Computational science-enabled radiological pathology for the non-invasive mapping of tumour heterogeneity in childhood neuroblastoma

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