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Characterising Heterogeneity of Glioblastoma using Multi-parametric Magnetic Resonance Imaging 10.17863/CAM.34779 Li, Chao 0000000207340011 Price, Stephen John A better understanding of tumour heterogeneity is central for accurate diagnosis, targeted therapy and personalised treatment of glioblastoma patients. This thesis aims to investigate whether pre-operative multi-parametric magnetic resonance imaging (MRI) can provide a useful tool for evaluating inter-tumoural and intra-tumoural heterogeneity of glioblastoma. For this purpose, we explored: 1) the utilities of habitat imaging in combining multi-parametric MRI for identifying invasive sub-regions (I & II); 2) the significance of integrating multi-parametric MRI, and extracting modality inter-dependence for patient stratification (III & IV); 3) the value of advanced physiological MRI and radiomics approach in predicting epigenetic phenotypes (V). The following observations were made: I. Using a joint histogram analysis method, habitats with different diffusivity patterns were identified. A non-enhancing sub-region with decreased isotropic diffusion and increased anisotropic diffusion was associated with progression-free survival (PFS, hazard ratio [HR] = 1.08, P < 0.001) and overall survival (OS, HR = 1.36, P < 0.001) in multivariate models. II. Using a thresholding method, two low perfusion compartments were identified, which displayed hypoxic and pro-inflammatory microenvironment. Higher lactate in the low perfusion compartment with restricted diffusion was associated with a worse survival (PFS: HR = 2.995, P = 0.047; OS: HR = 4.974, P = 0.005). III. Using an unsupervised multi-view feature selection and late integration method, two patient subgroups were identified, which demonstrated distinct OS (P = 0.007) and PFS (P < 0.001). Features selected by this approach showed significantly incremental prognostic value for 12-month OS (P = 0.049) and PFS (P = 0.022) than clinical factors. IV. Using a method of unsupervised clustering via copula transform and discrete feature extraction, three patient subgroups were identified. The subtype demonstrating high inter-dependency of diffusion and perfusion displayed higher lactate than the other two subtypes (P = 0.016 and P = 0.044, respectively). Both subtypes of low and high inter-dependency showed worse PFS compared to the intermediate subtype (P = 0.046 and P = 0.009, respectively). V. Using a radiomics approach, advanced physiological images showed better performance than structural images for predicting O6-methylguanine-DNA methyltransferase (MGMT) methylation status. For predicting 12-month PFS, the model of radiomic features and clinical factors outperformed the model of MGMT methylation and clinical factors (P = 0.010). In summary, pre-operative multi-parametric MRI shows potential for the non-invasive evaluation of glioblastoma heterogeneity, which could provide crucial information for patient care. University of Cambridge 2018-12-06 Thesis Doctoral Doctor of Philosophy (PhD) en The Cambridge Trust and China Scholarship Council ; Clare College; the British Neuro-Oncology Society; the EG Fearnsides Trust; the International Society for Magnetic Resonance in Medicine https://www.repository.cam.ac.uk/handle/1810/287475 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/d5dc6006-ec1d-4ac9-8111-9a70287137d8/download 4362314445103f6c1ea9f5fc08230a3f https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/f9fc9c9e-6a1a-4f66-b6e4-86c15f32961e/download 87eda9de84448d1f82354d60eee3eb5f https://www.rioxx.net/licenses/all-rights-reserved/ magnetic resonance imaging diffusion imaging perfusion imaging magnetic resonance spectroscopy multiparametric MRI glioblastoma neuro-oncology tumour heterogeneity machine learning artificial intelligence radiogenomics radiomics tumour progression tumour evolution