[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127640-en":3,"doc-seo-127640-105":31,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127640,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",7,"Healthcare","Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning","Background: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for multiple sclerosis diagnosis and treatment planning, traditionally relying on gadolinium-based contrast agents (GBCAs). Repeated GBCA use can increase gadolinium accumulation in tissues and raise healthcare costs. Objective: Implement nonlinear dimensionality reduction (NLDR) methods—locally linear embedding (LLE) and isometric feature mapping (Isomap)—to automatically identify active lesions on non-contrast brain MRI. Methods: Multiparametric MRI (FLAIR, T2, PD, pre- and post-contrast T1) was embedded via unsupervised NLDR; expert-labeled subtracted T1 lesions served as ground truth. Results: On 40 MS patients, median Dice similarity scores were 0.74±0.1 for LLE and 0.78±0.09 for Isomap, exceeding prior state-of-the-art methods. Conclusions: NLDR-based Isomap and LLE are viable options for active lesion identification using non-contrast images and may support clinical decision-making.","diagnostics  \nArticle  \nAutomatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning  \nJason Uwaeze 1, Ponnada A. Narayana 2, Arash Kamali 2, Vladimir Braverman 1, Michael A. Jacobs 1,2,3,4 and Alireza Akhbardeh 2,4, *  \nCitation: Uwaeze, J.; Narayana, P.A.; Kamali, A.; Braverman, V.; Jacobs, M.A.; Akhbardeh, A. Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning. Diagnostics 2024, 14, 632. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics14060632  \nAcademic Editors: Michał Strzelecki, Adam Piórkowski and Rafał Obuchowicz  \nReceived: 29 January 2024  \nRevised: 1 March 2024  \nAccepted: 2 March 2024  \nPublished: 16 March 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, Rice University, Houston, TX 77005, USA  \n2 Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA  \n3 Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA  \n4 The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA  \n* Correspondence: [alireza.akhbardeh@uth.tmc.edu](alireza.akhbardeh@uth.tmc.edu)  \nAbstract: Background: 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.  \nKeywords: multiple sclerosis; dimensionality reduction; multiparametric MRI; lesion segmentation  \n1. Introduction  \nMultiple sclerosis (MS) is the most common","cbCaipTz46lv3Tvj","https://ap.wps.com/l/cbCaipTz46lv3Tvj","pdf",1976933,4,1,14,"English","en",105,"# Introduction\n# Materials and Methods\n# Results\n# Discussion and Conclusions","[{\"question\":\"Why is identifying active lesions in multiple sclerosis important in MRI?\",\"answer\":\"Active lesions are essential for patient management and monitoring disease activity. They help guide diagnosis and treatment planning based on MRI findings.\"},{\"question\":\"How does the method avoid using gadolinium-based contrast agents?\",\"answer\":\"The study uses nonlinear dimensionality reduction (LLE and Isomap) to reconstruct multiparametric MRI into an embedded image, then identifies active lesions from non-contrast images rather than relying on contrast enhancement.\"},{\"question\":\"What data and ground truth were used to train and evaluate the approach?\",\"answer\":\"Multiparametric MRI inputs included FLAIR, T2-weighted, proton density-weighted, and pre/post-contrast T1-weighted images. Subtracted pre-and post-contrast T1 images were labeled by experts as active lesions for ground truth.\"},{\"question\":\"What performance did LLE and Isomap achieve?\",\"answer\":\"On 40 MS patients, LLE achieved a median Dice similarity score of 0.74±0.1 and Isomap achieved 0.78±0.09, outperforming current state-of-the-art methods mentioned in the study.\"}]","Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning | PDF",1785940452,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":91,"head_meta":93,"extra_data":95,"updated_unix":29},"automatic-active-lesion-tracking-in-multiple-sclerosis-using-unsupervised-machine-learning","",{"@graph":37,"@context":90},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/automatic-active-lesion-tracking-in-multiple-sclerosis-using-unsupervised-machine-learning/127640/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"Why is identifying active lesions in multiple sclerosis important in MRI?","Question",{"text":76,"@type":77},"Active lesions are essential for patient management and monitoring disease activity. They help guide diagnosis and treatment planning based on MRI findings.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the method avoid using gadolinium-based contrast agents?",{"text":81,"@type":77},"The study uses nonlinear dimensionality reduction (LLE and Isomap) to reconstruct multiparametric MRI into an embedded image, then identifies active lesions from non-contrast images rather than relying on contrast enhancement.",{"name":83,"@type":74,"acceptedAnswer":84},"What data and ground truth were used to train and evaluate the approach?",{"text":85,"@type":77},"Multiparametric MRI inputs included FLAIR, T2-weighted, proton density-weighted, and pre/post-contrast T1-weighted images. Subtracted pre-and post-contrast T1 images were labeled by experts as active lesions for ground truth.",{"name":87,"@type":74,"acceptedAnswer":88},"What performance did LLE and Isomap achieve?",{"text":89,"@type":77},"On 40 MS patients, LLE achieved a median Dice similarity score of 0.74±0.1 and Isomap achieved 0.78±0.09, outperforming current state-of-the-art methods mentioned in the study.","https://schema.org",{"og:url":53,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,123,128,133,136,140],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},40,"healthcare",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},8,"Research & Report",30,"research-report",{"id":129,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":47,"category_name":142,"show_sort_weight":111,"slug":143},19,"General","general"]