ArticleEuropean journal of nuclear medicine and molecular imaging2018
Tumor fragmentation estimated by volume surface ratio of tumors measured on 18F-FDG PET/CT is an independent prognostic factor of diffuse large B-cell lymphoma.
Article in European journal of nuclear medicine and molecular imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B cell lymphoma: a systematic review.European journal of nuclear medicine and molecular imaging · 2021Pooled 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
- Prognostic value of combined baseline FDG PET/CT radiomic parameters in primary mediastinal B-cell lymphoma.Blood advances · 2026Article
- 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
- Review
- Prognostic value of the combination of volume, massiveness and fragmentation parameters measured on baseline FDG pet in high-burden follicular lymphoma.Scientific reports · 2024Article
- Article
- Prognostic value of whole-body dynamicHeliyon · 2023Article
- Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features.European journal of nuclear medicine and molecular imaging · 2022Article
- A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA.EJHaem · 2022Article
- Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting Outcome in Diffuse Large B-Cell Lymphoma.Cancers · 2022Article
- Predicting cancer outcomes with radiomics and artificial intelligence in radiology.Nature reviews. Clinical oncology · 2022Review
- Gross Tumor Volume Definition and Comparative Assessment for Esophageal Squamous Cell Carcinoma From 3DFrontiers in oncology · 2022Article
- FDG-PET/CT in Lymphoma: Where Do We Go Now?Cancers · 2021Article
- Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.European journal of nuclear medicine and molecular imaging · 2021Article
- Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients.EJNMMI physics · 2020Article
- Correlations between baselineEJNMMI research · 2020Article
- Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma.Radiology. Artificial intelligence · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionOur aim was to study the prognostic value of two new
methodsWe retrospectively included 215 patients with DLBCL. Patients underwent initial
resultsTVSR was poorly correlated with TMTV (ρ = 0.5) and TTS (ρ = 0.26), while TTS was highly correlated with TMTV (ρ = 0.94) and was, therefore, excluded from the analysis. TMTV had the highest area under the ROC curve (0.711) and the best sensitivity (0.797), while TVSR had the best specificity (0.745). The optimal cut-off values to predict 5-year OS were 222 cm
conclusionsTVSR measured on the initial
Indexed as
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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.