mlsanityData to 14 Oct 2026

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

arXiv 2204.01702 ↗First posted 1 Apr 2022eess.IV cs.CV cs.LGcited by 7

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Abstract (arXiv)

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).

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 13 of its 54 references that are in the map. First posted on arXiv 1 Apr 2022.

Builds on

13 of 54 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  3. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
  4. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 358
  5. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
  6. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  7. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
  8. 2018Deep Neural Networks for Survival Analysis Based on a Multi-Task Frameworkcited by 45
  9. 2017Uplift Modeling with Multiple Treatments and General Response Typescited by 41
  10. 2019Uplift Modeling for Multiple Treatments with Cost Optimizationcited by 25
  11. 2017Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approachcited by 19
  12. 2018Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networkscited by 7
1 more
  1. 2021Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1; 2026: 1.

7 papers.

  1. 2023Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Modelscited by 5
  2. 2023Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Imagescited by 2
  3. 2024Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEscited by 1
  4. 2022Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple Sclerosiscited by 0
  5. 2024Enhancing predictive imaging biomarker discovery through treatment effect analysiscited by 0
  6. 2025Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatmentscited by 0
  7. 2026Physics-Grounded Disentangled Flow Modeling for Brain Disease Progression Trajectorycited by 0