Artificial Intelligence in Predicting Progression in Multiple Sclerosis Study

Artificial Intelligence in Predicting Progression in Multiple Sclerosis Study

Sponsors

Lead Sponsor: University of Ljubljana

Collaborator: Novartis
General and Teaching Hospital Celje
University Medical Centre Ljubljana
University Medical Centre Maribor
General Hospital Izola

Source University of Ljubljana
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.

Overall Status Recruiting
Start Date 2021-12-13
Completion Date 2023-06-30
Primary Completion Date 2023-06-30
Study Type Observational
Primary Outcome
Measure Time Frame
Atrophied lesion volume derived from MRI predicts confirmed EDSS disability progression Atrophied lesion volume quantified from two or more MR scans across the span of at least one and up to five years
Secondary Outcome
Measure Time Frame
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
Enrollment 1200
Condition
Eligibility

Sampling Method:

Non-Probability Sample

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

Gender:

All

Minimum Age:

18 Years

Maximum Age:

65 Years

Healthy Volunteers:

Accepts Healthy Volunteers

Overall Official
Last Name Role Affiliation
Ziga Spiclin, PhD Principal Investigator University of Ljubljana
Overall Contact

Last Name: Ziga Spiclin, PhD

Phone: 014768784

Email: [email protected]

Location
Facility: Status: Contact: Investigator:
University medical center Ljubljana | Ljubljana, Osrednjeslovenska, 1000, Slovenia Recruiting Gregor Brecl Jakob, MD, PhD Gregor Brecl Jakob, MD, PhD Principal Investigator
General and teaching hospital Celje | Celje, 3000, Slovenia Recruiting Lina Savsek, MD Lina Savsek, MD Principal Investigator
General hospital Izola | Izola, Slovenia Recruiting Bojan Rojc, MD, PhD Bojan Rojc, MD, PhD Principal Investigator
University medical center Maribor | Maribor, 2000, Slovenia Recruiting Jozef Magdic, MD Jozef Magdic, MD Principal Investigator
Location Countries

Slovenia

Verification Date

2022-06-01

Responsible Party

Type: Principal Investigator

Investigator Affiliation: University of Ljubljana

Investigator Full Name: Ziga Spiclin

Investigator Title: Associate professor, PhD

Keywords
Has Expanded Access No
Condition Browse
Acronym AI ProMiS
Patient Data No
Study Design Info

Observational Model: Cohort

Time Perspective: Retrospective

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