ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2021
Automated Segmentation of Baseline Metabolic Total Tumor Burden in Diffuse Large B-Cell Lymphoma: Which Method Is Most Successful? A Study on Behalf of the PETRA Consortium.
Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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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
62 citing papers in PubMed, 108 citations in OpenAlex.
- Malignancy Detection Rates of [Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Trial
- Health-related quality of life in patients with aggressive non-Hodgkin lymphoma: results from the PETAL trial.Annals of hematology · 2025Trial
- Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.BMC medical imaging · 2025Trial
- Trial
- PET-guided early identification of CAR-T candidates in diffuse large b-cell lymphoma: a multidisciplinary expert consensus.European journal of nuclear medicine and molecular imaging · 2026Article
- Article
- Validation of an AI Method for Automated Lymphoma Metabolic Tumor Volume Segmentation Using a Public Benchmark PET/CT Dataset.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Persistence of PET scan positivity after chemoimmunotherapy for DLBCL.Blood advances · 2026Review
- A Lesion-adaptive Segmentation Approach for Tumor Delineation on FDG PET/CT in Diffuse Large B-cell Lymphoma Patients.European journal of nuclear medicine and molecular imaging · 2026Article
- Artificial intelligence for TNM staging in NSCLC: a critical appraisal of segmentation utility in [European journal of nuclear medicine and molecular imaging · 2026Article
- Segmentation method comparison for baseline [EJNMMI physics · 2026Article
- [European journal of nuclear medicine and molecular imaging · 2026Article
- Association between metabolic tumor burden and health-related quality of life in patients with classic Hodgkin lymphoma.European journal of nuclear medicine and molecular imaging · 2026Article
- Prognostic Value of Qualitative Splenic [Cancers · 2026Article
- Can we obtain prognostic information from healthy tissue uptake and volume in baselineEuropean journal of nuclear medicine and molecular imaging · 2026Article
- AI Improves Agreement and Reduces Time for Quantifying Metabolic Tumour Burden in Hodgkin Lymphoma.Hematology reports · 2025Article
- MTV-guided radiotherapy bridging in large B-cell lymphoma patients receiving CD19 CAR T-cell therapy.British journal of haematology · 2025Article
- Risk prediction in diffuse large B-cell lymphoma improves when combining baseline PET features with interim PET response.Haematologica · 2025Article
- Integration of [Leukemia · 2025Article
- Prognostic Value of Multiple Manual Segmentation Methods for Diffuse Large B-Cell Lymphoma withCurrent oncology (Toronto, Ont.) · 2025Article
2 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 2 countries.
Funding
Abstract
Metabolic tumor volume (MTV) is a promising biomarker of pretreatment risk in diffuse large B-cell lymphoma (DLBCL). Different segmentation methods can be used that predict prognosis equally well but give different optimal cutoffs for risk stratification. Segmentation can be cumbersome; a fast, easy, and robust method is needed. Our aims were to evaluate the best automated MTV workflow in DLBCL; determine whether uptake time, compliance or noncompliance with standardized recommendations for
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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.