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December 8, 2025BloodOpen Access

Metabolomics profiling and machine learning prediction of efficacy and prolonged cytopenia in patients with large B-cell lymphoma undergoing CAR-T therapy

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Authors

YWYì WángSYShuhua Yi

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Overview

Machine learning predicts efficacy and risk of prolonged cytopenia in large B-cell lymphoma, suggesting a novel prognostic model.

Key Points

  • This research aims to develop a model predicting early response and prolonged cytopenia risk in patients with large B-cell lymphoma undergoing CAR-T therapy.
  • Serum samples from 123 R/R large B-cell lymphoma patients collected before and after CAR-T therapy
  • Untargeted metabolomic sequencing performed on serum samples
  • Recursive feature elimination and LASSO regression to identify differential metabolites
  • Performance of Random Forest and Logistic Regression algorithms evaluated using area under the curve
  • Models predicted complete response within 3 months with an AUC of 0.99 (RFE) and 0.82 (Logistic Regression)
  • The model was independently validated, showing an AUC of 1.00 (RFE) and 0.84 (Logistic Regression)
  • Pretreatment serum metabolites predicted neutropenia risk (AUC=0.694) and thrombocytopenia risk (AUC=0.754) beyond 1 month.

Cite This Study

Wáng et al. (2025) studied this question.

synapsesocial.com/papers/69362f444fa91c937236d599https://doi.org/10.1182/blood-2025-1938
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  5. 5Predictors of response to CD19 chimeric antigen receptor T-cell therapy in large B-cell lymphoma: a consolidated review2025 · 1 citations