Trial reportNature communications2022
Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning.
Trial report in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 31 citations in OpenAlex.
- Artificial intelligence in central nervous system autoimmune diseases: from clinical decision support to mechanistic discovery and translation.EMBO molecular medicine · 2026Review
- Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT).Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Observational
- Separating Ranking Signal, Incremental Feature Value, and Evaluation-Induced Optimism in Breast MRI Treatment-Effect Analysis: Foundation Model and Radiomic Representations.Journal of imaging informatics in medicine · 2026Article
- [XVIII Post-ECTRIMS Meeting: Review of the New Developments Presented at the 2025 ECTRIMS Congress (Part I)].Revista de neurologia · 2026Article
- Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.Caspian journal of internal medicine · 2026Review
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Deep learning-based ranking method for subgroup and predictive biomarker identification in patients.Communications medicine · 2025Article
- Personalized Treatment Response in Progressive MS: Can the Patient's Profile Influence the Outcome?Brain and behavior · 2025Article
- A bibliometric analysis of disability research in Saudi Arabia: Evolving trends, thematic mapping, and gaps.Saudi medical journal · 2025Article
- Applications of AI in Predicting Drug Responses for Type 2 Diabetes.JMIR diabetes · 2025Article
- A data-driven model of disability progression in progressive multiple sclerosis.Brain communications · 2025Article
- The role of AI for MRI-analysis in multiple sclerosis-A brief overview.Frontiers in artificial intelligence · 2025Review
- Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies.Critical care medicine · 2024Review
- Causal impact evaluation of occupational safety policies on firms' default using machine learning uplift modelling.Scientific reports · 2024Article
- Artificial intelligence in neurology: opportunities, challenges, and policy implications.Journal of neurology · 2024Review
- A Review on the Feasibility and Efficacy of Home-Based Cognitive Remediation in People with Multiple Sclerosis.Journal of clinical medicine · 2024Review
- Association between tranexamic acid administration and mortality based on the trauma phenotype: a retrospective analysis of a nationwide trauma registry in Japan.Critical care (London, England) · 2024Article
- Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review.Journal of clinical medicine · 2024Review
- Assessing heterogeneity of treatment effect in multiple sclerosis trials.Multiple sclerosis (Houndmills, Basingstoke, England) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Disability progression in multiple sclerosis remains resistant to treatment. The absence of a suitable biomarker to allow for phase 2 clinical trials presents a high barrier for drug development. We propose to enable short proof-of-concept trials by increasing statistical power using a deep-learning predictive enrichment strategy. Specifically, a multi-headed multilayer perceptron is used to estimate the conditional average treatment effect (CATE) using baseline clinical and imaging features, and patients predicted to be most responsive are preferentially randomized into a trial. Leveraging data from six randomized clinical trials (n = 3,830), we first pre-trained the model on the subset of relapsing-remitting MS patients (n = 2,520), then fine-tuned it on a subset of primary progressive MS (PPMS) patients (n = 695). In a separate held-out test set of PPMS patients randomized to anti-CD20 antibodies or placebo (n = 297), the average treatment effect was larger for the 50% (HR, 0.492; 95% CI, 0.266-0.912; p = 0.0218) and 30% (HR, 0.361; 95% CI, 0.165-0.79; p = 0.008) predicted to be most responsive, compared to 0.743 (95% CI, 0.482-1.15; p = 0.179) for the entire group. The same model could also identify responders to laquinimod in another held-out test set of PPMS patients (n = 318). Finally, we show that using this model for predictive enrichment results in important increases in power.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.