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1. Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs

Author

Durso-Finley, Joshua, Barile, Berardino, Falet, Jean-Pierre, Arnold, Douglas L., Pawlowski, Nick, and Arbel, Tal

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods
Abstract

Personalized medicine based on medical images, including predicting future individualized clinical disease progression and treatment response, would have an enormous impact on healthcare and drug development, particularly for diseases (e.g. multiple sclerosis (MS)) with long term, complex, heterogeneous evolutions and no cure. In this work, we present the first stochastic causal temporal framework to model the continuous temporal evolution of disease progression via Neural Stochastic Differential Equations (NSDE). The proposed causal inference model takes as input the patient's high dimensional images (MRI) and tabular data, and predicts both factual and counterfactual progression trajectories on different treatments in latent space. The NSDE permits the estimation of high-confidence personalized trajectories and treatment effects. Extensive experiments were performed on a large, multi-centre, proprietary dataset of patient 3D MRI and clinical data acquired during several randomized clinical trials for MS treatments. Our results present the first successful uncertainty-based causal Deep Learning (DL) model to: (a) accurately predict future patient MS disability evolution (e.g. EDSS) and treatment effects leveraging baseline MRI, and (b) permit the discovery of subgroups of patients for which the model has high confidence in their response to treatment even in clinical trials which did not reach their clinical endpoints.

Published
2024

2. A Bayesian factor analysis model for high-dimensional microbiome count data

Author

Ba, Ismaïla, Turgeon, Maxime, Veniamin, Simona, Joel, Juan, Miller, Richard, Graham, Morag, Bonner, Christine, Bernstein, Charles N., Arnold, Douglas L., Bar-Or, Amit, Marrie, Ruth Ann, O'Mahony, Julia, Yeh, E. Ann, Banwell, Brenda, Waubant, Emmanuelle, Knox, Natalie, Van Domselaar, Gary, Mirza, Ali I., Armstrong, Heather, Muthukumarana, Saman, and McGregor, Kevin

Subjects
Statistics - Methodology
Abstract

Dimension reduction techniques are among the most essential analytical tools in the analysis of high-dimensional data. Generalized principal component analysis (PCA) is an extension to standard PCA that has been widely used to identify low-dimensional features in high-dimensional discrete data, such as binary, multi-category and count data. For microbiome count data in particular, the multinomial PCA is a natural counterpart of the standard PCA. However, this technique fails to account for the excessive number of zero values, which is frequently observed in microbiome count data. To allow for sparsity, zero-inflated multivariate distributions can be used. We propose a zero-inflated probabilistic PCA model for latent factor analysis. The proposed model is a fully Bayesian factor analysis technique that is appropriate for microbiome count data analysis. In addition, we use the mean-field-type variational family to approximate the marginal likelihood and develop a classification variational approximation algorithm to fit the model. We demonstrate the efficiency of our procedure for predictions based on the latent factors and the model parameters through simulation experiments, showcasing its superiority over competing methods. This efficiency is further illustrated with two real microbiome count datasets. The method is implemented in R., Comment: 2 figures, 3 tables

Published
2024

4. Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis

Author

Shui, Changjian, Szeto, Justin, Mehta, Raghav, Arnold, Douglas L., and Arbel, Tal

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Trustworthy deployment of deep learning medical imaging models into real-world clinical practice requires that they be calibrated. However, models that are well calibrated overall can still be poorly calibrated for a sub-population, potentially resulting in a clinician unwittingly making poor decisions for this group based on the recommendations of the model. Although methods have been shown to successfully mitigate biases across subgroups in terms of model accuracy, this work focuses on the open problem of mitigating calibration biases in the context of medical image analysis. Our method does not require subgroup attributes during training, permitting the flexibility to mitigate biases for different choices of sensitive attributes without re-training. To this end, we propose a novel two-stage method: Cluster-Focal to first identify poorly calibrated samples, cluster them into groups, and then introduce group-wise focal loss to improve calibration bias. We evaluate our method on skin lesion classification with the public HAM10000 dataset, and on predicting future lesional activity for multiple sclerosis (MS) patients. In addition to considering traditional sensitive attributes (e.g. age, sex) with demographic subgroups, we also consider biases among groups with different image-derived attributes, such as lesion load, which are required in medical image analysis. Our results demonstrate that our method effectively controls calibration error in the worst-performing subgroups while preserving prediction performance, and outperforming recent baselines.

Published
2023

5. Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models

Author

Durso-Finley, Joshua, Falet, Jean-Pierre, Mehta, Raghav, Arnold, Douglas L., Pawlowski, Nick, and Arbel, Tal

Subjects
Computer Science - Machine Learning
Abstract

Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.

Published
2023

6. Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors

Author

Hu, Anjun, Falet, Jean-Pierre R., Nichyporuk, Brennan S., Shui, Changjian, Arnold, Douglas L., Tsaftaris, Sotirios A., and Arbel, Tal

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternative training configuration where we provide supervision to a subset of latent units. It is shown that (1) a partially supervised latent space achieves a higher degree of disentanglement between evidence of disease and subject-specific anatomy; (2) when the prior is formulated with an autoregressive structure, knowledge from the supervision can propagate to the unsupervised latent units, resulting in more informative latent representations capable of modelling anatomy-pathology interdependencies., Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, http://www.ml4h.cc, 11 pages

Published
2022

7. Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation

Author

Nichyporuk, Brennan, Cardinell, Jillian, Szeto, Justin, Mehta, Raghav, Falet, Jean-Pierre R., Arnold, Douglas L., Tsaftaris, Sotirios A., and Arbel, Tal

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, where unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the 'ground-truth' label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others. In this paper, we show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. To this end, we propose a generalized conditioning framework to (1) learn and account for different annotation styles across multiple datasets using a single model, (2) identify similar annotation styles across different datasets in order to permit their effective aggregation, and (3) fine-tune a fully trained model to a new annotation style with just a few samples. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.org/papers/2022:029.html

Published
2022

8. Heatmap Regression for Lesion Detection using Pointwise Annotations

Author

Myers-Colet, Chelsea, Schroeter, Julien, Arnold, Douglas L., and Arbel, Tal

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset., Comment: Accepted at Medical Image Learning with Limited & Noisy Data (MILLanD), a workshop hosted with the conference on Medical Image Computing and Computer Assisted Interventions (MICCAI) 2022

Published
2022

9. Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers

Author

Kumar, Amar, Hu, Anjun, Nichyporuk, Brennan, Falet, Jean-Pierre R., Arnold, Douglas L., Tsaftaris, Sotirios, and Arbel, Tal

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The discovery of patient-specific imaging markers that are predictive of future disease outcomes can help us better understand individual-level heterogeneity of disease evolution. In fact, deep learning models that can provide data-driven personalized markers are much more likely to be adopted in medical practice. In this work, we demonstrate that data-driven biomarker discovery can be achieved through a counterfactual synthesis process. We show how a deep conditional generative model can be used to perturb local imaging features in baseline images that are pertinent to subject-specific future disease evolution and result in a counterfactual image that is expected to have a different future outcome. Candidate biomarkers, therefore, result from examining the set of features that are perturbed in this process. Through several experiments on a large-scale, multi-scanner, multi-center multiple sclerosis (MS) clinical trial magnetic resonance imaging (MRI) dataset of relapsing-remitting (RRMS) patients, we demonstrate that our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the population level. Additional qualitative results illustrate that our model has the potential to discover novel and subject-specific predictive markers of future activity., Comment: Accepted to the MIABID workshop at MICCAI 2022

Published
2022

10. Segmentation-Consistent Probabilistic Lesion Counting

Author

Schroeter, Julien, Myers-Colet, Chelsea, Arnold, Douglas L, and Arbel, Tal

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach--which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting--is non-parametric and thus offers a robust and consistent way to augment lesion segmentation models with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion counting demonstrate that our method outputs accurate and well-calibrated count distributions that capture meaningful uncertainty information. They also reveal that our model is suitable for multi-task learning of lesion segmentation, is efficient in low data regimes, and is robust to adversarial attacks., Comment: Accepted at Medical Imaging with Deep Learning (MIDL) 2022

Published
2022

11. Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI

Author

Durso-Finley, Joshua, Falet, Jean-Pierre R., Nichyporuk, Brennan, Arnold, Douglas L., and Arbel, Tal

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients. Our model (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (e.g. side effects, patient preference, administration difficulties)., Comment: Accepted to MIDL 2022

Published
2022

12. Safety and efficacy of evobrutinib in relapsing multiple sclerosis (evolutionRMS1 and evolutionRMS2): two multicentre, randomised, double-blind, active-controlled, phase 3 trials

Author

Montalban, Xavier, Vermersch, Patrick, Arnold, Douglas L, Bar-Or, Amit, Cree, Bruce A C, Cross, Anne H, Kubala Havrdova, Eva, Kappos, Ludwig, Stuve, Olaf, Wiendl, Heinz, Wolinsky, Jerry S, Dahlke, Frank, Le Bolay, Claire, Shen Loo, Li, Gopalakrishnan, Sathej, Hyvert, Yann, Javor, Andrija, Guehring, Hans, Tenenbaum, Nadia, and Tomic, Davorka

Published
2024
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13. Effect of siponimod on magnetic resonance imaging measures of neurodegeneration and myelination in secondary progressive multiple sclerosis: Gray matter atrophy and magnetization transfer ratio analyses from the EXPAND phase 3 trial

Author

Arnold, Douglas L, Piani-Meier, Daniela, Bar-Or, Amit, Benedict, Ralph HB, Cree, Bruce AC, Giovannoni, Gavin, Gold, Ralf, Vermersch, Patrick, Arnould, Sophie, Dahlke, Frank, Hach, Thomas, Ritter, Shannon, Karlsson, Göril, Kappos, Ludwig, Fox, Robert J, and Investigators, for the EXPAND Clinical

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Neurosciences, Rare Diseases, Brain Disorders, Autoimmune Disease, Neurodegenerative, Clinical Research, Biomedical Imaging, Multiple Sclerosis, Clinical Trials and Supportive Activities, Neurological, Atrophy, Azetidines, Benzyl Compounds, Brain, Gray Matter, Humans, Magnetic Resonance Imaging, Multiple Sclerosis, Chronic Progressive, Secondary progressive multiple sclerosis, MRI, magnetization transfer ratio, gray matter, brain integrity, myelination, siponimod, EXPAND Clinical Investigators, Neurology & Neurosurgery, Clinical sciences, Biological psychology
Abstract

BackgroundMagnetic resonance imaging (MRI) measurements of gray matter (GM) atrophy and magnetization transfer ratio (MTR; correlate of myelination) may provide better insights than conventional MRI regarding brain tissue integrity/myelination in multiple sclerosis (MS).ObjectiveTo examine the effect of siponimod in the EXPAND trial on whole-brain and GM atrophy, newly formed normalized magnetization transfer ratio (nMTR) lesions, and nMTR-assessed integrity of normal-appearing brain tissue (NABT), cortical GM (cGM), and normal-appearing white matter (NAWM).MethodsPatients with secondary progressive multiple sclerosis (SPMS) received siponimod (2 mg/day; n =1037) or placebo (n = 523). Endpoints included percentage change from baseline to months 12/24 in whole-brain, cGM, and thalamic volumes; change in nMTR from baseline to months 12/24 in NABT, cGM, and NAWM; MTR recovery in newly formed lesions.ResultsCompared with placebo, siponimod significantly reduced progression of whole-brain and GM atrophy over 12/24 months, and was associated with improvements in brain tissue integrity/myelination within newly formed nMTR lesions and across NABT, cGM, and NAWM over 24 months. Effects were consistent across age, disease duration, inflammatory activity subgroups, and disease severity.ConclusionSiponimod reduced brain tissue damage in patients with SPMS as evidenced by objective measures of brain tissue integrity/myelination. This is consistent with central nervous system (CNS) effects observed in preclinical models. ClinicalTrials.gov number: NCT01665144.

Published
2022

14. Siponimod vs placebo in active secondary progressive multiple sclerosis: a post hoc analysis from the phase 3 EXPAND study

Author

Gold, Ralf, Piani-Meier, Daniela, Kappos, Ludwig, Bar-Or, Amit, Vermersch, Patrick, Giovannoni, Gavin, Fox, Robert J, Arnold, Douglas L, Benedict, Ralph HB, Penner, Iris-Katharina, Rouyrre, Nicolas, Kilaru, Ajay, Karlsson, Göril, Ritter, Shannon, Dahlke, Frank, Hach, Thomas, and Cree, Bruce AC

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Neurosciences, Brain Disorders, Biomedical Imaging, Clinical Research, Clinical Trials and Supportive Activities, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Azetidines, Benzyl Compounds, Disease Progression, Humans, Magnetic Resonance Imaging, Multiple Sclerosis, Multiple Sclerosis, Chronic Progressive, Multiple Sclerosis, Relapsing-Remitting, Siponimod, EXPAND, Active secondary progressive multiple sclerosis, Disability progression, MRI, Cognition, Neurology & Neurosurgery, Clinical sciences
Abstract

BackgroundSiponimod is a sphingosine 1-phosphate receptor modulator approved for active secondary progressive multiple sclerosis (aSPMS) in most countries; however, phase 3 EXPAND study data are from an SPMS population with/without disease activity. A need exists to characterize efficacy/safety of siponimod in aSPMS.MethodsPost hoc analysis of participants with aSPMS (≥ 1 relapse in 2 years before study and/or ≥ 1 T1 gadolinium-enhancing [Gd +] magnetic resonance imaging [MRI] lesions at baseline) receiving oral siponimod (2 mg/day) or placebo for up to 3 years in EXPAND.Endpoints3-month/6-month confirmed disability progression (3mCDP/6mCDP); 3-month confirmed ≥ 20% worsening in Timed 25-Foot Walk (T25FW); 6-month confirmed improvement/worsening in Symbol Digit Modalities Test (SDMT) scores (≥ 4-point change); T2 lesion volume (T2LV) change from baseline; number of T1 Gd + lesions baseline-month 24; number of new/enlarging (N/E) T2 lesions over all visits.ResultsData from 779 participants with aSPMS were analysed. Siponimod reduced risk of 3mCDP/6mCDP vs placebo (by 31%/37%, respectively; p

Published
2022

15. Long-term efficacy and safety of siponimod in patients with secondary progressive multiple sclerosis: Analysis of EXPAND core and extension data up to >5 years

Author

Cree, Bruce AC, Arnold, Douglas L, Fox, Robert J, Gold, Ralf, Vermersch, Patrick, Benedict, Ralph HB, Bar-Or, Amit, Piani-Meier, Daniela, Rouyrre, Nicolas, Ritter, Shannon, Kilaru, Ajay, Karlsson, Goeril, Giovannoni, Gavin, and Kappos, Ludwig

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Brain Disorders, Multiple Sclerosis, Autoimmune Disease, Biomedical Imaging, Clinical Trials and Supportive Activities, Neurosciences, Neurodegenerative, Aging, Clinical Research, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Neurological, Humans, Atrophy, Azetidines, Benzyl Compounds, Multiple Sclerosis, Chronic Progressive, Multiple Sclerosis, Relapsing-Remitting, Recurrence, Confirmed disability progression, confirmed cognitive worsening, cortical gray matter, secondary progressive multiple sclerosis, siponimod, S1P modulator, Neurology & Neurosurgery, Clinical sciences, Biological psychology
Abstract

BackgroundSiponimod significantly reduced the risk of confirmed disability progression (CDP), worsening in cognitive processing speed (CPS), relapses, and magnetic resonance imaging (MRI) measures of brain atrophy and inflammation versus placebo in secondary progressive multiple sclerosis (SPMS) patients in the Phase 3 EXPAND study.ObjectiveThe aim of this study was to assess long-term efficacy and safety of siponimod 2 mg/day from the EXPAND study including the extension part, up to > 5 years.MethodsIn the open-label extension part, participants receiving placebo during the core part were switched to siponimod (placebo-siponimod group) and those on siponimod continued the same treatment (continuous siponimod group).ResultsContinuous siponimod reduced the risk of 6-month CDP by 22% (hazard ratio (HR) (95% confidence interval (CI)): 0.78 (0.66-0.92) p = 0.0026) and 6-month confirmed worsening in CPS by 23% (HR (95% CI): 0.77 (0.65-0.92) p = 0.0047) versus the placebo-siponimod group. Sustained efficacy on annualized relapse rate, total and regional brain atrophy, and inflammatory disease activity was also observed. No new, unexpected safety signals for siponimod were identified over the long term.ConclusionThe sustained efficacy and consistent long-term safety profile of siponimod up to > 5 years support its clinical utility for long-term treatment of SPMS. Benefits in the continuous siponimod versus placebo-siponimod group highlight the significance of earlier treatment initiation.Trial registration numberNCT01665144.

Published
2022

16. Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation

Author

Nichyporuk, Brennan, Cardinell, Jillian, Szeto, Justin, Mehta, Raghav, Tsaftaris, Sotirios, Arnold, Douglas L., and Arbel, Tal

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Many automatic machine learning models developed for focal pathology (e.g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient cohorts, impeding their widespread adoption into real clinical contexts. One strategy to create a more diverse, generalizable training set is to naively pool datasets from different cohorts. Surprisingly, training on this \it{big data} does not necessarily increase, and may even reduce, overall performance and model generalizability, due to the existence of cohort biases that affect label distributions. In this paper, we propose a generalized affine conditioning framework to learn and account for cohort biases across multi-source datasets, which we call Source-Conditioned Instance Normalization (SCIN). Through extensive experimentation on three different, large scale, multi-scanner, multi-centre Multiple Sclerosis (MS) clinical trial MRI datasets, we show that our cohort bias adaptation method (1) improves performance of the network on pooled datasets relative to naively pooling datasets and (2) can quickly adapt to a new cohort by fine-tuning the instance normalization parameters, thus learning the new cohort bias with only 10 labelled samples., Comment: Accepted at DART 2021

Published
2021

17. Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting

Author

Nichyporuk, Brennan, Szeto, Justin, Arnold, Douglas L., and Arbel, Tal

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patient images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images., Comment: Accepted at MIDL 2021

Published
2021

18. Metagenomic Analysis of the Pediatric-Onset Multiple Sclerosis Gut Microbiome

Author

Mirza, Ali I, Zhu, Feng, Knox, Natalie, Forbes, Jessica D, Van Domselaar, Gary, Bernstein, Charles N, Graham, Morag, Marrie, Ruth Ann, Hart, Janace, Yeh, E Ann, Arnold, Douglas L, Bar-Or, Amit, O'Mahony, Julia, Zhao, Yinshan, Hsiao, William, Banwell, Brenda, Waubant, Emmanuelle, and Tremlett, Helen

Subjects
Pediatric, Neurodegenerative, Clinical Research, Autoimmune Disease, Digestive Diseases, Neurosciences, Brain Disorders, Genetics, Multiple Sclerosis, Neurological, Adolescent, Adult, Canada, Child, Female, Gastrointestinal Microbiome, Humans, Young Adult, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

Background and objectivesLittle is known of the functional potential of the gut microbiome in pediatric-onset multiple sclerosis (MS). We performed metagenomic analyses using stool samples from individuals with pediatric-onset MS and unaffected controls.MethodsPersons ≤21 years old enrolled in the Canadian Pediatric Demyelinating Disease Network providing a stool sample were eligible. Twenty patients with MS (McDonald criteria) with symptom onset 0.20), but metabolic pathways, gene annotations, and microbial taxonomy did. Individuals with MS (vs controls) exhibited higher methanogenesis prevalence (odds ratio 10, p = 0.044) and Methanobrevibacter abundance (log2 fold change [LFC] 1.7, p = 0.0014), but lower homolactic fermentation abundance (LFC -0.48, p = 0.039). Differences by DMD status included lower phosphate butyryl transferase for DMD-naive vs exposed patients with MS (LFC -1.0, p = 0.033).DiscussionThe gut microbiome's functional potential and taxonomy differed between individuals with pediatric-onset MS vs controls, including higher prevalence of a methane-producing pathway from Archaea and depletion of the lactate fermentation pathway. DMD exposure was associated with butyrate-producing enzyme enrichment. Together these findings indicate that the gut microbiome of individuals with MS may have a disturbed functional potential.

Published
2022

19. Prognostic Value of Serum Neurofilament Light Chain for Disease Activity and Worsening in Patients With Relapsing Multiple Sclerosis: Results From the Phase 3 ASCLEPIOS I and II Trials

Author

Ziemssen, Tjalf, Arnold, Douglas L, Alvarez, Enrique, Cross, Anne H, Willi, Roman, Li, Bingbing, Kukkaro, Petra, Kropshofer, Harald, Ramanathan, Krishnan, Merschhemke, Martin, Kieseier, Bernd, Su, Wendy, Häring, Dieter A, Hauser, Stephen L, Kappos, Ludwig, and Kuhle, Jens

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Neurosciences, Clinical Trials and Supportive Activities, Gray Matter, Humans, Intermediate Filaments, Multiple Sclerosis, Prognosis, Recurrence, serum neurofilament light chain, prognostic biomarker, MS disease activity, lesion formation, brain atrophy, Immunology, Medical Microbiology, Biochemistry and cell biology, Genetics
Abstract

ObjectiveThis study aims to confirm the prognostic value of baseline serum neurofilament light chain (sNfL) for on-study disease activity and worsening in patients with relapsing MS (RMS).BackgroundPrevious post-hoc studies suggested that sNfL could be a prognostic biomarker in RMS. In the phase 3 ASCLEPIOS I/II trials in which ofatumumab demonstrated better efficacy outcomes than teriflunomide, treatment with ofatumumab also led to significantly reduced sNfL levels compared to teriflunomide treatment.Design/methodsIn this study, we report protocol-planned analyses from the pooled ASCLEPIOS I/II trials (N=1882). Per protocol, patients were stratified by median baseline sNfL levels (9.3 pg/ml) into high (>median) and low (≤median) categories to prognosticate: annualized rate of new/enlarging T2 (neT2) lesions in year 1 and 2, annualized relapse rate, annual percentage change in whole brain (WB) and regional brain volume [thalamus, white matter (WM), cortical gray matter (cGM)], and disability outcomes. Similar analyses were performed for the recently diagnosed (within 3 years), treatment-naive patients (no prior disease-modifying therapy) subgroup.ResultsHigh versus low sNfL at baseline was prognostic of increased on-study T2 lesion formation at year 1 (relative increase: ofatumumab +158%; teriflunomide +69%, both p

Published
2022

20. Plasma neurofilament light chain concentrations as a biomarker of clinical and radiologic outcomes in relapsing multiple sclerosis: Post hoc analysis of Phase 3 ozanimod trials

Author

Harris, Sarah, Comi, Giancarlo, Cree, Bruce AC, Arnold, Douglas L, Steinman, Lawrence, Sheffield, James K, Southworth, Harry, Kappos, Ludwig, Cohen, Jeffrey A, and Investigators, the Ozanimod Study

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Clinical Research, Clinical Trials and Supportive Activities, Cancer, Biomarkers, Double-Blind Method, Humans, Indans, Interferon beta-1a, Intermediate Filaments, Magnetic Resonance Imaging, Multiple Sclerosis, Multiple Sclerosis, Relapsing-Remitting, Oxadiazoles, Recurrence, blood biomarkers, multiple sclerosis, neurofilament light, relapse, treatment outcome, Ozanimod Study Investigators, Neurology & Neurosurgery, Clinical sciences
Abstract

Background and purposeWe investigated plasma neurofilament light chain concentration (pNfL) as a biomarker for neuroaxonal damage and disease activity using data from Phase 3 trials of ozanimod in relapsing multiple sclerosis (RMS).MethodspNfL was measured before and after ozanimod 0.46 mg or 0.92 mg daily or interferon β-1a 30 µg weekly in the randomized, double-blind SUNBEAM and RADIANCE trials. In these post hoc analyses, we investigated relationships between pNfL (at baseline and median percentage change from baseline to Month 12 [SUNBEAM] or 24 [RADIANCE]) and clinical and magnetic resonance imaging outcomes.ResultsMedian (Q1, Q3) baseline pNfL, available in 1244 of 1346 SUNBEAM participants, was 14.70 (10.16, 23.26) pg/ml and in 1109 of 1313 RADIANCE participants was 13.35 (9.42, 20.41) pg/ml. Baseline gadolinium-enhancing (GdE) and T2 lesion counts increased and brain volume decreased with increasing baseline pNfL. Baseline pNfL was higher in those with versus without on-treatment relapse. Median percentage reduction in pNfL at 12 months in SUNBEAM (n = 1238) and 24 months in RADIANCE (n = 1088) was greater for ozanimod (20%-27%) than interferon β-1a (13%-16%; p

Published
2021

22. Secondary Progressive Multiple Sclerosis: New Insights

Author

Cree, Bruce AC, Arnold, Douglas L, Chataway, Jeremy, Chitnis, Tanuja, Fox, Robert J, Pozo Ramajo, Angela, Murphy, Niamh, and Lassmann, Hans

Subjects
Multiple Sclerosis, Autoimmune Disease, Neurodegenerative, Neurosciences, Brain Disorders, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Neurological, Biomarkers, Disease Progression, Humans, Immunologic Factors, Multiple Sclerosis, Chronic Progressive, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

In most cases, multiple sclerosis (MS) begins with a relapsing-remitting course followed by insidious disability worsening that is independent from clinically apparent relapses and is termed secondary progressive MS (SMPS). Major differences exist between relapsing-remitting MS (RRMS) and SPMS, especially regarding therapeutic response to treatment. This review provides an overview of the pathology, differentiation, and challenges in the diagnosis and treatment of SPMS. We emphasize the criticality of conversion from a relapsing-remitting to a secondary progressive disease course not only because such conversion is evidence of disability progression, but also because, until recently, treatments that effectively reduced disability progression in relapsing MS were not proven to be effective in SPMS. Clear clinical, imaging, immunologic, or pathologic criteria marking the transition from RRMS to SPMS have not yet been established. Early identification of SPMS will require tools that, together with the use of appropriate treatments, may result in better long-term outcomes for the population of patients with SPMS.

Published
2021

25. Five-years of ocrelizumab in relapsing multiple sclerosis: OPERA studies open-label extension

Author

Hauser, Stephen L, Kappos, Ludwig, Arnold, Douglas L, Bar-Or, Amit, Brochet, Bruno, Naismith, Robert T, Traboulsee, Anthony, Wolinsky, Jerry S, Belachew, Shibeshih, Koendgen, Harold, Levesque, Victoria, Manfrini, Marianna, Model, Fabian, Hubeaux, Stanislas, Mehta, Lahar, and Montalban, Xavier

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Neurosciences, Brain Disorders, Clinical Trials and Supportive Activities, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Neurological, Adult, Antibodies, Monoclonal, Humanized, Brain, Female, Follow-Up Studies, Humans, Immunologic Factors, Magnetic Resonance Imaging, Male, Multiple Sclerosis, Relapsing-Remitting, Neuroimaging, Randomized Controlled Trials as Topic, Recurrence, Time Factors, Treatment Outcome, Cognitive Sciences, Neurology & Neurosurgery, Clinical sciences
Abstract

ObjectiveTo assess over 3 years of follow-up the effects of maintaining or switching to ocrelizumab (OCR) therapy on clinical and MRI outcomes and safety measures in the open-label extension (OLE) phase of the pooled OPERA: I/II studies in relapsing multiple sclerosis.MethodsAfter 2 years of double-blind, controlled treatment, patients continued OCR (600 mg infusions every 24 weeks) or switched from interferon (IFN)-β-1a (44 μg 3 times weekly) to OCR when entering the OLE phase (3 years). Adjusted annualized relapse rate, time to onset of 24-week confirmed disability progression (CDP)/improvement (CDP), brain MRI activity (gadolinium-enhanced and new/enlarging T2 lesions), and percentage brain volume change were analyzed.ResultsOf patients entering the OLE phase, 88.6% completed year 5. The cumulative proportion with 24-week CDP was lower in patients who initiated OCR earlier vs patients initially receiving IFN-β-1a (16.1% vs 21.3% at year 5; p = 0.014). Patients continuing OCR maintained and those switching from IFN-β-1a to OCR attained near complete and sustained suppression of new brain MRI lesion activity from years 3-5. Over the OLE phase, patients continuing OCR exhibited less whole brain volume loss from double-blind study baseline vs those switching from IFN-β-1a (-1.87% vs -2.15% at year 5; p < 0.01). Adverse events were consistent with past reports and no new safety signals emerged with prolonged treatment.ConclusionCompared with patients switching from IFN-β-1a, earlier and continuous OCR treatment up to 5 years provided sustained benefit on clinical and MRI measures of disease progression.Classification of evidenceThis study provides Class III evidence that earlier and continuous treatment with OCR provided sustained benefit on clinical and MRI outcomes of disease activity and progression compared with patients switching from IFN-β-1a. The study is rated Class III because of the initial treatment randomization disclosure that occurred after inclusion in OLE.Clinical trial identifiersNCT01247324/NCT01412333.

Published
2020

26. Exploratory clinical efficacy and patient-reported outcomes from NOVA: A randomized controlled study of intravenous natalizumab 6-week dosing versus continued 4-week dosing for relapsing-remitting multiple sclerosis

Author

Ryerson, Lana Zhovtis, Foley, John F, Defer, Gilles, Cohen, Jeffrey A, Arnold, Douglas L, Butzkueven, Helmut, Cutter, Gary, Giovannoni, Gavin, Killestein, Joep, Wiendl, Heinz, Sinks, Susie, Kuhelj, Robert, Bodhinathan, Karthik, and Lasky, Tyler

Published
2023
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29. Multiple Sclerosis

Author

Ludwin, Samuel K., Antel, Jack, Arnold, Douglas L., Pfaff, Donald W., editor, Volkow, Nora D., editor, and Rubenstein, John L., editor

Published
2022
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31. Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation

Author

Nair, Tanya, Precup, Doina, Arnold, Douglas L., and Arbel, Tal

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning (DL) networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets [3,11,16], especially for large pathologies. However, in the context of diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions visible on MRI sequences, even very small ones, is essential for disease staging, prognosis, and evaluating treatment efficacy. Moreover, producing deterministic outputs hinders DL adoption into clinical routines. Uncertainty estimates for the predictions would permit subsequent revision by clinicians. We present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout [4] in the context of deep networks for lesion detection and segmentation in medical images. Specifically, we develop a 3D MS lesion segmentation CNN, augmented to provide four different voxel-based uncertainty measures based on MC dropout. We train the network on a proprietary, large-scale, multi-site, multi-scanner, clinical MS dataset, and compute lesion-wise uncertainties by accumulating evidence from voxel-wise uncertainties within detected lesions. We analyze the performance of voxel-based segmentation and lesion-level detection by choosing operating points based on the uncertainty. Empirical evidence suggests that uncertainty measures consistently allow us to choose superior operating points compared only using the network's sigmoid output as a probability., Comment: Updated references in Introduction; Accepted to the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018)

Published
2018

32. Slowly expanding/evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions

Author

Elliott, Colm, Wolinsky, Jerry S, Hauser, Stephen L, Kappos, Ludwig, Barkhof, Frederik, Bernasconi, Corrado, Wei, Wei, Belachew, Shibeshih, and Arnold, Douglas L

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Multiple Sclerosis, Neurodegenerative, Brain Disorders, Clinical Research, Autoimmune Disease, Neurosciences, Detection, screening and diagnosis, 2.1 Biological and endogenous factors, Aetiology, 4.2 Evaluation of markers and technologies, Neurological, Adult, Brain, Contrast Media, Disease Progression, Female, Gadolinium, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Multiple Sclerosis, Chronic Progressive, Multiple Sclerosis, Relapsing-Remitting, White Matter, Chronic active lesions, progressive multiple sclerosis, relapsing multiple sclerosis, slowly expanding/evolving lesions, smoldering plaques, Neurology & Neurosurgery, Clinical sciences, Biological psychology
Abstract

BACKGROUND:Chronic lesion activity driven by smoldering inflammation is a pathological hallmark of progressive forms of multiple sclerosis (MS). OBJECTIVE:To develop a method for automatic detection of slowly expanding/evolving lesions (SELs) on conventional brain magnetic resonance imaging (MRI) and characterize such SELs in primary progressive MS (PPMS) and relapsing MS (RMS) populations. METHODS:We defined SELs as contiguous regions of existing T2 lesions showing local expansion assessed by the Jacobian determinant of the deformation between reference and follow-up scans. SEL candidates were assigned a heuristic score based on concentricity and constancy of change in T2- and T1-weighted MRIs. SELs were examined in 1334 RMS patients and 555 PPMS patients. RESULTS:Compared with RMS patients, PPMS patients had higher numbers of SELs (p = 0.002) and higher T2 volumes of SELs (p

Published
2019

33. Chronic white matter lesion activity predicts clinical progression in primary progressive multiple sclerosis

Author

Elliott, Colm, Belachew, Shibeshih, Wolinsky, Jerry S, Hauser, Stephen L, Kappos, Ludwig, Barkhof, Frederik, Bernasconi, Corrado, Fecker, Julian, Model, Fabian, Wei, Wei, and Arnold, Douglas L

Subjects
Clinical Research, Biomedical Imaging, Neurosciences, Clinical Trials and Supportive Activities, Neurodegenerative, Autoimmune Disease, Multiple Sclerosis, Brain Disorders, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Neurological, Adult, Antibodies, Monoclonal, Humanized, Disease Progression, Double-Blind Method, Female, Humans, Immunologic Factors, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Multiple Sclerosis, Chronic Progressive, Predictive Value of Tests, White Matter, MS: imaging, MS: biomarkers, MS: clinical trials, neuroinflammation, white matter lesion, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

Chronic active and slowly expanding lesions with smouldering inflammation are neuropathological correlates of progressive multiple sclerosis pathology. T1 hypointense volume and signal intensity on T1-weighted MRI reflect brain tissue damage that may develop within newly formed acute focal inflammatory lesions or in chronic pre-existing lesions without signs of acute inflammation. Using a recently developed method to identify slowly expanding/evolving lesions in vivo from longitudinal conventional T2- and T1-weighted brain MRI scans, we measured the relative amount of chronic lesion activity as measured by change in T1 volume and intensity within slowly expanding/evolving lesions and non-slowly expanding/evolving lesion areas of baseline pre-existing T2 lesions, and assessed the effect of ocrelizumab on this outcome in patients with primary progressive multiple sclerosis participating in the phase III, randomized, placebo-controlled, double-blind ORATORIO study (n = 732, NCT01194570). We also assessed the predictive value of T1-weighted measures of chronic lesion activity for clinical multiple sclerosis progression as reflected by a composite disability measure including the Expanded Disability Status Scale, Timed 25-Foot Walk and 9-Hole Peg Test. We observed in this clinical trial population that most of total brain non-enhancing T1 hypointense lesion volume accumulation was derived from chronic lesion activity within pre-existing T2 lesions rather than new T2 lesion formation. There was a larger decrease in mean normalized T1 signal intensity and greater relative accumulation of T1 hypointense volume in slowly expanding/evolving lesions compared with non-slowly expanding/evolving lesions. Chronic white matter lesion activity measured by longitudinal T1 hypointense lesion volume accumulation in slowly expanding/evolving lesions and in non-slowly expanding/evolving lesion areas of pre-existing lesions predicted subsequent composite disability progression with consistent trends on all components of the composite. In contrast, whole brain volume loss and acute lesion activity measured by longitudinal T1 hypointense lesion volume accumulation in new focal T2 lesions did not predict subsequent composite disability progression in this trial at the population level. Ocrelizumab reduced longitudinal measures of chronic lesion activity such as T1 hypointense lesion volume accumulation and mean normalized T1 signal intensity decrease both within regions of pre-existing T2 lesions identified as slowly expanding/evolving and in non-slowly expanding/evolving lesions. Using conventional brain MRI, T1-weighted intensity-based measures of chronic white matter lesion activity predict clinical progression in primary progressive multiple sclerosis and may qualify as a longitudinal in vivo neuroimaging correlate of smouldering demyelination and axonal loss in chronic active lesions due to CNS-resident inflammation and/or secondary neurodegeneration across the multiple sclerosis disease continuum.

Published
2019

34. Efficacy and safety of ozanimod in multiple sclerosis: Dose-blinded extension of a randomized phase II study

Author

Cohen, Jeffrey A, Comi, Giancarlo, Arnold, Douglas L, Bar-Or, Amit, Selmaj, Krzysztof W, Steinman, Lawrence, Havrdová, Eva K, Cree, Bruce AC, Montalbán, Xavier, Hartung, Hans-Peter, Huang, Vivian, Frohna, Paul, Skolnick, Brett E, Kappos, Ludwig, and Investigators, for the RADIANCE Trial

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Trials and Supportive Activities, Neurosciences, Multiple Sclerosis, Brain Disorders, Clinical Research, Neurodegenerative, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Adolescent, Adult, Double-Blind Method, Female, Humans, Indans, Magnetic Resonance Imaging, Male, Middle Aged, Multiple Sclerosis, Relapsing-Remitting, Outcome Assessment, Health Care, Oxadiazoles, Sphingosine 1 Phosphate Receptor Modulators, Young Adult, Clinical trial, disease-modifying therapies, MRI, relapsing, remitting, T2 lesions, multiple sclerosis, RADIANCE Trial Investigators, relapsing/remitting, Neurology & Neurosurgery, Clinical sciences, Biological psychology
Abstract

BackgroundOzanimod, an oral immunomodulator, selectively targets sphingosine 1-phosphate receptors 1 and 5.ObjectiveEvaluate efficacy, safety, and tolerability of ozanimod in relapsing multiple sclerosis.MethodsIn the RADIANCE Part A phase II study (NCT01628393), participants with relapsing multiple sclerosis were randomized (1:1:1) to once-daily ozanimod hydrochloride (0.5 or 1 mg) or placebo. After 24 weeks, participants could enter a 2-year, dose-blinded extension. Ozanimod-treated participants continued their assigned dose; placebo participants were re-randomized (1:1) to ozanimod hydrochloride 0.5 or 1 mg (equivalent to ozanimod 0.46 and 0.92 mg).ResultsA total of 223 (89.6%) of the 249 participants completed the blinded extension. At 2 years of the extension, the percentage of participants who were gadolinium-enhancing lesion-free ranged from 86.5% to 94.6%. Unadjusted annualized relapse rate during the blinded extension (week 24-end of treatment) was 0.32 for ozanimod hydrochloride 0.5 mg → ozanimod hydrochloride 0.5 mg, 0.18 for ozanimod hydrochloride 1 mg → ozanimod hydrochloride 1 mg, 0.30 for placebo → ozanimod hydrochloride 0.5 mg, and 0.18 for placebo → ozanimod hydrochloride 1 mg. No second-degree or higher atrioventricular block or serious opportunistic infection was reported.ConclusionOzanimod demonstrated sustained efficacy in participants continuing treatment up to 2 years and reached similar efficacy in participants who switched from placebo; no unexpected safety signals emerged.

Published
2019

35. Imaging outcome measures of neuroprotection and repair in MS: A consensus statement from NAIMS.

Author

Oh, Jiwon, Ontaneda, Daniel, Azevedo, Christina, Klawiter, Eric C, Absinta, Martina, Arnold, Douglas L, Bakshi, Rohit, Calabresi, Peter A, Crainiceanu, Ciprian, Dewey, Blake, Freeman, Leorah, Gauthier, Susan, Henry, Roland, Inglese, Mathilde, Kolind, Shannon, Li, David KB, Mainero, Caterina, Menon, Ravi S, Nair, Govind, Narayanan, Sridar, Nelson, Flavia, Pelletier, Daniel, Rauscher, Alexander, Rooney, William, Sati, Pascal, Schwartz, Daniel, Shinohara, Russell T, Tagge, Ian, Traboulsee, Anthony, Wang, Yi, Yoo, Youngjin, Yousry, Tarek, Zhang, Yunyan, Sicotte, Nancy L, and Reich, Daniel S

Subjects
Brain Disorders, Neurodegenerative, Clinical Research, Biomedical Imaging, Multiple Sclerosis, Clinical Trials and Supportive Activities, Autoimmune Disease, Neurosciences, Neurological, Brain, Diffusion Tensor Imaging, Humans, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Multimodal Imaging, Nerve Regeneration, Neuroprotection, Outcome Assessment, Health Care, Positron-Emission Tomography, Spinal Cord, North American Imaging in Multiple Sclerosis Cooperative, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

ObjectiveTo summarize current and emerging imaging techniques that can be used to assess neuroprotection and repair in multiple sclerosis (MS), and to provide a consensus opinion on the potential utility of each technique in clinical trial settings.MethodsClinicians and scientists with expertise in the use of MRI in MS convened in Toronto, Canada, in November 2016 at a North American Imaging in Multiple Sclerosis (NAIMS) Cooperative workshop meeting. The discussion was compiled into a manuscript and circulated to all NAIMS members in attendance. Edits and feedback were incorporated until all authors were in agreement.ResultsA wide spectrum of imaging techniques and analysis methods in the context of specific study designs were discussed, with a focus on the utility and limitations of applying each technique to assess neuroprotection and repair. Techniques were discussed under specific themes, and included conventional imaging, magnetization transfer ratio, diffusion tensor imaging, susceptibility-weighted imaging, imaging cortical lesions, magnetic resonance spectroscopy, PET, advanced diffusion imaging, sodium imaging, multimodal techniques, imaging of special regions, statistical considerations, and study design.ConclusionsImaging biomarkers of neuroprotection and repair are an unmet need in MS. There are a number of promising techniques with different strengths and limitations, and selection of a specific technique will depend on a number of factors, notably the question the trial seeks to answer. Ongoing collaborative efforts will enable further refinement and improved methods to image the effect of novel therapeutic agents that exert benefit in MS predominately through neuroprotective and reparative mechanisms.

Published
2019

36. Comparison of switching to 6-week dosing of natalizumab versus continuing with 4-week dosing in patients with relapsing-remitting multiple sclerosis (NOVA): a randomised, controlled, open-label, phase 3b trial

Author

van der Walt, Anneke, Dwyer, Christopher, Buzzard, Katherine, Spies, Judith, Parratt, John, van Pesch, Vincent, Willekens, Barbara, Perrotta, Gaetano, Bartholomé, Emmanuel, Grand'Maison, Francois, Jacques, Francois, Giacomini, Paul, Vosoughi, Reza, Girard, Jean-Marc, de Seze, Jerome, Lebrun Frenay, Christine, Ruet, Aurelie, Laplaud, David-Axel, Reifschneider, Gerd, Wagner, Bert, Rauer, Sebastian, Pul, Refik, Seipelt, Maria, Berthele, Achim, Klotz, Luisa, Kallmann, Boris-Alexander, Paul, Friedemann, Achiron, Anat, Lus, Giacomo, Centonze, Diego, Patti, Francesco, Grimaldi, Luigi, Hupperts, Raymond, Frequin, Stephan, Fermont, Jiske, Madueno, Sara Eichau, Alonso Torres, Ana Maria, Costa-Frossard França, Lucienne, Meca-Lallana, Jose Eustasio, Ruiz, Luis Brieva, Pearson, Owen, Rog, David, Evangelou, Nikolaos, Ismail, Azza, Lathi, Ellen, Fox, Edward, Leist, Thomas, Sloane, Jacob, Wu, Gregory, Khatri, Bhupendra, Steingo, Brian, Thrower, Ben, Gudesblatt, Mark, Calkwood, Jonathan, Bandari, Daniel, Scagnelli, John, Laganke, Christopher, Robertson, Derrick, Kipp, Lucas, Belkin, Martin, Cohan, Stanley, Goldstick, Lawrence, Courtney, Ardith, Vargas, Wendy, Sylvester, Andrew, Srinivasan, Jayshri, Kannan, Meena, Picone, Maryann, English, Jeffrey, Napoli, Salvatore, Balabanov, Roumen, Zaydan, Islam, Nicholas, Jacqueline, Kaplan, Jeffrey, Lublin, Fred, Riser, Emily, Miller, Tamara, Alvarez, Enrique, Wray, Sibyl, Gross, Jeffrey, Pawate, Siddharama, Hersh, Carrie, McCarthy, Lucas, Crayton, Heidi, Graves, Jennifer, Foley, John F, Defer, Gilles, Ryerson, Lana Zhovtis, Cohen, Jeffrey A, Arnold, Douglas L, Butzkueven, Helmut, Cutter, Gary, Giovannoni, Gavin, Killestein, Joep, Wiendl, Heinz, Smirnakis, Karen, Xiao, Shan, Kong, George, Kuhelj, Robert, and Campbell, Nolan

Published
2022
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37. Efficacy and Safety Outcomes with Diroximel Fumarate After Switching from Prior Therapies or Continuing on DRF: Results from the Phase 3 EVOLVE-MS-1 Study

Author

Wray, Sibyl, Then Bergh, Florian, Wundes, Annette, Arnold, Douglas L., Drulovic, Jelena, Jasinska, Elzbieta, Bowen, James D., Negroski, Donald, Naismith, Robert T., Hunter, Samuel F., Gudesblatt, Mark, Chen, Hailu, Lyons, Jennifer, Shankar, Sai L., Kapadia, Shivani, Mendoza, Jason P., and Singer, Barry A.

Published
2022
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39. Slowly Expanding Lesions Differentiate Pediatric Multiple Sclerosis from Myelin Oligodendrocyte Glycoprotein Antibody Disease.

Author

Fadda, Giulia, Banwell, Brenda, Elliott, Colm, Fetco, Dumitru, Arnold, Douglas L., Waters, Patrick, Yeh, E. Ann, Marrie, Ruth Ann, Bar‐Or, Amit, and Narayanan, Sridar

Subjects
MYELIN oligodendrocyte glycoprotein, DEMYELINATION, MULTIPLE sclerosis, ADULTS, IMMUNOGLOBULINS
Abstract

Slowly expanding lesions (SELs) in adults with multiple sclerosis (MS) indicate a progressive pathological process. Whether SELs are present in pediatric‐onset MS (POMS) or myelin oligodendrocyte glycoprotein antibody‐associated disease (MOGAD) is unknown. We studied 19 children with POMS and 14 with MOGAD (median age 14.3 and 9.4 years, respectively) recruited to the Canadian Pediatric Demyelinating Disease Study with: (1) ≥3 research scans 12 months apart; and (2) ≥1 T2‐lesions on the earliest scan. A total of 70 SELs from 16 POMS participants and 1 SEL in the MOGAD group were detected. SELs are an early feature of POMS and essentially not a feature of MOGAD. ANN NEUROL 2024;96:1086–1091 [ABSTRACT FROM AUTHOR]

Published
2024
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42. Prognostic factors for worsening and improvement in multiple sclerosis using a multistate model.

Author

Ocampo, Alex, Hatami, Farhad, Čuklina, Jelena, Graham, Gordon, Ganjgahi, Habib, Sun, Yang, Su, Wendy, Mousseau, Marie-Catherine, Gardiner, Stephen, Pendleton, Samantha C, Aarden, Piet, Kieseier, Bernd C, Arnold, Douglas L, Bermel, Robert A, Häring, Dieter A, Nichols, Thomas E, and Wiendl, Heinz

Subjects
BRAIN damage, PROGNOSIS, DATABASES, MULTIPLE sclerosis, DISABILITIES
Abstract

Background: The long-term disease trajectory of people living with multiple sclerosis (MS) can be improved by initiating efficacious treatment early. More quantitative evidence is needed on factors that affect a patient's risk of disability worsening or possibility of improvement to inform timely treatment decisions. Methods: We developed a multistate model to quantify the influence of demographic, clinical, and imaging factors on disability worsening and disability improvement simultaneously across the disability spectrum as measured by the Expanded Disability Status Scale (EDSS). We used clinical trial data from the Novartis–Oxford MS database including ~130,000 EDSS assessments from ~8000 patients, spanning all MS phenotypes. Results: Higher brain volume was positively associated with disability improvement at all disability levels (hazard ratio (HR) = 1.09–1.19; 95% credible interval (CI) = 1.02–1.27). Higher T2 lesion volume was negatively associated with disability improvement up to EDSS 6 (HR = 0.80–0.89; 95% CI = 0.75–0.94). Older age, time since first symptoms, and the number of relapses in the past year were confirmed as predictors of future disability worsening. Conclusions: Brain damage was identified as the most consistent factor limiting the patient's probability for improvements from the earliest stages and across the whole course of MS. Protecting brain integrity early in MS should have greater weight in clinical decision-making. [ABSTRACT FROM AUTHOR]

Published
2024
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43. No evidence of disease activity (NEDA) analysis by epochs in patients with relapsing multiple sclerosis treated with ocrelizumab vs interferon beta-1a

Author

Havrdová, Eva, Arnold, Douglas L, Bar-Or, Amit, Comi, Giancarlo, Hartung, Hans-Peter, Kappos, Ludwig, Lublin, Fred, Selmaj, Krzysztof, Traboulsee, Anthony, Belachew, Shibeshih, Bennett, Iain, Buffels, Regine, Garren, Hideki, Han, Jian, Julian, Laura, Napieralski, Julie, Hauser, Stephen L, and Giovannoni, Gavin

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Brain Disorders, Multiple Sclerosis, Clinical Trials and Supportive Activities, Clinical Research, Neurodegenerative, Neurosciences, Autoimmune Disease, MRI activity, NEDA, disability progression, disease activity, relapse, Epidemiology, Health services and systems
Abstract

BackgroundNo evidence of disease activity (NEDA; defined as no 12-week confirmed disability progression, no protocol-defined relapses, no new/enlarging T2 lesions and no T1 gadolinium-enhancing lesions) using a fixed-study entry baseline is commonly used as a treatment outcome in multiple sclerosis (MS).ObjectiveThe objective of this paper is to assess the effect of ocrelizumab on NEDA using re-baselining analysis, and the predictive value of NEDA status.MethodsNEDA was assessed in a modified intent-to-treat population (n = 1520) from the pooled OPERA I and OPERA II studies over various epochs in patients with relapsing MS receiving ocrelizumab (600 mg) or interferon beta-1a (IFN β-1a; 44 μg).ResultsNEDA was increased with ocrelizumab vs IFN β-1a over 96 weeks by 75% (p

Published
2018

44. Phase IV study of retention on fingolimod versus injectable multiple sclerosis therapies: a randomized clinical trial

Author

Cree, Bruce AC, Arnold, Douglas L, Cascione, Mark, Fox, Edward J, Williams, Ian M, Meng, Xiangyi, Schofield, Lesley, and Tenenbaum, Nadia

Subjects
Neurosciences, Clinical Research, Brain Disorders, Neurodegenerative, Multiple Sclerosis, Clinical Trials and Supportive Activities, Autoimmune Disease, Evaluation of treatments and therapeutic interventions, Management of diseases and conditions, 7.1 Individual care needs, 6.1 Pharmaceuticals, adherence, disease-modifying therapy, fingolimod, glatiramer acetate, interferon, randomized controlled trial, retention, multiple sclerosis
Abstract

ObjectiveIn relapsing-remitting multiple sclerosis (RRMS), suboptimal adherence to injectable disease-modifying therapies (iDMTs; interferon β-1a/b, glatiramer acetate) is common, reducing their effectiveness. Patient retention on oral fingolimod and iDMTs was evaluated in PREFERMS, a randomized, parallel-group, active-controlled, open-label, 48-week study.MethodsPatients were included if they had RRMS, were aged 18-65 years and had Expanded Disability Status Scale score up to 6, enrolled at 117 US study sites, were treatment naïve or had received only one iDMT class. Patients were randomized 1:1 (fingolimod 0.5 mg/day; preselected iDMT) by interactive voice-and-web-response system without blinding, followed up quarterly, and allowed one study-approved treatment switch after 12 weeks, or earlier for efficacy or safety reasons. The primary outcome was patient retention on randomized treatment over 48 weeks. Secondary endpoints included patient-reported outcomes, brain volume loss (BVL), and cognitive function.ResultsAnalysis of 433/436 patients receiving fingolimod and 428/439 receiving iDMTs showed that patient retention rate was significantly higher with fingolimod than with iDMTs [352 (81.3%) versus 125 (29.2%); 95% confidence interval 46.4-57.8%; p < 0.0001]. The most common treatment switch was from iDMT to fingolimod for injection-related reasons. Patient satisfaction was greater and BVL less with fingolimod than with iDMTs, with no difference in cognitive function. Adverse events were consistent with established tolerability profiles for each treatment.ConclusionsIn RRMS, fingolimod was associated with better treatment retention, patient satisfaction and BVL outcomes than iDMTs. Patients may persist with iDMTs, but many may switch treatment if permitted. Treatment satisfaction fosters adherence, a prerequisite for optimal outcomes.

Published
2018

45. Teriflunomide in pediatric patients with relapsing multiple sclerosis: Open-label extension of TERIKIDS

Author

Chitnis, Tanuja, primary, Banwell, Brenda, additional, Kappos, Ludwig, additional, Arnold, Douglas L, additional, Gücüyener, Kivilcim, additional, Deiva, Kumaran, additional, Saubadu, Stephane, additional, Hu, Wenruo, additional, Benamor, Myriam, additional, Le-Halpere, Annaig, additional, Truffinet, Philippe, additional, and Tardieu, Marc, additional

Published
2024
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46. Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI

Author

Durso-Finley, Joshua, Arnold, Douglas L., Arbel, Tal, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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47. Safety and efficacy of teriflunomide in paediatric multiple sclerosis (TERIKIDS): a multicentre, double-blind, phase 3, randomised, placebo-controlled trial

Author

Dubois, Benedicte, Verhelst, Helene, Bojinova-Tchamova, Veneta, Mah, Jean, Cui, Li-Ying, Fang, Fang, Hao, Yunpeng, Jiang, Li, Li, Ling, Mao, Ding'An, Qiu, Wei, Tan, Guojun, Wu, Ye, Zhang, Meini, Zhou, Hongyu, Zhou, Shuizhen, Gross-Paju, Katrin, Cheuret, Emmanuel, Deiva, Kumaran, Edan, Giles, Vukusic, Sandra, Chrousos, George, Zafeiriou, Dimitrios, Achiron, Anat, Vaknin-Dembinsky, Adi, Yamout, Bassem, Laurynaitiene, Jurate, Vaiciene-Magistris, Nerija, Bojkovski, Vladimir, Trajkova, Vesna, Chaouki, Sana, Kissani, Najib, Neuteboom, Rinze, Palavra, Filipe, Belova, Anna, Boyko, Alexey, Evdoshenko, Evgeny, Kairbekova, Ekaterina, Malkova, Nadezhda, Shumilina, Maria, Skripchenko, Natalya, Nikolic, Dimitrije, Meca-Lallana, Jose, Triki, Chahnez Charfi, Chokri, Mhiri, Gouider, Riadh, Anlar, Banu, Gücüyener, Kivilcim, Hiz, Ayse Semra, Idiman, Egemen, Turkoglu, Recai, Yapici, Zuhal, Yilmaz, Unsal, Tantsura, Lyudmyla, Voloshyna, Nataliia, Lim, Ming, Wassmer, Evangeline, Cascione, Mark, Chitnis, Tanuja, LaGanke, Christopher, Rathke, Kevin, Scagnelli, John, Banwell, Brenda, Kappos, Ludwig, Arnold, Douglas L, Skripchenko, Natalia, Saubadu, Stephane, Hu, Wenruo, Benamor, Myriam, Le-Halpere, Annaig, Truffinet, Philippe, and Tardieu, Marc

Published
2021
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48. Safety and efficacy of tolebrutinib, an oral brain-penetrant BTK inhibitor, in relapsing multiple sclerosis: a phase 2b, randomised, double-blind, placebo-controlled trial

Author

Grand'Maison, Francois, Jacques, Francois, Traboulsee, Anthony, Tyblova, Michaela, Meluzinova, Eva, Ampapa, Radek, Valis, Martin, Hradilke, Pavel, Mareš, Miroslav, Stourac, Pavel, Gross-Paju, Katrin, Laplaud, David, Mathey, Guillaume, Uitdehaag, Bernard, Evdoshenkoo, Evgeny, Popova, Ekaterina, Zakharova, Maria, Totolyan, Natalia, Litvinenko, Igor, Khabirov, Farit, Sivertseva, Stella, Hancinova, Viera, Kantorova, Ema, Gines, Maria Luisa Martinez, Montalban, Xavier, Maduano, Sara Eichau, Meca-Lallana, Jose, Ramió-Torrentà, Lluís, Nehrych, Tetyanna, Pashkovskyy, Valeriy, Moskovko, Sergii, Kalbus, Oleksandr, Khavunka, Marta, Pryshchepa, Volodymyr, Goloborodko, Alla, Wynn, Daniel, Honeycutt, William, Wray, Sibyl, Steingo, Brian, LaGanke, Christopher, Huang, Deren, Hemphill, John Michael, Goldstick, Lawrence, Robertson, Derrick, Reich, Daniel S, Arnold, Douglas L, Vermersch, Patrick, Bar-Or, Amit, Fox, Robert J, Matta, Andre, Turner, Timothy, Wallström, Erik, Zhang, Xinyan, and Khabirov, Farit A

Published
2021
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