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Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.

Chong Jiang1, Chunjun Qian2,3,4, Zekun Jiang5

  • 1Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan, China.

European Journal of Nuclear Medicine and Molecular Imaging
|August 22, 2023
PubMed
Summary

Deep learning models accurately predict survival in diffuse large B cell lymphoma (DLBCL) patients using PET scans. These models serve as robust prognostic imaging biomarkers, improving patient risk stratification.

Keywords:
Deep learningDiffuse large B cell lymphomaPrognosisTransfer learning[18F]FDG PET/CT

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Diffuse large B cell lymphoma (DLBCL) is an aggressive non-Hodgkin lymphoma.
  • Accurate prognostic markers are crucial for guiding treatment decisions in DLBCL.
  • Current prognostic models may not fully capture the complexity of DLBCL patient outcomes.

Purpose of the Study:

  • To develop and validate prognostic imaging biomarkers from PET scans for survival prediction in DLBCL patients.
  • To utilize deep learning techniques for precise survival prediction.
  • To assess the performance of multiparametric models incorporating these biomarkers.

Main Methods:

  • Retrospective study of 684 DLBCL patients from three medical centers.
  • Generation of deep learning scores (DLS) using VGG19 and DenseNet121 convolutional neural networks on PET images.
  • Development and assessment of multiparametric models using Cox proportional hazards model, calibration curves, C-index, and DCA.

Main Results:

  • DLS showed significant associations with progression-free survival (PFS) and overall survival (OS).
  • Multiparametric models incorporating DLS demonstrated superior PFS (C-index: 0.866) and OS (C-index: 0.835) prediction in training cohorts.
  • External validation confirmed reliable model performance with C-indices of 0.760-0.770 for PFS and 0.748-0.766 for OS.

Conclusions:

  • Deep learning-derived scores (DLS) are robust prognostic imaging biomarkers for DLBCL patient survival.
  • The developed multiparametric models accurately stratify patients by survival risk.
  • These models hold promise for improving clinical decision-making in DLBCL management.