A Multicenter Longitudinal MRI Study Assessing LeMan-PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients.
Détails
Télécharger: 36708267.pdf (973.45 [Ko])
Etat: Public
Version: Final published version
Licence: CC BY-NC 4.0
Etat: Public
Version: Final published version
Licence: CC BY-NC 4.0
ID Serval
serval:BIB_FC74FD68A743
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
A Multicenter Longitudinal MRI Study Assessing LeMan-PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients.
Périodique
Journal of magnetic resonance imaging
Collaborateur⸱rice⸱s
Swiss MS Cohort Study
ISSN
1522-2586 (Electronic)
ISSN-L
1053-1807
Statut éditorial
Publié
Date de publication
09/2023
Peer-reviewed
Oui
Volume
58
Numéro
3
Pages
864-876
Langue
anglais
Notes
Publication types: Multicenter Study ; Journal Article
Publication Status: ppublish
Publication Status: ppublish
Résumé
Detecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan-PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow-up of MS patients; however, multicenter validation studies are lacking.
To assess the accuracy of LeMan-PV for the longitudinal detection NEL white-matter MS lesions in a multicenter clinical setting.
Retrospective, longitudinal.
A total of 206 patients with a definitive MS diagnosis and at least two follow-up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow-up = 45.2 years (range: 36.9-52.8 years); 70 males.
Fluid attenuated inversion recovery (FLAIR) and T1-weighted magnetization prepared rapid gradient echo (T1-MPRAGE) sequences at 1.5 T and 3 T.
The study included 313 MRI pairs of datasets. Data were analyzed with LeMan-PV and compared with a manual "reference standard" provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating-accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1-score, lesion-wise False-Positive-Rate (aFPR), and other measures were used to assess LeMan-PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T.
Intraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers.
The interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10 <sup>-20</sup> , CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10 <sup>-12</sup> , CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan-PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1-score = 0.44, aFPR = 1.31. When both follow-ups were acquired at 3 T, LeMan-PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1-score = 0.28, aFPR = 3.03).
In this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan-PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan-PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological-routine flow.
4 TECHNICAL EFFICACY: Stage 2.
To assess the accuracy of LeMan-PV for the longitudinal detection NEL white-matter MS lesions in a multicenter clinical setting.
Retrospective, longitudinal.
A total of 206 patients with a definitive MS diagnosis and at least two follow-up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow-up = 45.2 years (range: 36.9-52.8 years); 70 males.
Fluid attenuated inversion recovery (FLAIR) and T1-weighted magnetization prepared rapid gradient echo (T1-MPRAGE) sequences at 1.5 T and 3 T.
The study included 313 MRI pairs of datasets. Data were analyzed with LeMan-PV and compared with a manual "reference standard" provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating-accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1-score, lesion-wise False-Positive-Rate (aFPR), and other measures were used to assess LeMan-PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T.
Intraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers.
The interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10 <sup>-20</sup> , CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10 <sup>-12</sup> , CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan-PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1-score = 0.44, aFPR = 1.31. When both follow-ups were acquired at 3 T, LeMan-PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1-score = 0.28, aFPR = 3.03).
In this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan-PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan-PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological-routine flow.
4 TECHNICAL EFFICACY: Stage 2.
Mots-clé
Male, Humans, Adult, Middle Aged, Multiple Sclerosis/diagnostic imaging, Multiple Sclerosis/pathology, White Matter/diagnostic imaging, White Matter/pathology, Cohort Studies, Retrospective Studies, Magnetic Resonance Imaging/methods, Brain/diagnostic imaging, Brain/pathology, lesion activity, lesion segmentation, longitudinal analysis, longitudinal lesion segmentation, multiple sclerosis, white matter lesions
Pubmed
Web of science
Open Access
Oui
Création de la notice
27/02/2023 11:45
Dernière modification de la notice
09/02/2024 8:55