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December 8, 2025BloodOpen Access

Deep learning-based image analysis of pretreatment FDG-PET/CT predicts CAR-T cell treatment outcome at month-12 for patients with Relapsed/Refractory large B-cell lymphomas

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Authors

YTYubing TongCWCaiyun Wu

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Overview

Analysis predicts patient outcomes at Month-12 for CAR-T therapy, suggesting deep learning techniques improve prognosis assessment.

Key Points

  • This research aims to predict treatment outcomes for patients with relapsed large B-cell lymphomas undergoing CAR-T therapy using image analysis techniques.
  • Evaluated pre-infusion FDG-PET/CT and LD-CT images from patients treated with tisagenlecleucel.
  • Used deep learning for lesion-level prediction of treatment outcomes in 66 evaluable patients.
  • Compared predictions with established prognostic indices like serum LDH and sIPI.
  • Achieved 77% sensitivity in predicting Responder status for Month-12 outcomes using deep learning.
  • Serum LDH showed 100% sensitivity but low specificity in identifying outcomes.
  • sIPI demonstrated moderate sensitivity (65%) and specificity (62%) in predicting non-responders.

Cite This Study

Tong et al. (2025) studied this question.

synapsesocial.com/papers/69362f444fa91c937236d5b4https://doi.org/10.1182/blood-2025-2585
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predicting survival outcomes in CAR-t treated B-cell lymphoma by combining PET/CT imagingand clinical features2025
  2. 2Added prognostic value of baseline pre-infusion 18F-FDG PET/CT in diffuse large B-cell lymphoma patients receiving chimeric antigen receptor T-cell therapy2025
  3. 3Systematic review and meta−analysis of PET−based prognostic metrics in CAR−T treatment of DLBCL2026 · 2 citations
  4. 4Predicting survival, neurotoxicity and response in B-Cell lymphoma patients treated with CAR-T therapy using an imaging features-based model2024
  5. 5[18F]FDG-PET/CT in DLBCL-patients treated with CAR-T cell therapy: potential for defining patient prognosis2025