ArticleEJNMMI physics2020
Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients.
Article in EJNMMI physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prognostic role of PET/CT volumetric parameters in paediatric Hodgkin lymphoma: A systematic review and expert recommendations from the International SEARCH for CAYAHL Group.British journal of haematology · 2026Pooled it
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- 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
- AI Improves Agreement and Reduces Time for Quantifying Metabolic Tumour Burden in Hodgkin Lymphoma.Hematology reports · 2025Article
- Prognostic impact of metabolic tumor volume using the SUV4.0 segmentation threshold in 1,960 lymphoma patients from prospective LYSA trials.European journal of nuclear medicine and molecular imaging · 2025Article
- Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Using a Longitudinally Aware Segmentation Network.Radiology. Artificial intelligence · 2025Article
- Review
- Article
- Comparative analysis of F-18 FDG PET/CT images between scrub typhus and systemic lupus erythematosus.Scientific reports · 2024Article
- Recent advances and impending challenges for the radiopharmaceutical sciences in oncology.The Lancet. Oncology · 2024Review
- TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images - a multi-center generalizability analysis.European journal of nuclear medicine and molecular imaging · 2024Article
- Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance.Bioengineering (Basel, Switzerland) · 2024Article
- Semiquantitative 2-[Frontiers in medicine · 2024Review
- Sensitivity of an AI method for [EJNMMI research · 2023Article
- Quantitative PET-based biomarkers in lymphoma: getting ready for primetime.Nature reviews. Clinical oncology · 2023Review
- Children's Oncology Group's 2023 blueprint for research: Hodgkin lymphoma.Pediatric blood & cancer · 2023Article
- An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients.Scientific reports · 2023Article
- A nomogram for predicting the rapid progression of diffuse large B-cell lymphoma established by combining baseline PET/CT total metabolic tumor volume, lesion diffusion, and TP53 mutations.Cancer medicine · 2023Article
- Importance of Central Imaging Review in a Pediatric Hodgkin Lymphoma Trial Using Positron Emission Tomography Response Adapted Radiation Therapy.International journal of radiation oncology, biology, physics · 2023Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
purposeFor pediatric lymphoma, quantitative FDG PET/CT imaging features such as metabolic tumor volume (MTV) are important for prognosis and risk stratification strategies. However, feature extraction is difficult and time-consuming in cases of high disease burden. The purpose of this study was to fully automate the measurement of PET imaging features in PET/CT images of pediatric lymphoma.
methods
resultsMedian Dice similarity coefficient of patient contours between automated and physician contours was 0.86 (IQR 0.78-0.91). Automated SUV
conclusionsAn automated method using an ensemble of multi-resolution pathway 3D CNNs was able to quantify PET imaging features of lymphoma on baseline FDG PET/CT images with excellent agreement to reference physician PET segmentation. Automated methods with faster throughput for PET quantitation, such as MTV and TLG, show promise in more accessible clinical and research applications.
Indexed as
Identifiers
What OpenQuestion holds
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.