Predicting disease severity in multiple sclerosis using multimodal data and machine learning

被引：14

作者：

Andorra, Magi ^{[1
,2
]}

Freire, Ana ^{[3
,20
]}

Zubizarreta, Irati ^{[1
,2
]}

de Rosbo, Nicole Kerlero ^{[4
,5
]}

Bos, Steffan D. ^{[6
,7
]}

Rinas, Melanie ^{[8
,9
]}

Hogestol, Einar A. ^{[6
,7
]}

Benavent, Sigrid A. de Rodez ^{[5
,6
]}

Berge, Tone ^{[7
,10
]}

Brune-Ingebretse, Synne ^{[6
,7
]}

Ivaldi, Federico ^{[11
]}

Cellerino, Maria ^{[4
]}

Pardini, Matteo ^{[4
,5
]}

Vila, Gemma ^{[1
,2
]}

Pulido-Valdeolivas, Irene ^{[1
,2
]}

Martinez-Lapiscina, Elena H. ^{[1
,2
]}

Llufriu, Sara ^{[1
,2
]}

Saiz, Albert ^{[1
,2
]}

Blanco, Yolanda ^{[1
,2
]}

Martinez-Heras, Eloy ^{[1
,2
]}

Solana, Elisabeth ^{[1
,2
]}

Baecker-Koduah, Priscilla ^{[12
]}

Behrens, Janina ^{[12
]}

Kuchling, Joseph ^{[12
]}

Asseyer, Susanna ^{[12
,13
]}

Scheel, Michael ^{[12
]}

Chien, Claudia ^{[12
,13
]}

Zimmermann, Hanna ^{[12
,13
]}

Motamedi, Seyedamirhosein ^{[12
]}

Kauer-Bonin, Josef ^{[12
]}

Brandt, Alex ^{[12
]}

Saez-Rodriguez, Julio ^{[8
,9
]}

Alexopoulos, Leonidas G. ^{[14
,15
]}

Paul, Friedemann ^{[12
,13
]}

Harbo, Hanne F. ^{[6
,7
]}

Shams, Hengameh ^{[16
]}

Oksenberg, Jorge ^{[16
]}

Uccelli, Antonio ^{[4
,5
]}

Baeza-Yates, Ricardo ^{[17
]}

Villoslada, Pablo ^{[18
,19
]}

机构：

[1] Inst Invest Biomed August Pi & Sunyer IDIBAPS, Barcelona, Spain

[2] Hosp Clin Barcelona, Barcelona, Spain

[3] Pompeu Fabra Univ, Sch Management, Barcelona, Spain

[4] Univ Genoa, Dept Neurosci Rehabil Ophthalmol Genet Maternal &, Genoa, Italy

[5] IRCCS Osped Policlin San Martino, Genoa, Italy

[6] Univ Oslo, Oslo, Norway

[7] Oslo Univ Hosp, Oslo, Norway

[8] Heidelberg Univ Hosp, Inst Computat Biomed, Heidelberg, Germany

[9] Heidelberg Univ, Heidelberg, Germany

[10] Oslo Metropolitan Univ, Oslo, Norway

[11] Univ Genoa, Dept Internal Med, Genoa, Italy

[12] Charite Univ Med Berlin, Berlin, Germany

[13] Max Delbrueck Ctr Mol Med, Berlin, Germany

[14] ProtATonce Ltd, Athens, Greece

[15] Natl Tech Univ Athens, Sch Mech Engn, Zografos, Greece

[16] Univ Calif San Francisco, Dept Neurol, San Francisco, CA USA

[17] Pompeu Fabra Univ, Sch Engn, Barcelona, Spain

[18] Pompeu Fabra Univ, Dept Med & Life Sci, Barcelona, Spain

[19] Hosp Mar, Res Inst, Barcelona, Spain

[20] UPF, Barcelona Sch Management, Balmes 132, Barcelona 08008, Spain

来源：

JOURNAL OF NEUROLOGY | 2024年 / 271卷 / 03期

关键词：

Multiple sclerosis; Omics; Imaging; Machine learning; Precision medicine; OLIGOCLONAL BANDS; RISK; DISABILITY; BIOMARKER; NETWORKS; MEDICINE; SCORE;

D O I：

10.1007/s00415-023-12132-z

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

摘要：

BackgroundMultiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.MethodsWe have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre.ResultsWe found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts.ConclusionCombining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

引用

页码：1133 / 1149

页数：17

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