{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T11:12:01Z","timestamp":1792494721865,"version":"4.5.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Chronic myelogenous leukemia (CML) is a clonal stem cell disorder accounting for 15% of adult leukemias. We aimed to determine if machine learning models could predict CML using blood cell counts prior to diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We identified patients with a diagnostic test for CML (BCR-ABL1) and at least 6 consecutive prior years of differential blood cell counts between 1999 and 2020 in the largest integrated health care system in the United States. Blood cell counts from different time periods prior to CML diagnostic testing were used to train, validate, and test machine learning models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The sample included 1,623 patients with BCR-ABL1 positivity rate 6.2%. The predictive ability of machine learning models improved when trained with blood cell counts closer to time of diagnosis: 2 to 5 years area under the curve (AUC), 0.59 to 0.67, 0.5 to 1 years AUC, 0.75 to 0.80, at diagnosis AUC, 0.87 to 0.92.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Blood cell counts collected up to 5 years prior to diagnostic workup of CML successfully predicted the BCR-ABL1 test result. These findings suggest a machine learning model trained with blood cell counts could lead to diagnosis of CML earlier in the disease course compared to usual medical care.<\/jats:p><\/jats:sec>","DOI":"10.1093\/ajcp\/aqab086","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T20:28:54Z","timestamp":1618864134000},"page":"1142-1148","source":"Crossref","is-referenced-by-count":34,"title":["A Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia With Retrospective Electronic Health Records Data"],"prefix":"10.1093","volume":"156","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7361-7162","authenticated-orcid":false,"given":"Ronald G","family":"Hauser","sequence":"first","affiliation":[{"name":"Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA"},{"name":"Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denise","family":"Esserman","sequence":"additional","affiliation":[{"name":"Yale School of Public Health, Department of Biostatistics, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6488-435X","authenticated-orcid":false,"given":"Lauren A","family":"Beste","sequence":"additional","affiliation":[{"name":"Veterans Affairs Puget Sound Healthcare System, Seattle, WA, USA"},{"name":"Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shawn Y","family":"Ong","sequence":"additional","affiliation":[{"name":"Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA"},{"name":"Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"suffix":"Jr","given":"Denis G","family":"Colomb","sequence":"additional","affiliation":[{"name":"Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA"},{"name":"Department of Medical Informatics, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankur","family":"Bhargava","sequence":"additional","affiliation":[{"name":"Department of Preventive Medicine, University of Kentucky, Lexington, KY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roxanne","family":"Wadia","sequence":"additional","affiliation":[{"name":"Department of Pathology, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michal G","family":"Rose","sequence":"additional","affiliation":[{"name":"Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA"},{"name":"Department of Cancer Center, Yale School of Medicine, New Haven, CT, USA"},{"name":"Department of Section of Medical Oncology, Department of Medicine, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,6,29]]},"reference":[{"key":"2021110810014973900_CIT0001","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21551","article-title":"Cancer statistics, 2019","volume":"69","author":"Siegel","year":"2019","journal-title":"CA Cancer J Clin."},{"key":"2021110810014973900_CIT0002","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1002\/ajh.25792","article-title":"Chronic myeloid leukemia: 2020 update on diagnosis, therapy and monitoring","volume":"95","author":"Jabbour","year":"2020","journal-title":"Am J Hematol."},{"key":"2021110810014973900_CIT0003","volume-title":"WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues.","author":"Swerdlow","year":"2008"},{"key":"2021110810014973900_CIT0004","first-page":"208","article-title":"P190BCR-ABL chronic myeloid leukaemia: the missing link with chronic myelomonocytic leukaemia?","volume":"8","author":"Melo","year":"1994","journal-title":"Leukemia."},{"key":"2021110810014973900_CIT0005","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1182\/blood-2009-02-204693","article-title":"Chronic myeloid leukemia (CML) with P190 BCR-ABL: analysis of characteristics, outcomes, and prognostic significance","volume":"114","author":"Verma","year":"2009","journal-title":"Blood."},{"key":"2021110810014973900_CIT0006","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.cca.2018.08.038","article-title":"A simple screening method for the diagnosis of chronic myeloid leukemia using the parameters of a complete blood count and differentials","volume":"489","author":"Ogasawara","year":"2019","journal-title":"Clin Chim Acta."},{"key":"2021110810014973900_CIT0007","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1007\/s12185-015-1777-1","article-title":"The criteria to suspect chronic myeloid leukemia using absolute basophil counts in peripheral blood","volume":"101","author":"Masuda","year":"2015","journal-title":"Int J Hematol."},{"key":"2021110810014973900_CIT0008","volume-title":"CML phases and prognostic factors","author":"Leukemia","year":"2021"},{"key":"2021110810014973900_CIT0009","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1038\/s41375-020-0776-2","article-title":"European LeukemiaNet 2020 recommendations for treating chronic myeloid leukemia","volume":"34","author":"Hochhaus","year":"2020","journal-title":"Leukemia."},{"key":"2021110810014973900_CIT0010","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"Xgboost: a scalable tree boosting system","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chen","year":"2016"},{"key":"2021110810014973900_CIT0011","volume-title":"Tree boosting with XGBoost-why does XGBoost win \u201cevery\u201d machine learning competition?: NTNU","author":"Nielsen","year":"2016"},{"key":"2021110810014973900_CIT0012","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1093\/jamia\/ocx046","article-title":"LabRS: a Rosetta stone for retrospective standardization of clinical laboratory test results","volume":"25","author":"Hauser","year":"2018","journal-title":"J Am Med Inform Assoc."},{"key":"2021110810014973900_CIT0013","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1093\/jamia\/ocx056","article-title":"Unit conversions between LOINC codes","volume":"25","author":"Hauser","year":"2018","journal-title":"J Am Med Inform Assoc."},{"key":"2021110810014973900_CIT0014","doi-asserted-by":"crossref","first-page":"478","DOI":"10.5858\/arpa.2019-0055-OA","article-title":"A 20-year evaluation of LOINC in the United States\u2019 largest integrated health system","volume":"144","author":"Bhargava","year":"2020","journal-title":"Arch Pathol Lab Med."},{"key":"2021110810014973900_CIT0015","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Interference, and Prediction.","author":"Hastie","year":"2009"},{"key":"2021110810014973900_CIT0016","doi-asserted-by":"crossref","first-page":"g7594","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ."},{"key":"2021110810014973900_CIT0017","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J R Stat Soc: Ser B (Methodological)"},{"key":"2021110810014973900_CIT0018","first-page":"1","volume-title":"glmnet: LASSO and elastic-net regularized generalized linear models. R package version","author":"Friedman","year":"2009"},{"key":"2021110810014973900_CIT0019","first-page":"1","author":"Chen"},{"key":"2021110810014973900_CIT0020","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics."},{"key":"2021110810014973900_CIT0021","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/1471-2105-12-77","article-title":"pROC: an open-source package for R and S+ to analyze and compare ROC curves","volume":"12","author":"Robin","year":"2011","journal-title":"BMC Bioinformatics."},{"key":"2021110810014973900_CIT0022","author":"Hauser","year":"2021"},{"key":"2021110810014973900_CIT0023"},{"key":"2021110810014973900_CIT0024","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1111\/bjh.16506","article-title":"Prospects for achieving treatment-free remission in chronic myeloid leukaemia","volume":"190","author":"Saglio","year":"2020","journal-title":"Br J Haematol."},{"key":"2021110810014973900_CIT0025","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.6004\/jnccn.2018.0071","article-title":"Chronic myeloid leukemia, version 1.2019, NCCN clinical practice guidelines in oncology","volume":"16","author":"Radich","year":"2018","journal-title":"J Natl Compr Canc Netw."},{"key":"2021110810014973900_CIT0026","doi-asserted-by":"crossref","first-page":"e67","DOI":"10.1371\/journal.pmed.0050067","article-title":"Observational research, randomised trials, and two views of medical science","volume":"5","author":"Vandenbroucke","year":"2008","journal-title":"PLoS Med."},{"key":"2021110810014973900_CIT0027","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1309\/AJCP19BFTHYTMORO","article-title":"Is the blood basophil count sufficiently precise, accurate, and specific?: three automated hematology instruments and flow cytometry compared","volume":"137","author":"Amundsen","year":"2012","journal-title":"Am J Clin Pathol."},{"key":"2021110810014973900_CIT0028","doi-asserted-by":"crossref","first-page":"101194","DOI":"10.1016\/j.beha.2020.101194","article-title":"CML\u2014not only BCR-ABL1 matters","volume":"33","author":"Rinke","year":"2020","journal-title":"Best Pract Res Clin Haematol."},{"key":"2021110810014973900_CIT0029","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.1182\/blood.V78.11.2969.2969","article-title":"Duration of the preclinical phase of chronic myelogenous leukemia: a case report","volume":"78","author":"Berman","year":"1991","journal-title":"Blood."},{"key":"2021110810014973900_CIT0030","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s12185-018-2528-x","article-title":"Systematic review of pre-clinical chronic myeloid leukaemia","volume":"108","author":"Kuan","year":"2018","journal-title":"Int J Hematol."}],"container-title":["American Journal of Clinical Pathology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ajcp\/article-pdf\/156\/6\/1142\/41071877\/aqab086.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ajcp\/article-pdf\/156\/6\/1142\/41071877\/aqab086.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T19:21:38Z","timestamp":1724872898000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ajcp\/article\/156\/6\/1142\/6310949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,29]]},"references-count":30,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6,29]]},"published-print":{"date-parts":[[2021,11,8]]}},"URL":"https:\/\/doi.org\/10.1093\/ajcp\/aqab086","relation":{},"ISSN":["0002-9173","1943-7722"],"issn-type":[{"value":"0002-9173","type":"print"},{"value":"1943-7722","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,12,1]]},"published":{"date-parts":[[2021,6,29]]}}}