{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T02:43:07Z","timestamp":1780108987947,"version":"3.54.0"},"reference-count":27,"publisher":"European Respiratory Society (ERS)","issue":"6","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.ersjournals.com\/user-licence"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EPSRC Molecular Pathology Node"],"award-info":[{"award-number":["EPSRC Molecular Pathology Node"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","award":["NIHR Rare Disease Translational Research Consortiu"],"award-info":[{"award-number":["NIHR Rare Disease Translational Research Consortiu"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nottingham MRC"}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["Eur Respir J"],"accepted":{"date-parts":[[2020,11,17]]},"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to identify clusters of associated features which could be used to stratify patients and predict outcomes in individuals.<\/jats:p><\/jats:sec><jats:sec><jats:title>Patients and methods<\/jats:title><jats:p>Using unsupervised machine learning we generated patient clusters using data from 173 women with LAM from the UK and 186 replication subjects from the US National Heart, Lung, and Blood Institute (NHLBI) LAM registry. Prospective outcomes were associated with cluster results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Two- and three-cluster models were developed. A three-cluster model separated a large group of subjects presenting with dyspnoea or pneumothorax from a second cluster with a high prevalence of angiomyolipoma symptoms (p=0.0001) and tuberous sclerosis complex (TSC) (p=0.041). Patients in the third cluster were older, never presented with dyspnoea or pneumothorax (p=0.0001) and had better lung function. Similar clusters were reproduced in the NHLBI cohort. Assigning patients to clusters predicted prospective outcomes: in a two-cluster model the future risk of pneumothorax was 3.3 (95% CI 1.7\u20135.6)-fold greater in cluster 1 than cluster 2 (p=0.0002). Using the three-cluster model, the need for intervention for angiomyolipoma was lower in clusters 2 and 3 than cluster 1 (p&lt;0.00001). In the NHLBI cohort, the incidence of death or lung transplant was much lower in clusters 2 and 3 (p=0.0045).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Machine learning has identified clinically relevant clusters associated with complications and outcome. Assigning individuals to clusters could improve decision making and prognostic information for patients.<\/jats:p><\/jats:sec>","DOI":"10.1183\/13993003.03036-2020","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T19:28:58Z","timestamp":1611170938000},"page":"2003036","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis"],"prefix":"10.1183","volume":"57","author":[{"given":"Saisakul","family":"Chernbumroong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janice","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nishant","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8508-1871","authenticated-orcid":false,"given":"Suzanne","family":"Miller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7168-9464","authenticated-orcid":false,"given":"Francis X.","family":"McCormack","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9690-7074","authenticated-orcid":false,"given":"Jonathan M.","family":"Garibaldi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9837-2763","authenticated-orcid":false,"given":"Simon R.","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2020,12,10]]},"reference":[{"key":"2024102102110037000_57.6.2003036.1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.ccm.2016.04.002","article-title":"Lymphangioleiomyomatosis","volume":"37","author":"Johnson","year":"2016","journal-title":"Clin Chest Med"},{"key":"2024102102110037000_57.6.2003036.2","doi-asserted-by":"publisher","DOI":"10.1093\/qjmed\/hcr116"},{"key":"2024102102110037000_57.6.2003036.3","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.c848"},{"key":"2024102102110037000_57.6.2003036.4","doi-asserted-by":"publisher","DOI":"10.1378\/chest.09-0624"},{"key":"2024102102110037000_57.6.2003036.5","doi-asserted-by":"publisher","DOI":"10.1136\/thoraxjnl-2019-213338"},{"key":"2024102102110037000_57.6.2003036.6","doi-asserted-by":"publisher","DOI":"10.1186\/s13023-014-0151-3"},{"key":"2024102102110037000_57.6.2003036.7","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.200409-1298OC"},{"key":"2024102102110037000_57.6.2003036.8","doi-asserted-by":"publisher","DOI":"10.1136\/thorax.55.12.1052"},{"key":"2024102102110037000_57.6.2003036.9","doi-asserted-by":"publisher","DOI":"10.1378\/chest.126.6.1867"},{"key":"2024102102110037000_57.6.2003036.10","doi-asserted-by":"publisher","DOI":"10.1164\/ajrccm.160.2.9901027"},{"key":"2024102102110037000_57.6.2003036.11","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.02066-2018"},{"key":"2024102102110037000_57.6.2003036.12","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/S2213-2600(13)70090-0","article-title":"Serum VEGF-D concentration as a biomarker of lymphangioleiomyomatosis severity and treatment response: a prospective analysis of the Multicenter International Lymphangioleiomyomatosis Efficacy of Sirolimus (MILES) trial","volume":"1","author":"Young","year":"2013","journal-title":"Lancet Respir Med"},{"key":"2024102102110037000_57.6.2003036.13","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1719960115"},{"key":"2024102102110037000_57.6.2003036.14","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.00951-2018"},{"key":"2024102102110037000_57.6.2003036.15","doi-asserted-by":"publisher","DOI":"10.1016\/j.rmed.2003.11.013"},{"key":"2024102102110037000_57.6.2003036.16","doi-asserted-by":"publisher","DOI":"10.1136\/thx.2004.035154"},{"key":"2024102102110037000_57.6.2003036.17","doi-asserted-by":"publisher","DOI":"10.1378\/chest.129.5.1267"},{"key":"2024102102110037000_57.6.2003036.18","doi-asserted-by":"publisher","DOI":"10.1183\/09031936.00076209"},{"key":"2024102102110037000_57.6.2003036.19","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1164\/rccm.201709-1965ST","article-title":"Lymphangioleiomyomatosis Diagnosis and management: high-resolution chest computed tomography, transbronchial lung biopsy, and pleural disease management. 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