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18F-FDG PET-Based Combined Baseline and End-Of-Treatment Radiomics Model Improves the Prognosis Prediction in Diffuse Large B Cell Lymphoma After First-Line Therapy

  • Yingpu Cui
    Affiliations
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China

    Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
    Search for articles by this author
  • Yongluo Jiang
    Affiliations
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China

    Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
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  • Xi Deng
    Affiliations
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China

    Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
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  • Wen Long
    Affiliations
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China

    Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
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  • Baocong Liu
    Affiliations
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China

    Department of Medical imaging, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
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  • Author Footnotes
    ⁎ Wei Fan, Yinghe Li, and Xu Zhang equally contributed to and co-supervised this work.
    Wei Fan
    Footnotes
    ⁎ Wei Fan, Yinghe Li, and Xu Zhang equally contributed to and co-supervised this work.
    Affiliations
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China

    Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
    Search for articles by this author
  • Author Footnotes
    ⁎ Wei Fan, Yinghe Li, and Xu Zhang equally contributed to and co-supervised this work.
    Yinghe Li
    Footnotes
    ⁎ Wei Fan, Yinghe Li, and Xu Zhang equally contributed to and co-supervised this work.
    Affiliations
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China

    Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
    Search for articles by this author
  • Author Footnotes
    ⁎ Wei Fan, Yinghe Li, and Xu Zhang equally contributed to and co-supervised this work.
    Xu Zhang
    Correspondence
    Address correspondence to: Xu Zhang, Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfengdong Road, Guangzhou, Guangdong, China
    Footnotes
    ⁎ Wei Fan, Yinghe Li, and Xu Zhang equally contributed to and co-supervised this work.
    Affiliations
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China

    Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
    Search for articles by this author
  • Author Footnotes
    ⁎ Wei Fan, Yinghe Li, and Xu Zhang equally contributed to and co-supervised this work.
Published:November 25, 2022DOI:https://doi.org/10.1016/j.acra.2022.10.011

      Rationale and Objectives

      To develop a combined model incorporating the clinical and PET features for identifying patients with diffuse large B-cell lymphoma (DLBCL) at high risk of progression or relapse after first-line therapy, compared to International Prognostic Index (IPI) and Deauville score (DS) assessment.

      Materials and Methods

      271 18F-FDG PET images with DLBCL were retrospectively collected and randomly divided into the training (n=190) and test dataset (n=81). All visible lesions were annotated. Baseline, end-of-treatment (EoT), and delta PET radiomics features were extracted. IPI model, the baseline clinical model group (MG), DS model, the combined clinical MG, the PET-based radiomics MG, and the combined MG were constructed to predict 2-year time to progression (2Y-TTP). For each MG, the cross-combination method was performed to generate 1680 candidate models based on three normalization methods, 20 features, 4 feature-selection methods, and 7 classifiers. The model achieving the highest AUC was selected as the best for each MG. Cox regression analysis was further performed.

      Results

      In the test set, the best combined model showed better discriminative power compared to IPI model, the best baseline clinical model, DS model, the best combined clinical model, and the best PET-based radiomics model (AUC 0.898 vs. 0.584, 0.695, 0.756, 0.824, 0.832; p < 0.001, 0.014, 0.018, 0.152, 0.042, respectively). The combined model was superior to other models for progression-free-survival prediction (C-index: 0.853 vs. 0.568, 0.666, 0.753, 0.808, 0.814, respectively).

      Conclusion

      A combined model for identifying patients at high risk of progression or relapse after first-line therapy was constructed, superior to IPI and DS assessment.

      Key Words

      Abbreviations:

      DLBCL (Diffuse large B cell lymphoma), EoT (end-of-treatment), IPI (international prognostic index), DS (Deauville scores), SUV (standard uptake value), TLG (total lesion glycolysis), MTV (metabolic tumor volume), ICC (intraclass correlation coefficient), 2Y-TTP (2-year time to progression), PFS (progression-free survival), OS (overall survival), MG (model group), CV (cross-validation)
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