteomics or transcriptomics) aims to deepen our understanding of tumour and tumour microenvironment biology and translate radiomics into clinically relevant insights. This thesis addresses technical barriers in integrating radiogenomics into HGSOC clinical settings, as well as leveraging radiomics for treatment response prediction. It includes four technical contributions, with a comprehensive background chapter introducing concepts and challenges in HGSOC, Computed Tomography (CT), ultrasonography (US), radiomics and radiogenomics to unfamiliar readers. Two chapters focus on multi-modal co-registration algorithms for radiogenomics studies. Chapter 3 refines multi-modal image registration for real-time US-guided biopsies of CT-derived habitats, enhancing the degrees of freedom of a commercially available software to accommodate complex abdominal deformations. Additionally, a novel real-time quantification metric assesses registration fidelity. In Chapter 4, a clinical pipeline for generating 3D-printed moulds for image-tissue registration of resected pelvic and ovarian tumours is described, developed through a pilot trial in a clinical setting, detailing the iterative process. Furthermore, radiomics may correlate with clinical endpoints, providing biomarkers for diagnosis, prognosis, and treatment response evaluation. In Chapter 5, novel image-based biomarkers and radiomic features are integrated into a sequential treatment clinical trial to predict and compare PARP inhibitor and immunotherapy responses over time. Chapter 6 presents an interpretable radiomics framework as a feasibility study to predict changes in tumour composition under neoadjuvant chemotherapy (NACT) from pre-treatment baseline scans. Throughout the investigations, practical constraints and potential improvements have been identified and discussed.
Degree
thesis:*- Name dc:type.qualificationname
- Doctor of Philosophy (PhD)
- Level dc:type.qualificationlevel
- Doctoral
- Grantor dc:publisher.institution
- University of Cambridge
- Year dc:date.issued
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Delgado Ortet, Maria
- Advisor dc:contributor.advisor
-
- Escudero Sanchez, Lorena
Subjects
dc:subject × 7Rights
dc:rightsIdentifiers
dc:identifier.*- DOI dc:identifier.doi
- https://doi.org/10.17863/CAM.112878
- OAI identifier oai:identifier
- oai:www.repository.cam.ac.uk:1810/375055