Evidence map›Paper›PMID 42224860›Full record

ArticleNeuroImage. Clinical2026

A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.

Nataliia Molchanova, Alessandro Cagol, Mario Ocampo-Pineda, Po-Jui Lu, Matthias Weigel, Xinjie Chen, Erin S Beck, Charidimos Tsagkas, Daniel S Reich, Colin Vanden Bulcke and 10 more

Abstract readComparative StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Nataliia MolchanovaFaculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland; Radiology Department, Lausanne University Hospital (CHUV), Lausanne, Switzerland; MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Switzerland. Electronic address: nataliia.molchanova@unil.ch.
Alessandro CagolTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Multiple Sclerosis Center, Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; Dipartimento di Scienze della Salute, Università degli Studi di Genova, Genova, Italy.
Mario Ocampo-PinedaTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Multiple Sclerosis Center, Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
Po-Jui LuTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Multiple Sclerosis Center, Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
Matthias WeigelTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Multiple Sclerosis Center, Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.
Xinjie ChenTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Multiple Sclerosis Center, Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
Erin S BeckDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York City, USA; Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA.
Charidimos TsagkasTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA.
Daniel S ReichTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA.
Colin Vanden BulckeNeuroinflammation Imaging Lab (NIL), Université catholique de Louvain, Brussels, Belgium.
Anna StöltingNeuroinflammation Imaging Lab (NIL), Université catholique de Louvain, Brussels, Belgium.
Serena BorrelliNeuroinflammation Imaging Lab (NIL), Université catholique de Louvain, Brussels, Belgium; Department of Neurology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université libre de Bruxelles, Brussels, Belgium.
Pietro MaggiNeuroinflammation Imaging Lab (NIL), Université catholique de Louvain, Brussels, Belgium.
Sebastian Baez LugoEPFL+ECAL Lab, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Delphine Ribes LemayEPFL+ECAL Lab, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Adrien DepeursingeMedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Cristina GranzieraTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Multiple Sclerosis Center, Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
Henning MüllerMedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
Pedro M GordalizaFaculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland; Radiology Department, Lausanne University Hospital (CHUV), Lausanne, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Switzerland.
Meritxell Bach CuadraFaculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland; Radiology Department, Lausanne University Hospital (CHUV), Lausanne, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cortical lesions (CLs) have emerged as valuable biomarkers in multiple sclerosis (MS), offering high diagnostic specificity and prognostic relevance. However, their routine clinical integration remains limited due to subtle magnetic resonance imaging (MRI) appearance, challenges in expert annotation, and a lack of standardized automated methods. We present a multi-centric comparative study of CL detection and segmentation in MRI. A total of 656 MRI scans, including clinical trial and research data from four institutions, were acquired at 3T and 7T using MP2RAGE and MPRAGE sequences with expert-consensus annotations. We rely on the self-configuring nnU-Net framework, designed for medical imaging segmentation, and propose adaptations tailored to the improved CL detection. We evaluated model generalization through out-of-distribution testing, demonstrating promising lesion detection capabilities with an F1-score of 0.64 and 0.5 in and out of the domain, respectively. We also analyze internal model features and model errors for a better understanding of AI decision-making. Our study examines how data variability, lesion ambiguity, and protocol differences impact model performance, offering future recommendations to address these barriers to clinical adoption. Furthermore, we designed and implemented a medical expert questionnaire for better assessment of clinical value of the model predictions. To reinforce the reproducibility, the implementation and models will be publicly accessible and ready to use at GitHub and Zenodo.

Indexed as

Cerebral CortexDeep LearningImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMagnetic Resonance ImagingMultiple SclerosisAdultFemaleHumansMaleBrainCortical lesionsDeep learningDetectionMagnetic resonance imagingMultiple sclerosisSegmentationTrustworthy AI

Identifiers

PMID42224860
PMCPMC13251776

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.