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Artificial Intelligence in Predicting Progression in Multiple Sclerosis Study (AI ProMiS)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT05426980
Recruitment Status : Recruiting
First Posted : June 22, 2022
Last Update Posted : June 22, 2022
Sponsor:
Collaborators:
Novartis
General and Teaching Hospital Celje
University Medical Centre Ljubljana
University Medical Centre Maribor
General Hospital Izola
Information provided by (Responsible Party):
Ziga Spiclin, University of Ljubljana

Study Description
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Brief Summary:
The study proposal focuses on multiple sclerosis (MS), a chronic incurable disease of the central nervous system (CNS). The MS disease is characterised by recurrent transient disability progression, quantified by increase in the extended disability status score (EDSS), and subsequent remission (disappearance of symptoms and reduced EDSS score) or, alternatively, a gradual EDSS disability progression and exacerbation of associated symptoms. At the same time, the MS is characterised by multifocal inflammatory lesions disseminated throughout the white and grey matter of the CNS, which can be observed and quantified in the magnetic resonance (MR) scans. The proposed study will address the critical unmet need of computer-assisted extraction and assessment of prognostic factors based from an individual patient's brain MR scan, such as lesion count, volume, whole-brain and regional brain atrophy, and atrophied lesion volume, in order to evaluate the capability for personalized future disability progression prediction.

Condition or disease
Multiple Sclerosis Multiple Sclerosis Lesion Multiple Sclerosis Brain Lesion

Study Design
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Study Type : Observational
Estimated Enrollment : 1200 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Artificial Intelligence in Predicting Progression in Multiple Sclerosis Study
Actual Study Start Date : December 13, 2021
Estimated Primary Completion Date : June 30, 2023
Estimated Study Completion Date : June 30, 2023

Resource links provided by the National Library of Medicine


Groups and Cohorts
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Outcome Measures
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Primary Outcome Measures :
  1. Atrophied lesion volume derived from MRI predicts confirmed EDSS disability progression [ Time Frame: Atrophied lesion volume quantified from two or more MR scans across the span of at least one and up to five years ]
    Patients will be divided into two groups based on the presence or absence of EDSS disability progression (DP) during the observation period. The DP converters will be classified as patients with an EDSS change of at least 1.5 if the baseline EDSS is less than 1.0, those with an EDSS change of at least 1.0 if the baseline EDSS is 1.0-5.5, and those with an EDSS change of at least 0.5 if the baseline EDSS is 5.5 or higher [15]. DP converters should have confirmed progression of EDSS impairment over a period of at least 6 months. DP non-converters include individuals who do not meet the criteria for conversion. Atrophied lesion volume will be quantified from MR scans taken >6 months prior to the observed EDSS increase. Advanced artificial intelligence based image analysis tools will be applied to assess the atrophied lesion volume.


Secondary Outcome Measures :
  1. Atrophied lesion volume derived from MRI predicts conversion to secondary progressive multiple sclerosis [ Time Frame: Atrophied lesion volume quantified from two or more MR scans across the span of least one and up to five years ]
    Patients will be divided into two groups, i.e. those who transitioned from clinically isolate syndrome (CIS) or relapsing-remitting (RR) to secondary progressive (SP) form of MS and those who were diagnosed with CIS/RRMS during the observation period. A consilium for patients with MS will confirm the SPMS diagnosis by consensus. Atrophied lesion volume will be quantified from MR scans taken >6 months prior to the observed conversion to the SPMS. Advanced artificial intelligence based image analysis tools will be applied to assess the atrophied lesion volume.


Eligibility Criteria
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Ages Eligible for Study:   18 Years to 65 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
The study will be based on a retrospective secondary analysis of demographic and clinical data and MRI scans of approximately 1200 Slovenian patients with MS that are regularly monitored between 2015 and present.
Criteria

Inclusion Criteria:

  • persons diagnosed with MS (any phenotype; according to the 2010 McDonald criteria) and CIS patients
  • availability of at least two MRI exams with both FLAIR and T1-weighted scans of the same participant over a period of at least 6 months at the most recent examination
  • availability of demographic, clinical data and treatment information for the same participant over a period of at least 6 months at the most recent examination
  • availability of EDSS score and at least one previous EDSS scores for the same participant over a period of at least 6 months at the most recent examination

Exclusion Criteria:

  • other clinically relevant systemic diseases if the researcher considers them to be significant
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05426980


Contacts
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Contact: Ziga Spiclin, PhD 014768784 ziga.spiclin@fe.uni-lj.si

Locations
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Slovenia
University medical center Ljubljana Recruiting
Ljubljana, Osrednjeslovenska, Slovenia, 1000
Contact: Gregor Brecl Jakob, MD, PhD         
Principal Investigator: Gregor Brecl Jakob, MD, PhD         
General and teaching hospital Celje Recruiting
Celje, Slovenia, 3000
Contact: Lina Savsek, MD         
Principal Investigator: Lina Savsek, MD         
General hospital Izola Recruiting
Izola, Slovenia
Contact: Bojan Rojc, MD, PhD         
Principal Investigator: Bojan Rojc, MD, PhD         
University medical center Maribor Recruiting
Maribor, Slovenia, 2000
Contact: Jozef Magdic, MD         
Principal Investigator: Jozef Magdic, MD         
Sponsors and Collaborators
University of Ljubljana
Novartis
General and Teaching Hospital Celje
University Medical Centre Ljubljana
University Medical Centre Maribor
General Hospital Izola
Investigators
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Principal Investigator: Ziga Spiclin, PhD University of Ljubljana
More Information
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Responsible Party: Ziga Spiclin, Associate professor, PhD, University of Ljubljana
ClinicalTrials.gov Identifier: NCT05426980    
Other Study ID Numbers: 0120-570/2021/5
First Posted: June 22, 2022    Key Record Dates
Last Update Posted: June 22, 2022
Last Verified: June 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Ziga Spiclin, University of Ljubljana:
Artificial intelligence
Prediction model
Magnetic resonance imaging
Computer-assisted image analysis
Additional relevant MeSH terms:
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Multiple Sclerosis
Sclerosis
Pathologic Processes
Demyelinating Autoimmune Diseases, CNS
Autoimmune Diseases of the Nervous System
 Nervous System Diseases
Demyelinating Diseases
Autoimmune Diseases
Immune System Diseases