Artificial Intelligence in Predicting Progression in Multiple Sclerosis Study
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- University of Ljubljana
- Enrollment
- 654
- Locations
- 4
- Primary Endpoint
- Atrophied lesion volume derived from MRI predicts confirmed EDSS disability progression
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 18 Years to 65 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
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
Outcomes
Primary Outcomes
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 Outcomes
- Atrophied lesion volume derived from MRI predicts conversion to secondary progressive multiple sclerosis(Atrophied lesion volume quantified from two or more MR scans across the span of least one and up to five years)
Investigators
Ziga Spiclin
Associate professor, PhD
University of Ljubljana