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Related Experiment Videos

Baseline [18F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line

Zeying Wen1, Xiaohe Gao2, Qingxia Wu3

  • 1Department of Radiology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China.

BMC Cancer
|January 24, 2025
PubMed
Summary

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<sup>18</sup>F-FDG PET/CT-based radiomics for differentiating low-grade and grade 3A of follicular lymphoma.

BMC medical imaging·2026

Machine learning models combining radiomics and clinical data can predict treatment response in follicular lymphoma (FL) patients. This approach shows promise for assessing interim efficacy in FL management.

Area of Science:

  • Oncology
  • Medical Imaging
  • Machine Learning

Background:

  • Follicular lymphoma (FL) is a common indolent non-Hodgkin lymphoma.
  • Accurate prediction of interim treatment efficacy is crucial for optimizing patient management.
  • Current prediction methods may not fully leverage the potential of imaging and clinical data.

Purpose of the Study:

  • To evaluate the predictive capability of machine learning-based radiomics from PET/CT scans combined with clinical risk factors for interim treatment efficacy in FL.
  • To develop and validate a predictive model for early assessment of treatment response.

Main Methods:

  • Retrospective analysis of 97 FL patients' PET/CT data.
  • Extraction of radiomics features (first-order, shape, texture) using LIFEx and uAI Research Portal.
Keywords:
Follicular lymphomaInterim efficacyRadiomics

Related Experiment Videos

  • Application of univariate analysis, MRMR, and LASSO for feature selection.
  • Development of logistic regression models (clinical, radiomics, combined) using five-fold cross-validation.

    • Main Results:

    • A combined radiomics-clinical model achieved the highest predictive performance.
    • The combined model yielded a mean AUC of 0.849 and an accuracy of 0.795.
    • Seven clinical factors and ten radiomics features were identified as significant predictors.

    Conclusions:

    • A combined model integrating baseline [18F]FDG PET/CT radiomics and clinical risk factors shows potential for predicting interim efficacy in FL.
    • This approach may aid in early identification of treatment response, facilitating timely therapeutic adjustments.