ArticleEuropean journal of nuclear medicine and molecular imaging2016
Article in European journal of nuclear medicine and molecular imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- EANM guideline on the role of 2-[European journal of nuclear medicine and molecular imaging · 2021Guideline
- Repeatability and Reproducibility of Radiomic Features: A Systematic Review.International journal of radiation oncology, biology, physics · 2018Pooled it
- Dual-Time-Point Radiomics for Prognosis Prediction in Colorectal Liver Metastasis Treated with Neoadjuvant Therapy Before Radical Resection: A Two-Center Study.Annals of surgical oncology · 2025Article
- On the robustness of [18F]-FDG-PET radiomic features to variations in image acquisition and reconstruction settings: A phantom study.PloS one · 2025Article
- Artificial Intelligence and Radiomics Applied to Prostate Cancer Bone Metastasis Imaging: A Review.iRadiology · 2024Article
- AnJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Exploring published and novel pre-treatment CT and PET radiomics to stratify risk of progression among early-stage non-small cell lung cancer patients treated with stereotactic radiation.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024Article
- Predicting the Efficacy of SBRT for Lung Cancer withLife (Basel, Switzerland) · 2023Article
- A review of harmonization strategies for quantitative PET.Annals of nuclear medicine · 2023Review
- Prediction of the Presence of Targetable Molecular Alteration(s) with Clinico-MetabolicDiagnostics (Basel, Switzerland) · 2022Article
- A Systematic Review of PET Textural Analysis and Radiomics in Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- Radiomics in medical imaging-"how-to" guide and critical reflection.Insights into imaging · 2020Review
- BaselineEuropean radiology · 2020Article
- Predicting EGFR mutation subtypes in lung adenocarcinoma usingTranslational lung cancer research · 2020Article
- Introduction to Radiomics.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2020Review
- Experimental Multicenter and Multivendor Evaluation of the Performance of PET Radiomic Features Using 3-Dimensionally Printed Phantom Inserts.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2020Article
- Radiomics for radiation oncologists: are we ready to go?BJR open · 2020Review
- Positron emission tomography PET/CT harmonisation study of different clinical PET/CT scanners using commercially available software.BJR open · 2020Article
- Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis.European journal of nuclear medicine and molecular imaging · 2019Review
- NCTN Assessment on Current Applications of Radiomics in Oncology.International journal of radiation oncology, biology, physics · 2019Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeQuantification of tumour heterogeneity in PET images has recently gained interest, but has been shown to be dependent on image reconstruction. This study aimed to evaluate the impact of the EANM/EARL accreditation program on selected
methodsTo carry out our study, we prospectively analysed 71 tumours in 60 biopsy-proven lung cancer patient acquisitions reconstructed with unfiltered point spread function (PSF) positron emission tomography (PET) images (optimised for diagnostic purposes), PSF-reconstructed images with a 7-mm Gaussian filter (PSF
resultsVolumes obtained with SUV
conclusionPSF reconstruction with Gaussian filtering chosen to meet harmonising standards resulted in similar SUV values and heterogeneity information as compared to OSEM images, which validates its use within the harmonisation strategy context. However, unfiltered PSF-reconstructed images also showed higher heterogeneity according to some metrics, as well as a wider range of heterogeneity values than OSEM images for most of the metrics considered, especially when analysing larger tumours. This suggests that, whenever available, unfiltered PSF images should also be exploited to obtain the most discriminative quantitative heterogeneity features.
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.