dc.contributor.advisor | Jamin, Y | |
dc.contributor.author | Zormpas Petridis, K | |
dc.date.accessioned | 2021-08-25T09:46:55Z | |
dc.date.available | 2021-08-25T09:46:55Z | |
dc.date.issued | 2021-01-31 | |
dc.identifier.citation | 2021 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/4774 | |
dc.description.abstract | Neuroblastoma is a common childhood solid tumour that accounts for 15% of all cancer paediatric deaths. This thesis addresses key deficiencies in our ability to define, monitor and predict neuroblastoma heterogeneity for precision medicine. I used computational science to integrate the spatially-encoded phenotypic information provided by multi-parametric magnetic resonance imaging (MRI) with digital histopathology, demonstrating that MRI can provide non-invasive pathology to characterise neuroblastoma heterogeneity and provide biomarkers of response in clinically-relevant transgenic mouse models of high-risk disease. I first developed and demonstrated the application of novel computational pathology methodologies to enhance the quantitative assessment of tumour components from H&E-stained whole-slide images (WSI). These include two frameworks: SuperCRF, which fuses traditional machine learning with deep learning to model the way pathologists incorporate large-scale tissue architecture and context across spatial spaces to significantly improve single-cell classification and, SuperHistopath, which combines the application of the SLIC superpixels algorithm on low-magnification WSIs (5x) with a convolutional neural network (CNN) for superpixels classification to accurately map tumour heterogeneity from low-resolution histology. I then developed an MRI-histopathology cross-validation pipeline which provides the rigorous validation needed to support the deployment of novel MRI scans in the neuroblastoma clinic. Using this platform, I demonstrated the sensitivity of susceptibility-, T1-Mapping- and diffusion-weighted-MRI to the cellular and microenvironmental hallmarks of high-risk neuroblastoma and their modulation by either vascular- or MYCN-targeted therapies. Finally, I used supervised machine learning classification- and regression-based approaches to show proof-of-concept that habitat imaging derived from these three scans can non-invasively provide quantitative data typically acquired from histological analysis, such as densities of specific cell populations. This thesis demonstrates the potential of multi-parametric MRI to deliver non-invasive "virtual" biopsies to enhance diagnostic and treatment monitoring for children with neuroblastoma and pave new ways in studying tumour as an evolving ecosystem. | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | Institute of Cancer Research (University Of London) | |
dc.rights.uri | https://www.rioxx.net/licenses/all-rights-reserved | |
dc.subject | Theses, Doctoral | |
dc.subject | Neuroblastoma - Radiology | |
dc.subject | Computational Medicine | |
dc.subject | Magnetic Resonance Imaging | |
dc.subject | Computational Pathology | |
dc.subject | Computational Science | |
dc.subject | Machine Learning | |
dc.subject | Histopathology | |
dc.title | Computational science-enabled radiological pathology for the non-invasive mapping of tumour heterogeneity in childhood neuroblastoma | |
dc.type | Thesis or Dissertation | |
dcterms.accessRights | Public | |
dcterms.license | https://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.version | AO | |
rioxxterms.licenseref.uri | https://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2021-01-31 | |
rioxxterms.type | Thesis | |
pubs.notes | No embargo | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Computational Imaging | |
pubs.organisational-group | /ICR/Students | |
pubs.organisational-group | /ICR/Students/PhD and MPhil | |
pubs.organisational-group | /ICR/Students/PhD and MPhil/16/17 Starting Cohort | |
pubs.embargo.terms | No embargo | |
icr.researchteam | Computational Imaging | en_US |
dc.contributor.icrauthor | Zormpas Petridis, Konstantinos | |
uketdterms.institution | Institute of Cancer Research | |
uketdterms.qualificationlevel | Doctoral | |
uketdterms.qualificationname | Ph.D | |
dc.type.qualificationlevel | Doctoral | |
dc.type.qualificationname | Ph.D | |