ArticleJCO clinical cancer informatics2026
Machine Learning Model Predicts Monoclonal Gammopathy Using Routine Laboratory Values.
Article in JCO clinical cancer informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeMonoclonal 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.
methodsThe 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 50-85 years, ≥3 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.
resultsThe 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.
conclusionOur 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.
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