ArticleEuropean journal of nuclear medicine and molecular imaging2013
Robustness of intratumour ¹⁸F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma.
Article in European journal of nuclear medicine and molecular imaging, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 5 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
117 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.European journal of nuclear medicine and molecular imaging · 2023Guideline
- Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.European journal of nuclear medicine and molecular imaging · 2022Pooled it
- Repeatability and Reproducibility of Radiomic Features: A Systematic Review.International journal of radiation oncology, biology, physics · 2018Pooled it
- False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review.PloS one · 2015Pooled it
- Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review.PloS one · 2014Pooled it
- Correlation between tumour characteristics, SUV measurements, metabolic tumour volume, TLG and textural features assessed withEuropean journal of nuclear medicine and molecular imaging · 2017Trial
- Robustness of Radiomic Features in [Molecular imaging and biology · 2016Trial
- Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [(18)F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation.Molecular imaging and biology · 2016Trial
- Radiomics in laryngeal squamous cell carcinoma: state of the art.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- Critical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook.European journal of nuclear medicine and molecular imaging · 2026Review
- Prognostic Value of Radiomic Analysis Using Pre- and Post-TreatmentJournal of personalized medicine · 2024Article
- Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan.PloS one · 2024Article
- Article
- Development and External Validation ofCancers · 2023Article
- Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts.European journal of nuclear medicine and molecular imaging · 2023Article
- "Fuzzy" radiomics: the way forward for nuclear medicine imaging applications?European journal of nuclear medicine and molecular imaging · 2023Article
- Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer.Scientific reports · 2023Article
- Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to NasopharyngealCancers · 2023Article
- Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [European journal of nuclear medicine and molecular imaging · 2022Article
- Principal component analysis of texture features derived from FDG PET images of melanoma lesions.EJNMMI physics · 2022Article
57 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIntratumour uptake heterogeneity in PET quantified in terms of textural features for response to therapy has been investigated in several studies, including assessment of their robustness for reconstruction and physiological reproducibility. However, there has been no thorough assessment of the potential impact of preprocessing steps on the resulting quantification and its predictive value. The goal of this work was to assess the robustness of PET heterogeneity in textural features for delineation of functional volumes and partial volume correction (PVC).
methodsThis retrospective analysis included 50 patients with oesophageal cancer. PVC of each PET image was performed. Tumour volumes were determined using fixed and adaptive thresholding, and the fuzzy locally adaptive Bayesian algorithm, and heterogeneity was quantified using local and regional textural features. Differences in the absolute values of the image-derived parameters considered were assessed using Bland-Altman analysis. The impact on their predictive value for the identification of patient nonresponders was assessed by comparing areas under the receiver operating characteristic curves.
resultsHeterogeneity parameters were more dependent on delineation than on PVC. The parameters most sensitive to delineation and PVC were regional ones (intensity variability and size zone variability), whereas local parameters such as entropy and homogeneity were the most robust. Despite the large differences in absolute values obtained from different delineation methods or after PVC, these differences did not necessarily translate into a significant impact on their predictive value.
conclusionParameters such as entropy, homogeneity, dissimilarity (for local heterogeneity characterization) and zone percentage (for regional characterization) should be preferred. This selection is based on a demonstrated high differentiation power in terms of predicting response, as well as a significant robustness with respect to the delineation method used and the partial volume effects.
Indexed as
Identifiers
23857457What 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.