ArticleCurrent medical science2026
Machine Learning-Based Prediction of Mononuclear Cell Collection Efficiency for CAR-T Manufacturing.
Article in Current medical science, 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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5 authors.
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Abstract
objectiveTo develop and internally validate an interpretable machine-learning model using routine precollection variables to predict mononuclear cell (MNC) collection efficiency (CE) during leukapheresis for CAR-T manufacturing.
methodsThis retrospective study included 206 consecutive patients who underwent leukapheresis between 2022 and 2025. Candidate variables were screened with Pearson correlation, Boruta, recursive feature elimination, and variance-threshold filters. Six supervised algorithms were trained with nested cross-validation, using an inner fivefold loop for tuning and an outer tenfold loop repeated three times for performance estimation. Bootstrap resampling (1000 iterations) generated confidence intervals, and SHAP analysis was used for interpretation.
resultsExtreme gradient boosting (XGBoost) showed the best continuous prediction performance, with an R
conclusionRoutine precollection variables estimated MNC CE with good internal validity and may support individualized leukapheresis planning before CAR-T manufacturing.
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