Using a philosophical approach or deductive reasoning, we challenge the dominant clinico-radiological worldview that defines multiple sclerosis (MS) as a focal inflammatory disease of the central nervous system (CNS). We provide a range of evidence to argue that the ‘real MS’ is in fact driven primarily by a smouldering pathological disease process. In natural history studies and clinical trials, relapses and focal activity revealed by magnetic resonance imaging (MRI) in MS patients on placebo or on disease-modifying therapies (DMTs) were found to be poor predictors of long-term disease evolution and were dissociated from disability outcomes. In addition, the progressive accumulation of disability in MS can occur independently of relapse activity from early in the disease course. This scenario is underpinned by a more diffuse smouldering pathological process that may affect the entire CNS. Many putative pathological drivers of smouldering MS can be potentially modified by specific therapeutic strategies, an approach that may have major implications for the management of MS patients. We hypothesise that therapeutically targeting a state of ‘no evident inflammatory disease activity’ (NEIDA) cannot sufficiently prevent disability accumulation in MS, meaning that treatment should also focus on other brain and spinal cord pathological processes contributing to the slow loss of neurological function. This should also be complemented with a holistic approach to the management of other systemic disease processes that have been shown to worsen MS outcomes.
Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.
Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Non-invasive neuroimaging techniques, such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in the clinical routine to depict arteries. They are, however, only visually assessed. Fully automated vessel segmentation integrated into the clinical routine could facilitate the time-critical diagnosis of vessel abnormalities and might facilitate the identification of valuable biomarkers for cerebrovascular events. In the present work, we developed and validated a new deep learning model for vessel segmentation, coined BRAVE-NET, on a large aggregated dataset of patients with cerebrovascular diseases. Methods: BRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model developed on a dataset of 264 patients from three different studies enrolling patients with cerebrovascular diseases. A context path, dually capturing high-and low-resolution volumes, and deep supervision were implemented. The BRAVE-NET model was compared to a baseline Unet model and variants with only context paths and deep supervision, respectively. The models were developed and validated using high-quality manual labels as ground truth. Next to precision and recall, the performance was assessed quantitatively by Dice coefficient (DSC); average Hausdorff distance (AVD); 95-percentile Hausdorff distance (95HD); and via visual qualitative rating. Results: The BRAVE-NET performance surpassed the other models for arterial brain vessel segmentation with a DSC = 0.931, AVD = 0.165, and 95HD = 29.153. The BRAVE-NET model was also the most resistant toward false labelings as revealed by the visual analysis. The performance improvement is primarily attributed to the integration Hilbert et al. Fully-Automated Arterial Brain Vessel Segmentation of the multiscaling context path into the 3-D Unet and to a lesser extent to the deep supervision architectural component. Discussion: We present a new state-of-the-art of arterial brain vessel segmentation tailored to cerebrovascular pathology. We provide an extensive experimental validation of the model using a large aggregated dataset encompassing a large variability of cerebrovascular disease and an external set of healthy volunteers. The framework provides the technological foundation for improving the clinical workflow and can serve as a biomarker extraction tool in cerebrovascular diseases.
ObjectiveTo investigate serum neurofilament light chain (sNfL) as a potential biomarker for disease activity and treatment response in pediatric patients with multiple sclerosis (MS).MethodsIn this retrospective cohort study, sNfL levels were measured in a pediatric MS cohort (n = 55, follow-up 12–105 months) and in a non-neurologic pediatric control cohort (n = 301) using a high-sensitivity single-molecule array assay. Association of sNfL levels and treatment and clinical and MRI parameters were calculated.ResultsUntreated patients had higher sNfL levels than controls (median 19.0 vs 4.6 pg/mL; CI [4.732, 6.911]), p < 0.001). sNfL levels were significantly associated with MRI activity (+9.1% per contrast-enhancing lesion, CI [1.045, 1.138], p < 0.001; +0.6% per T2-weighted lesion, CI [1.001, 1.010], p = 0.015). Higher values were associated with a relapse <90 days ago (+51.1%; CI [1.184, 1.929], p < 0.001) and a higher Expanded Disability Status Scale score (CI [1.001, 1.240], p = 0.048). In patients treated with interferon beta-1a/b (n = 27), sNfL levels declined from 14.7 to 7.9 pg/mL after 6 ± 2 months (CI [0.339, 0.603], p < 0.001). Patients with insufficient control of clinical or MRI disease activity under treatment with interferon beta-1a/b or glatiramer acetate who switched to fingolimod (n = 18) showed a reduction of sNfL levels from 16.5 to 10.0 pg/mL 6 ± 2 months after switch (CI [0.481, 0.701], p < 0.001).ConclusionssNfL is a useful biomarker for monitoring disease activity and treatment response in pediatric MS. It is most likely helpful to predict disease severity and to guide treatment decisions in patients with pediatric MS. This study provides Class III evidence that sNfL levels are associated with disease activity in pediatric MS.
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