Assessment and optimisation of MRI measures of atrophy as potential markers of disease progression in multiple sclerosis

There is a need for sensitive measures of disease progression in multiple sclerosis (MS) to monitor treatment effects and understand disease evolution. MRI measures of brain atrophy have been proposed for this purpose. This thesis investigates a number of measurement techniques to assess their relative ability to monitor disease progression in clinically isolated syndromes (CIS) and early relapsing remitting MS (RRMS). Presented, is work demonstrating that measurement techniques and MR acquisitions can be optimised to give small but significant improvements in measurement sensitivity and precision, which provided greater statistical power. Direct comparison of numerous techniques demonstrated significant differences between them. Atrophy measurements from SIENA and the BBSI (registration-based techniques) were significantly more precise than segmentation and subtraction of brain volumes, although larger percentage losses were observed in grey matter fraction. Ventricular enlargement (VE) gave similar statistical power and these techniques were robust and reliable; scan-rescan measurement error was <0.01% of brain volume for BBSI and SIENA and <0.04ml for VE. Annual atrophy rates (using SIENA) were -0.78% in RRMS and -0.52% in CIS patients who progressed to MS, which were significantly greater than the rate observed in controls (-0.07%). Sample size calculations for future trials of disease-modifying treatments in RRMS, using brain atrophy as an outcome measure, are described. For SIENA, the BBSI and VE respectively, an estimated 123, 157 and 140 patients per treatment arm respectively would be required to show a 30% slowing of atrophy rate over two years. In CIS subjects brain atrophy rate was a significant prognostic factor, independent of T2 MRI lesions at baseline, for development of MS by five year follow-up. It was also the most significant MR predictor of disability in RRMS subjects. Cognitive assessment of RRMS patients at five year follow-up is described, and brain atrophy rate was a significant predictor of overall cognitive performance, and more specifically, of performance in tests of memory. The work in this thesis has identified methods for sensitively measuring progressive brain atrophy in MS. It has shown that brain atrophy changes in early MS are related to early clinical evolution, providing complementary information to clinical assessment that could be utilised to monitor disease progression

Precision Monitoring for Disease Progression in Patients with Multiple Sclerosis: A Deep Learning Approach

Artificial intelligence has tremendous potential in a range of clinical applications. Leveraging recent advances in deep learning, the works in this thesis has generated a range of technologies for patients with Multiple Sclerosis (MS) that facilitate precision monitoring using routine MRI and clinical assessments; and contribute to realising the goal of personalised disease management. MS is a chronic inflammatory demyelinating disease of the central nervous system (CNS), characterised by focal demyelinating plaques in the brain and spinal cord; and progressive neurodegeneration. Despite success in cohort studies and clinical trials, the measurement of disease activity using conventional imaging biomarkers in real-world clinical practice is limited to qualitative assessment of lesion activity, which is time consuming and prone to human error. Quantitative measures, such as T2 lesion load, volumetric assessment of lesion activity and brain atrophy, are constrained by challenges associated with handling real-world data variances. In this thesis, DeepBVC was developed for robust brain atrophy assessment through imaging synthesis, while a lesion segmentation model was developed using a novel federated learning framework, Fed-CoT, to leverage large data collaborations. With existing quantitative brain structural analyses, this work has developed an effective deep learning analysis pipeline, which delivers a fully automated suite of MS-specific clinical imaging biomarkers to facilitate the precision monitoring of patients with MS and response to disease modifying therapy. The framework for individualised MRI-guided management in this thesis was complemented by a disease prognosis model, based on a Large Language Model, providing insights into the risks of clinical worsening over the subsequent 3 years. The value and performance of the MS biomarkers in this thesis are underpinned by extensive validation in real-world, multi-centre data from more than 1030 patients

Diagnostic and prognostic magnetic resonance studies in patients with clinically isolated syndromes.

Multiple Sclerosis (MS) is an acquired demyelinating disease of the central nervous system (CNS) which usually presents as a Clinically Isolated Syndrome (CIS). Longitudinal clinical and MRI studies of patients with CIS give insight into the clinical prognostic factors for the development of Relapsing Remitting (RR) and Secondary Progressive (SP) MS. In this MD thesis, I have discussed the clinical and MRI correlations in a group of 65 patients recruited between 1995 and 1999 and who have been followed for approximately 3 years. In Chapters 3.2 and 3.3, I evaluated baseline MRI brain and spinal cord findings as predictive tests for MS at 3 years. In 2001, the International Panel on MS diagnosis published revised criteria on the diagnosis of MS. For the first time a diagnosis of MS could be made in patients with CIS suggestive of MS using MRI for evidence of Dissemination in Space (DIS) and Dissemination in Time (DIT). The accuracy of both the new MRI and clinical criteria was evaluated at one and 3 years, in Chapter 4.1. Although the new diagnostic criteria were found to specific for MS, their sensitivity was lower. While, high specificity was achieved by the requirement of new Gadolinium enhancing lesions at a 3 month follow up scan in patients imaged initially within 3 months of the onset of symptoms, the same requirement reduced sensitivity. New T2 lesions are seen more often on the 3 month follow up scan than new Gadolinium enhancing lesions. Our next project in chapter 4.2, was to evaluate inclusion of new T2 lesions as a predictive test for MS at 3 years. Interestingly, new T2 lesions used as evidence for DIT were sensitive for a diagnosis of MS at 3 years without a loss in specificity. Finally, as an exploratory exercise in this section, we evaluated new T2 lesions, regardless of evidence of MRI DIS. New T2 lesions were both sensitive and specific for a diagnosis of MS at 3 years. Further evaluation of a new T2 lesion at 3 months together with optimum MRI evidence for DIS is therefore warranted. Brain atrophy has been evaluated as a surrogate marker in MS. The cause and timing of atrophy and its association with inflammatory MRI lesions are not clear. In chapter 5.1, Ventricular Volume (VV) was analyzed as an atrophy marker in a cohort of 55 patients followed for one year. In Chapter 5.2, Grey Matter (GM), White Matter (WM), Brain Parenchymal Fractions (BPF) and (VV) were analyzed as atrophy markers in 58 patients followed for 3 years. Significant GM atrophy and an increase in VV were seen in those who developed MS. There were moderate correlations between lesions and increase in VV and reduction in GMF and BPF. In conclusion, although imaging of the brain is extremely helpful in patients with optic neuritis in order to assign risk of MS, imaging of the spinal cord is less useful. The new diagnostic criteria for the diagnosis of MS in patients with CIS are specific. Sensitivity of the diagnostic criteria may be improved by the inclusion of new T2 lesions after 3 months as evidence of DIT. Regional atrophy affecting both GM and VV size was noted in patients with CIS, who went on to develop MS. Pathogenic process including lesions and atrophy occur in the earliest clinical stages of MS and are only partially related. It is appropriate to measure both processes in future disease modifying treatment trials in patients with CIS or early MS