Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

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dc.citation.articleNumber632
dc.citation.issueNumber6
dc.citation.journalTitleDiagnostics
dc.citation.volumeNumber14
dc.contributor.authorUwaeze, Jason
dc.contributor.authorNarayana, Ponnada A.
dc.contributor.authorKamali, Arash
dc.contributor.authorBraverman, Vladimir
dc.contributor.authorJacobs, Michael A.
dc.contributor.authorAkhbardeh, Alireza
dc.date.accessioned2024-07-25T20:55:16Z
dc.date.available2024-07-25T20:55:16Z
dc.date.issued2024
dc.description.abstractBackground: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. Objective: To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. Materials and Methods: Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. Results: LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. Conclusions: NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.
dc.identifier.citationUwaeze, J., Narayana, P. A., Kamali, A., Braverman, V., Jacobs, M. A., & Akhbardeh, A. (2024). Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning. Diagnostics, 14(6), Article 6. https://doi.org/10.3390/diagnostics14060632
dc.identifier.digitaldiagnostics-14-00632-v2
dc.identifier.doihttps://doi.org/10.3390/diagnostics14060632
dc.identifier.urihttps://hdl.handle.net/1911/117510
dc.language.isoeng
dc.publisherMDPI
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleAutomatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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