ArticleCancers2022
A Deep Learning-Aided Automated Method for Calculating Metabolic Tumor Volume in Diffuse Large B-Cell Lymphoma.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00118209 (Phase III Randomized Study of R-CHOP V. Dose-Adjusted EPOCH-R With Molecular Profiling in Untreated De Novo Diffuse Large B-Cell Lymphomas), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Phase III Randomized Study of R-CHOP V. Dose-Adjusted EPOCH-R With Molecular Profiling in Untreated De Novo Diffuse Large B-Cell Lymphomas
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- 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
- Article
- Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Prognostic value of baseline body composition parameters fromAnnals of hematology · 2025Article
- Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Review
- The impact of initial tumor bulk in DLBCL treated with DA-EPOCH-RLeukemia & lymphoma · 2024Article
- Review
- Article
- Exploring the applicability of a lesion segmentation method on [European journal of hybrid imaging · 2023Article
- Clinical evaluation of deep learning-based automatic clinical target volume segmentation: a single-institution multi-site tumor experience.La Radiologia medica · 2023Article
- Quantitative PET-based biomarkers in lymphoma: getting ready for primetime.Nature reviews. Clinical oncology · 2023Review
- Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Metabolic tumor volume (MTV) is a robust prognostic biomarker in diffuse large B-cell lymphoma (DLBCL). The available semiautomatic software for calculating MTV requires manual input limiting its routine application in clinical research. Our objective was to develop a fully automated method (AM) for calculating MTV and to validate the method by comparing its results with those from two nuclear medicine (NM) readers. The automated method designed for this study employed a deep convolutional neural network to segment normal physiologic structures from the computed tomography (CT) scans that demonstrate intense avidity on positron emission tomography (PET) scans. The study cohort consisted of 100 patients with newly diagnosed DLBCL who were randomly selected from the Alliance/CALGB 50,303 (NCT00118209) trial. We observed high concordance in MTV calculations between the AM and readers with Pearson's correlation coefficients and interclass correlations comparing reader 1 to AM of 0.9814 (
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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.