18F-FDG PET/CT radiomics for predicting relapse within 5 years after complete response in diffuse large B-cell lymphoma

This retrospective study investigates whether pre-treatment 18 F-FDG PET/CT radiomics enhances prediction of 5-year relapse risk in diffuse large B-cell lymphoma (DLBCL) patients achieving complete response (CR). Between July 2011 and February 2019, 147 CR-DLBCL patients were divided into recurrence ( n = 39) and non-recurrence ( n = 108) groups. Three prediction models were constructed: Model 1 (clinical/conventional PET parameters), Model 2 (radiomics features), and Model 3 (integrated features). Random forest analysis was used for feature selection and model building. Performance was evaluated via 5-fold cross-validation (internal test) and an external validation cohort ( n = 28, 10 recurrences). The area under the receiver operating characteristic curve (AUC) was calculated, and differences between models were compared using the Delong test. Kaplan–Meier estimates and log-rank tests assessed progression-free survival (PFS). Internal testing showed Model 1 had a mean AUC of 0.579 ± 0.09, while Model 2 achieved 0.733 ± 0.06. In external validation, Model 3 improved AUC from 0.625 (Model 1) to 0.828 and yielded a higher hazard ratio (HR = 10.981) for PFS prediction compared to Model 2 (HR = 3.492). Pre-treatment 18 F-FDG PET/CT radiomics may provide complementary information for predicting 5-year relapse risk in patients with CR-DLBCL.

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Journal
European journal of medical research
Published
2026-10-06
DOI
https://doi.org/10.1186/s40001-026-05304-w
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

18F-FDG PET/CT radiomics for predicting relapse within 5 years after complete response in diffuse large B-cell lymphoma

Xiaozhu Lin, Siwen Wang, 李培勇, Lei Jiang et al.
European journal of medical research
Radiomics and Machine Learning in Medical Imaging
article

18F-FDG PET/CT radiomics for predicting relapse within 5 years after complete response in diffuse large B-cell lymphoma

Xiaozhu Lin, Siwen Wang, 李培勇, Lei Jiang, Suyun Chen, 江旭峰, Xiaoyue Tan, Wang Li, Yaya Bai, Xinyun Huang
article en

Abstract

This retrospective study investigates whether pre-treatment 18 F-FDG PET/CT radiomics enhances prediction of 5-year relapse risk in diffuse large B-cell lymphoma (DLBCL) patients achieving complete response (CR). Between July 2011 and February 2019, 147 CR-DLBCL patients were divided into recurrence ( n = 39) and non-recurrence ( n = 108) groups. Three prediction models were constructed: Model 1 (clinical/conventional PET parameters), Model 2 (radiomics features), and Model 3 (integrated features). Random forest analysis was used for feature selection and model building. Performance was evaluated via 5-fold cross-validation (internal test) and an external validation cohort ( n = 28, 10 recurrences). The area under the receiver operating characteristic curve (AUC) was calculated, and differences between models were compared using the Delong test. Kaplan–Meier estimates and log-rank tests assessed progression-free survival (PFS). Internal testing showed Model 1 had a mean AUC of 0.579 ± 0.09, while Model 2 achieved 0.733 ± 0.06. In external validation, Model 3 improved AUC from 0.625 (Model 1) to 0.828 and yielded a higher hazard ratio (HR = 10.981) for PFS prediction compared to Model 2 (HR = 3.492). Pre-treatment 18 F-FDG PET/CT radiomics may provide complementary information for predicting 5-year relapse risk in patients with CR-DLBCL.

European journal of medical research
Shanghai Jiao Tong University (CN), XinHua Hospital (CN), Ruijin Hospital (CN), Guangdong Academy of Medical Sciences (CN)
Openalex Percentile: Top 12%
Radiomics and Machine Learning in Medical Imaging
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18F-FDG PET/CT radiomics for predicting relapse within 5 years after complete response in diffuse large B-cell lymphoma — Xiaozhu Lin, Siwen Wang, et al. · European journal of medical research (2026) | TGRS Research Map | TGRS