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Title:           Deep learning to predict progression independent of relapse activity at a first demyelinating event
Subject:         DOI: 10.1093/braincomms/fcaf243; Brain Communications, 7, 4, 2025-07-04.; Abstract: Progression independent of relapse activity is the main cause of irreversible disability in multiple sclerosis and is strongly associated with older age at symptom onset. Early and accurate prediction, at symptom onset, of which patients are at highest risk of progression independent of relapses, is an unmet need. This study aimed to develop a deep learning survival model using only routine MRI acquired at the first demyelinating attack to predict the risk of progression independent of relapses, and assess its ability to improve classical age-adjusted predictions. We analysed a prospective cohort of patients under 50, clinically assessed within three months of symptom onset, with available MRI (T1- and T2-Fluid-Attenuated Inversion Recovery sequences). An independent early multiple sclerosis cohort (≤1 year from symptom onset) from the Multiple Sclerosis Partners Advancing Technology and Health Soluti
Keywords:        multiple sclerosis; progression independent of relapse activity; predictive model; brain MRI; deep learning
Author:          Llucia Coll
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     Item: 
      Title: Deep learning to predict progression independent of relapse activity at a first demyelinating event
      Destination: head1
      Children: 
       Item: 
        Title: Introduction
        Destination: secfcaf243-s1
       Item: 
        Title: Materials and methods
        Destination: secfcaf243-s2
        Children: 
         Item: 
          Title: Patients
          Destination: secfcaf243-s2.1
          Children: 
           Item: 
            Title: Original (training and testing) cohort: Vall d’Hebron University Hospital cohort
            Destination: secfcaf243-s2.1.1
           Item: 
            Title: External validation cohort: MS PATHS cohort
            Destination: secfcaf243-s2.1.2
           Item: 
            Title: Ethical requirements and permissions
            Destination: secfcaf243-s2.1.3
         Item: 
          Title: Clinical assessments and outcomes
          Destination: secfcaf243-s2.2
          Children: 
           Item: 
            Title: Original cohort
            Destination: secfcaf243-s2.2.1
           Item: 
            Title: External validation cohort
            Destination: secfcaf243-s2.2.2
         Item: 
          Title: MRI data
          Destination: secfcaf243-s2.3
          Children: 
           Item: 
            Title: Original cohort
            Destination: secfcaf243-s2.3.1
           Item: 
            Title: External validation cohort
            Destination: secfcaf243-s2.3.2
         Item: 
          Title: Data analysis
          Destination: secfcaf243-s2.4
          Children: 
           Item: 
            Title: Descriptive statistics
            Destination: secfcaf243-s2.4.1
           Item: 
            Title: DL model to predict time to PIRA
            Destination: secfcaf243-s2.4.2
           Item: 
            Title: Training procedure
            Destination: secfcaf243-s2.4.3
           Item: 
            Title: Model inference
            Destination: secfcaf243-s2.4.4
           Item: 
            Title: Evaluation of model performance
            Destination: secfcaf243-s2.4.5
           Item: 
            Title: Assessment of the added value of the DL-based estimations to conventional survival models of time to PIRA
            Destination: secfcaf243-s2.4.6
           Item: 
            Title: Assessing model interpretability through relevance maps: deep SHAP
            Destination: secfcaf243-s2.4.7
           Item: 
            Title: External validation analysis
            Destination: secfcaf243-s2.4.8
           Item: 
            Title: Implementation details
            Destination: secfcaf243-s2.4.9
       Item: 
        Title: Results
        Destination: secfcaf243-s3
        Children: 
         Item: 
          Title: Descriptive statistics (original cohort)
          Destination: secfcaf243-s3.1
         Item: 
          Title: DL prediction of PIRA
          Destination: secfcaf243-s3.2
          Children: 
           Item: 
            Title: Best threshold to predict PIRA
            Destination: secfcaf243-s3.2.1
           Item: 
            Title: Improvement of conventional survival models with DL-based outputs
            Destination: secfcaf243-s3.2.2
           Item: 
            Title: Model interpretability: relevance maps
            Destination: secfcaf243-s3.2.3
         Item: 
          Title: External validation analysis
          Destination: secfcaf243-s3.3
          Children: 
           Item: 
            Title: Descriptive statistics (validation cohort)
            Destination: secfcaf243-s3.3.1
           Item: 
            Title: Prediction of PIRA using the best threshold estimated in the original cohort
            Destination: secfcaf243-s3.3.2
       Item: 
        Title: Discussion
        Destination: secfcaf243-s4
        Children: 
         Item: 
          Title: Limitations of the study
          Destination: secfcaf243-s4.1
       Item: 
        Title: Conclusions
        Destination: secfcaf243-s5
       Item: 
        Title: Supplementary material
        Destination: secfcaf243-s6
       Item: 
        Title: Acknowledgements
        Destination: acknow
       Item: 
        Title: Funding
        Destination: secfcaf243-s7
       Item: 
        Title: Competing Interests
        Destination: secfcaf243-s8
       Item: 
        Title: Data availability
        Destination: secfcaf243-s9
       Item: 
        Title: References
        Destination: reflist
   Info: 
    Title: Deep learning to predict progression independent of relapse activity at a first demyelinating event
    Author: Llucia Coll
    Subject: DOI: 10.1093/braincomms/fcaf243; Brain Communications, 7, 4, 2025-07-04.; Abstract: Progression independent of relapse activity is the main cause of irreversible disability in multiple sclerosis and is strongly associated with older age at symptom onset. 䔀愀爀氀礀 愀渀搀 愀挀挀甀爀愀琀攀 瀀爀攀搀椀挀琀椀漀渀Ⰰ 愀琀 猀礀洀瀀琀漀洀 漀渀猀攀琀Ⰰ 漀昀 眀栀椀挀栀 瀀愀琀椀攀渀琀猀 愀爀攀 愀琀 栀椀最栀攀猀琀 爀椀猀欀 漀昀 瀀爀漀最爀攀猀猀椀漀渀 椀渀搀攀瀀攀渀搀攀渀琀 漀昀 爀攀氀愀瀀猀攀猀Ⰰis an unmet need. This study aimed to develop a deep learning survival model using only routine MRI acquired at the first demye氀椀渀愀琀椀渀最 愀琀琀愀挀欀 琀漀 瀀爀攀搀椀挀琀 琀栀攀 爀椀猀欀 漀昀 瀀爀漀最爀攀猀猀椀漀渀 椀渀搀攀瀀攀渀搀攀渀琀 漀昀 爀攀氀愀瀀猀攀猀Ⰰ 愀渀搀 愀猀猀攀猀猀 椀琀猀 愀戀椀氀椀琀礀 琀漀 椀洀瀀爀漀瘀攀 挀氀愀猀猀椀挀愀氀 愀最攀ⴀ愀搀樀sted predictions. We analysed a prospective cohort of patients under 50, clinically assessed within three months of symptom ons攀琀Ⰰ 眀椀琀栀 愀瘀愀椀氀愀戀氀攀 䴀刀䤀 吀㄀ⴀ 愀渀搀 吀㈀ⴀ䘀氀甀椀搀ⴀ䄀琀琀攀渀甀愀琀攀搀 䤀渀瘀攀爀猀椀漀渀 刀攀挀漀瘀攀爀礀 猀攀焀甀攀渀挀攀猀⸀ 䄀渀 椀渀搀攀瀀攀渀搀攀渀琀 攀愀爀氀礀 洀甀氀琀椀瀀氀攀 猀挀氀攀爀漀猀椀猀 挀漀栀rt (≤1 year from symptom onset) from the Multiple Sclerosis Partners Advancing Technology and Health Soluti
    Keywords: multiple sclerosis; progression independent of relapse activity; predictive model; brain MRI; deep learning
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       Destination: 66
      Annotation: 
       Subtype: Link
       Rect: 311, 555, 320, 563
       Flags: Print
       Destination: 66
      Annotation: 
       Subtype: Link
       Rect: 311, 524, 320, 532
       Flags: Print
       Destination: 66
  Checksum: 3fcbb9b0
   Type: CRC32
  Checksum: 07252ba34f372f8920460bd9601da81e
   Type: MD5
  Checksum: 0baf55cc7aab2564b195d403a05a2315033573fd
   Type: SHA-1
  Checksum: 9887ea2468bd71fa9059001cbd3464114b3bc0b5d8ce1f672cf5bc5d7d1ed459
   Type: SHA-256