ArticleEuropean journal of nuclear medicine and molecular imaging2022
Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances.Medical sciences (Basel, Switzerland) · 2025Pooled it
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- Multi-Modal Ultrasound-Based Prognostic Model for Diffuse Large B-Cell Lymphoma with Predominantly Superficial Lymph Node Involvement.Diagnostics (Basel, Switzerland) · 2026Article
- Prognostic value of a combined model integrating clinical and PET radiomics parameters in metastatic melanoma: A dual-center retrospective study.Annals of nuclear medicine · 2026Article
- Automatic Lugano staging for risk stratification in lymphoma: a multicenter PET radiomics and machine learning study with survival analysis.Nuclear medicine communications · 2025Article
- A proof of concept study ofScientific reports · 2025Article
- BaselineClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Radiomics based onQuantitative imaging in medicine and surgery · 2025Article
- The predictive power ofAnnals of hematology · 2025Article
- Clinical scoring systems, molecular subtypes and baseline [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- ExplainableJournal of cancer research and clinical oncology · 2024Article
- Review
- Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning.Journal of cancer research and clinical oncology · 2024Article
- Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients.Scientific reports · 2024Article
- Evaluation of therapeutic effect and prognostic value ofEJNMMI research · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- PET-based radiomic feature based on the cross-combination method for predicting the mid-term efficacy and prognosis in high-risk diffuse large B-cell lymphoma patients.Frontiers in oncology · 2024Article
- BaselineEJNMMI research · 2023Article
- Article
- OptimalFrontiers in oncology · 2023Article
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8 authors.
Funding
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Abstract
purposeTo develop and externally validate models incorporating a PET radiomics signature (R-signature) obtained by the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL).
methodsA total of 383 patients with DLBCL from two medical centres between 2011 and 2019 were included. The cross-combination method was used on three types of PET radiomics features from the training cohort to generate 49 feature selection-classification candidates based on 7 different machine learning models. The R-signature was then built by selecting the optimal candidates based on their progression-free survival (PFS) and overall survival (OS). Cox regression analysis was used to develop the survival prediction models. The calibration, discrimination, and clinical utility of the models were assessed and externally validated.
resultsThe R-signatures determined by 12 and 31 radiomics features were significantly associated with PFS and OS, respectively (P<0.05). The combined models that incorporated R-signatures, metabolic metrics, and clinical risk factors exhibited significant prognostic superiority over the clinical models, PET-based models, and the National Comprehensive Cancer Network International Prognostic Index in terms of both PFS (C-index: 0.801 vs. 0.732 vs. 0.785 vs. 0.720, respectively) and OS (C-index: 0.807 vs. 0.740 vs. 0.773 vs. 0.726, respectively). For external validation, the C-indices were 0.758 vs. 0.621 vs. 0.732 vs. 0.673 and 0.794 vs. 0.696 vs. 0.781 vs. 0.708 in the PFS and OS analyses, respectively. The calibration curves showed good consistency, and the decision curve analysis supported the clinical utility of the combined model.
conclusionThe R-signature could be used as a survival predictor for DLBCL, and its combination with clinical factors may allow for accurate risk stratification.
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