ArticleEuropean journal of nuclear medicine and molecular imaging2017
Textural features of
Article in European journal of nuclear medicine and molecular imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Evidence-based guideline of the European Association of Nuclear Medicine (EANM) on imaging infection in vascular grafts.European journal of nuclear medicine and molecular imaging · 2022Guideline
- Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign.BMC cancer · 2023Trial
- FDG-PET/CT in infected and non-infected prosthetic vascular grafts.The international journal of cardiovascular imaging · 2025Article
- Radiomics with Clinical Data and [Diagnostics (Basel, Switzerland) · 2025Article
- Empowering PET: harnessing deep learning for improved clinical insight.European radiology experimental · 2024Review
- Diagnostic biomarker panels of osteoarthritis: UPLC-QToF/MS-based serum metabolic profiling.PeerJ · 2023Article
- Fluorine-18 fluorodeoxyglucose positron emission tomography-based textural features for prediction of event prone carotid atherosclerotic plaques.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- Diagnostic performance of quantitative and qualitative parameters for the diagnosis of aortic graft infection using [Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- Diagnostic performance and image interpretation ofJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- State of the art ofClinical and translational imaging · 2021Review
- Inflammatory lesions and brain tumors: is it possible to differentiate them based on texture features in magnetic resonance imaging?The journal of venomous animals and toxins including tropical diseases · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe clinical problem in suspected aortoiliac graft infection (AGI) is to obtain proof of infection. Although
methodsThirty patients with a history of aortic graft reconstruction who underwent
resultsThe feature selection process yielded the textural features named variance (AUC = 0.88), high grey level zone emphasis (AUC = 0.87), small zone low grey level emphasis (AUC = 0.80), and small zone high grey level emphasis (AUC = 0.81) most optimal for distinguishing between groups I and II. SUVmax, TBR, and VGS were also able to distinguish between these groups with AUCs of 0.87, 0.78, and 0.90, respectively. The textural feature named short run high grey level emphasis was able to distinguish group Ia from Ib (AUC = 0.83), while for the same task the TBR and VGS were not found to be predictive. SUVmax was found predictive in distinguishing these groups, but showed an unsatisfactory accuracy (AUC = 0.75).
conclusionTextural analysis to characterize
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.