{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T15:19:03Z","timestamp":1780327143784,"version":"3.54.1"},"reference-count":52,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"2","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Monoclonal gammopathy (MG) is a disorder defined by the presence of monoclonal immunoglobulin. Monoclonal protein (M-protein) serves as an important biomarker for the spectrum of MG that includes plasma cell myeloma (PCM) and, its precursor, MG of undetermined significance (MGUS). Recent evidence suggest early identification of MGUS before the onset of PCM is associated with improvement in overall survival. Despite these benefits, the recognition rate of MG remains inadequate. Addressing this care gap would help identify at-risk patients who may benefit from targeted evaluation to prevent adverse outcomes. However, to date, to our knowledge, there are no widely used machine learning (ML) models that predict MG. Therefore, our aim was to leverage routine laboratory data to train and evaluate the performance of ML-based risk models for M-protein associated with MG.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>The study population was composed of deidentified laboratory data procured from a cohort of 232,813 individuals within a large US outpatient network. The 7-year longitudinal data set included 1,610 patients with the following inclusion criteria: age 50\u201385 years, \u22653 complete blood count and metabolic panel results, and at least one protein electrophoresis result. ML models were developed using XGBoost. The reference outcome was M-protein.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>The seven-variable risk classifier model accurately predicted M-protein within 5 years and achieved an AUC of 0.84. The most important predictors were absolute lymphocyte trajectory, age, RBC, total protein, RBC distribution width, blood urea nitrogen, and relative eosinophils.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>Our ML risk classifier accurately predicted the presence of M-protein 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 risk for MG.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-26-00013","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T20:00:13Z","timestamp":1779393613000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Model Predicts Monoclonal Gammopathy Using Routine Laboratory Values"],"prefix":"10.1200","volume":"10","author":[{"given":"Mercedeh","family":"Movassagh","sequence":"first","affiliation":[{"name":"Sonic Healthcare USA, Dallas, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2524-8072","authenticated-orcid":true,"given":"Cihan","family":"Kaya","sequence":"additional","affiliation":[{"name":"Sonic Healthcare USA, Dallas, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Con","family":"Skordis","sequence":"additional","affiliation":[{"name":"Sonic Healthcare USA, Dallas, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ella","family":"Aoki","sequence":"additional","affiliation":[{"name":"Sonic Healthcare USA, Dallas, TX"},{"name":"'Iolani School, Honolulu, HI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarush","family":"Kothari","sequence":"additional","affiliation":[{"name":"Sonic Healthcare USA, Dallas, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed E.","family":"Salama","sequence":"additional","affiliation":[{"name":"Sonic Healthcare USA, Dallas, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5458-5089","authenticated-orcid":true,"given":"Joseph","family":"Aoki","sequence":"additional","affiliation":[{"name":"Sonic Healthcare USA, Dallas, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"233","reference":[{"key":"e_1_3_6_2_2","volume-title":"Haematolymphoid Tumours","author":"WHO Classification of Tumours Editorial Board","year":"2024","unstructured":"WHO Classification of Tumours Editorial Board: Haematolymphoid Tumours. 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