{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:51:50Z","timestamp":1753883510211,"version":"3.41.2"},"reference-count":38,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"9","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:sec>\n            <jats:title>PURPOSE<\/jats:title>\n            <jats:p>The diagnosis of chronic lymphocytic leukemia (CLL) is often delayed several years in advance of disease. Addressing this care gap would aid in identifying at-risk patients who may benefit from targeted evaluation to prevent adverse outcomes. To our knowledge, to date, however, there are no widely utilized machine learning (ML) models that predict development of CLL. Therefore, the objective of this study was to leverage readily available laboratory data to train and test the performance of ML-based risk models for abnormal lymphocytosis associated with CLL.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODS<\/jats:title>\n            <jats:p>\n              The observational study population was composed of deidentified laboratory data procured from a large US outpatient network. The 7-year longitudinal data set included 1,090,707 adult patients with the following inclusion criteria: age 50 to 75 years and initial absolute lymphocyte count (ALC) &lt;5 \u00d7 10\n              <jats:sup>9<\/jats:sup>\n              \/L. The data set was split into training and held-out test sets, where 80% of the data were used in training and 20% were used for independent testing. ML models were developed using random forest survival methods. The ground truth outcome was abnormal lymphocytosis associated with CLL and monoclonal B-cell lymphocytosis diagnosis: ALC \u22655 \u00d7 10\n              <jats:sup>9<\/jats:sup>\n              \/L with \u226540% relative lymphocytosis.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS<\/jats:title>\n            <jats:p>\n              The 12-variable risk classifier model accurately predicted ALC \u22655 \u00d7 10\n              <jats:sup>9<\/jats:sup>\n              \/L within 5 years and achieved an area under the curve receiver operating characteristic of 0.92. The most important predictors were ALC (initial, slope), WBC (last, max, slope, initial), platelet (last, slope, max, initial), age, and sex.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSION<\/jats:title>\n            <jats:p>Our ML risk classifier accurately predicts abnormal lymphocytosis associated with CLL using routine laboratory data. Although prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition for patients at a risk of CLL.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1200\/cci-24-00197","type":"journal-article","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T20:00:53Z","timestamp":1750795253000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Model Predicts Abnormal Lymphocytosis Associated With Chronic Lymphocytic Leukemia"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5458-5089","authenticated-orcid":true,"given":"Joseph","family":"Aoki","sequence":"first","affiliation":[{"name":"Sonic Healthcare USA, Austin, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar","family":"Khalid","sequence":"additional","affiliation":[{"name":"Sonic Healthcare USA, Austin, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2524-8072","authenticated-orcid":true,"given":"Cihan","family":"Kaya","sequence":"additional","affiliation":[{"name":"Sonic Healthcare USA, Austin, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed E.","family":"Salama","sequence":"additional","affiliation":[{"name":"Sonic Healthcare USA, Austin, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"233","reference":[{"volume-title":"Cancer Stat Facts: Leukemia\u2014Chronic Lymphocytic Leukemia (CLL)","year":"2024","key":"e_1_3_6_2_2","unstructured":"Cancer Stat Facts: Leukemia\u2014Chronic Lymphocytic Leukemia (CLL). National Cancer Institute Surveillance, Epidemiology, and End Results Program. 2024. https:\/\/seer.cancer.gov\/statfacts\/html\/clyl.html"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2016.96"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/ejh.12468"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2017-09-806398"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa075290"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2008-09-176933"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa0806122"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2009-08-237651"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2019-036006"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.1093\/ajcp\/aqz057"},{"key":"e_1_3_6_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-022-00117-4"},{"key":"e_1_3_6_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2009.287"},{"key":"e_1_3_6_14_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2015-02-585059"},{"key":"e_1_3_6_15_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2011-05-357251"},{"key":"e_1_3_6_16_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2013-08-523704"},{"key":"e_1_3_6_17_2","doi-asserted-by":"publisher","DOI":"10.2174\/1389202922666210705124359"},{"key":"e_1_3_6_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.xkme.2023.100692"},{"key":"e_1_3_6_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0316-z"},{"key":"e_1_3_6_20_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2024.0052"},{"key":"e_1_3_6_21_2","volume-title":"Advanced Laboratory Methods in Haematology","author":"Rowan RM","year":"2001","unstructured":"Rowan RM, van Assendelft OW, Preston FE: Advanced Laboratory Methods in Haematology. London, Taylor & Francis, 2001"},{"key":"e_1_3_6_22_2","unstructured":"World Health Organization (WHO): International Statistical Classification of Diseases Tenth Revision (ICD-10). Geneva Switzerland WHO 1992"},{"key":"e_1_3_6_23_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/303250"},{"key":"e_1_3_6_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40064-016-3113-5"},{"key":"e_1_3_6_25_2","first-page":"85","article-title":"A selective review on random survival forests for high dimensional data","volume":"36","author":"Wang H","year":"2017","unstructured":"Wang H, Li G: A selective review on random survival forests for high dimensional data. Quant Biosci 36:85-96, 2017","journal-title":"Quant Biosci"},{"key":"e_1_3_6_26_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2141.2009.07849.x"},{"key":"e_1_3_6_27_2","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2012.187"},{"key":"e_1_3_6_28_2","first-page":"S61","article-title":"Monoclonal B cell lymphocytosis in hepatitis C virus infected individuals","volume":"78","author":"Fazi C","year":"2010","unstructured":"Fazi C, Dagklis A, Cottini F, et al: Monoclonal B cell lymphocytosis in hepatitis C virus infected individuals. Cytometry B Clin Cytom 78:S61-S68, 2010 (suppl 1)","journal-title":"Cytometry B Clin Cytom"},{"key":"e_1_3_6_29_2","doi-asserted-by":"publisher","DOI":"10.1111\/bjh.13127"},{"key":"e_1_3_6_30_2","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2012.43.4449"},{"key":"e_1_3_6_31_2","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.25655"},{"key":"e_1_3_6_32_2","doi-asserted-by":"publisher","DOI":"10.1017\/S1463423623000129"},{"key":"e_1_3_6_33_2","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.21.01414"},{"key":"e_1_3_6_34_2","doi-asserted-by":"publisher","DOI":"10.4065\/75.10.1039"},{"key":"e_1_3_6_35_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12010138"},{"key":"e_1_3_6_36_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2005-12-051458"},{"key":"e_1_3_6_37_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2141.2011.08974.x"},{"key":"e_1_3_6_38_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2012-09-458265"},{"key":"e_1_3_6_39_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-14225-8"}],"container-title":["JCO Clinical Cancer Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ascopubs.org\/doi\/pdfdirect\/10.1200\/CCI-24-00197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T20:00:54Z","timestamp":1750795254000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascopubs.org\/doi\/10.1200\/CCI-24-00197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":38,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["10.1200\/CCI-24-00197"],"URL":"https:\/\/doi.org\/10.1200\/cci-24-00197","relation":{},"ISSN":["2473-4276"],"issn-type":[{"type":"electronic","value":"2473-4276"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"2024-08-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-14","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-06","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e2400197"}}