How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review.

Humans Workflow Ambulatory Care Facilities Multiple Sclerosis / diagnostic imaging
Lesion detection Lesion segmentation MRI Multiple sclerosis Systematic review

Journal

NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070

Informations de publication

Date de publication:
2023
Historique:
received: 02 08 2023
accepted: 04 08 2023
medline: 18 9 2023
pubmed: 3 9 2023
entrez: 2 9 2023
Statut: ppublish

Résumé

Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI). Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow. Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI's six-steps, which include a tool's technical assessment, clinical validation, and integration. We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth. To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients' management of such tools remain almost unexplored.

Identifiants

DOI: 10.1016/j.nicl.2023.103491 PMID: 37659189 PMC: PMC10480555
pubmed: 37659189
pii: S2213-1582(23)00182-1
doi: 10.1016/j.nicl.2023.103491
pmc: PMC10480555
pii:
doi:

Types de publication

Systematic Review Journal Article Review Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

103491

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.

Auteurs

Federico Spagnolo (F)

Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; 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; MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.

Adrien Depeursinge (A)

MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.

Sabine Schädelin (S)

Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.

Aysenur Akbulut (A)

Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Ankara University School of Medicine, Ankara, Turkey.

Henning Müller (H)

MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; The Sense Research and Innovation Center, Lausanne and Sion, Switzerland.

Muhamed Barakovic (M)

Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; 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.

Lester Melie-Garcia (L)

Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; 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.

Meritxell Bach Cuadra (M)

CIBM Center for Biomedical Imaging, Lausanne, Switzerland; Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.

Cristina Granziera (C)

Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; 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. Electronic address: cristina.granziera@unibas.ch.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › How far MS lesion detection and segmentation...
questionsmedicales.fr › Sciences de l'information › Analyse des systèmes › Flux de travaux › How far MS lesion detection and segmentation...
questionsmedicales.fr › Établissements, main d'oeuvre et services de soins de santé › Établissements de santé › Établissements de soins ambulatoires › How far MS lesion detection and segmentation...
questionsmedicales.fr › Maladies du système immunitaire › Maladies auto-immunes › Maladies auto-immunes du système nerveux › Maladies démyélinisantes auto-immunes du SNC › Sclérose en plaques › How far MS lesion detection and segmentation...