ArticleEuropean journal of nuclear medicine and molecular imaging2022
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 85 papers, 3 of them syntheses that pooled it.
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Who cites it
85 citing papers in PubMed, 3 syntheses or guidelines pooled it, 138 citations in OpenAlex.
- The prognostic role of maximum tumor dissemination derived by PET/CT in oncological diseases: a systematic review.Frontiers in medicine · 2025Pooled it
- Mapping the research landscape of PET/CT in lymphoma: insights from a bibliometric analysis.Frontiers in oncology · 2025Pooled it
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells.Journal of cancer research and clinical oncology · 2023Trial
- Diagnostic performance and generalizability of a clinical-ultrasound radiomics model for predicting extrathyroidal extension in thyroid carcinoma: a retrospective study.Annals of medicine · 2026Article
- Integrating Lymph Node Metastasis and Programmed Death-Ligand 1 Prediction in Non-Small Cell Lung Cancer From a Single PET/CT Scan: Multicenter Radiomics Study.JMIR medical informatics · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Improving the predictive value of end-of-treatment PET/ CT in diffuse large B-cell lymphoma.Haematologica · 2026Article
- Radiogenomics in Lymphoma and Multiple Myeloma: A Systematic Review of Current Evidence and Future Directions.Journal of clinical medicine · 2026Review
- Segmentation method comparison for baseline [EJNMMI physics · 2026Article
- Can we obtain prognostic information from healthy tissue uptake and volume in baselineEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Glycolysis-Driven Immune Subtypes in the Tumor Microenvironment Determine Clinical Outcomes in Diffuse Large B-Cell Lymphoma.Cancers · 2025Article
- Automatic Lugano staging for risk stratification in lymphoma: a multicenter PET radiomics and machine learning study with survival analysis.Nuclear medicine communications · 2025Article
- Divergent prognostic utility of metabolic risk scores in large B-cell lymphoma subtypes: a real-world analysis.European journal of nuclear medicine and molecular imaging · 2025Article
- Predicting 2-year time to progression in diffuse large B cell lymphoma using 3D CNNs on whole-body PET/CT scans.EJNMMI research · 2025Article
- Tumor metabolic heterogeneity based onBMC cancer · 2025Article
- Predictive value of maximum tumor dissemination (Dmax) in lymphoma patients treated with CD19-specific CAR T-Cells.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Risk prediction in diffuse large B-cell lymphoma improves when combining baseline PET features with interim PET response.Haematologica · 2025Article
- A proof of concept study ofScientific reports · 2025Article
- Integration of [Leukemia · 2025Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAccurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment.
methodsThree hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value ≥ 4.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values.
resultsThe IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUV
conclusionPrediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance. TRIAL REGISTRATION NUMBER AND DATE: EudraCT: 2006-005,174-42, 01-08-2008.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.