ArticleFrontiers in immunology2026
Multi-center validation of a machine learning model for early detection of monoclonal immunoglobulin-related disorders using routine laboratory data.
Article in Frontiers in immunology, 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
Background: Monoclonal immunoglobulin (MIg)-related disorders are clonal plasma cell diseases defined by aberrant MIg overproduction, typically presenting with subtle, non-specific manifestations that lead to underrecognition in routine clinical practice, delayed diagnosis, and subsequent irreversible organ damage and poor outcomes. Methods: To address this critical gap and facilitate timely MIg diagnosis, we developed and compared eight machine learning models using clinical and routine laboratory data. Cohort 1 (n=5018) included patients undergoing their first serum immunofixation electrophoresis (IFE) test at Xijing Hospital between 2017 and 2024, comprising 2038 MIg-positive and 2980 MIg-negative cases. The top-performing model was subsequently advanced for Cohort 2 (n=2270), consisting of three subsets (Set 1: Xijing Hospital, n=1753; Set 2: 3201 Hospital, n=398; Set 3: Shaanxi Provincial People's Hospital, n=119), served as a fully independent multi-institutional cohort, and was applied to assess the generalizability of the model. Results: Feature selection identified ten key variables-urinary cast count, mean corpuscular volume, prothrombin time, activated partial thromboplastin time, albumin/globulin ratio, globulin, total protein, calcium, age, and sex-which were used for final model construction. Among the eight machine learning algorithms, the light gradient boosting machine (LightGBM) model showed superior performance, achieving an AUC of 0.945 (87.0% sensitivity, 89.6% specificity) in the Cohort 2, and achieved 84.6% sensitivity, significantly outperforming the conventional serum protein electrophoresis (75.3% sensitivity) in the subset of cohort 2. In a separate cohort of newly enrolled patients, its 88.2% sensitivity also exceeded that of three non-specialist clinicians. Conclusions: This LightGBM model, built on readily available clinical and routine laboratory data, may help identify potential MIg-related cases and prioritize patients for further specialized confirmatory testing, which could facilitate earlier recognition and reduce diagnostic delays.
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