Predictive biomarkers validation of CD3⁺ cell apheresis yield in CAR-T manufacturing for diffuse large B-cell lymphoma: a machine learning approach.

ABSTRACT: Chimeric antigen receptor (CAR) T-cell therapy has shown significant success in treating diffuse large B-cell lymphoma (DLBCL). The initial step involves collecting autologous CD3⁺ lymphocytes through apheresis, in which obtaining an adequate CD3⁺ cell yield is essential for therapeutic efficacy. Despite prior research, the factors influencing CD3⁺ cell apheresis remain poorly understood. Traditional statistical analyses offer limited insights, but machine learning (ML) approaches enable precision modeling of clinical predictors owing to their advanced pattern-recognition capabilities. In this study, we employed three ML algorithms, random forest classifier (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) to analyze a homogeneous coho

SUBMITTER: Carbonell D 

PROVIDER: S-EPMC12663458 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature