ReviewThe British journal of radiology2018
Towards enhanced PET quantification in clinical oncology.
Review in The British journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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.
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Who cites it
69 citing papers in PubMed, 132 citations in OpenAlex.
- Enhanced Lymphoma Subtype Classification and Prognosis Using Machine Learning withNuclear medicine and molecular imaging · 2026Article
- Large axial field of view PET/CT - Not just a longer PET gantry.Zeitschrift fur medizinische Physik · 2026Review
- Kinetic modelling of [⁶⁸Ga]Ga-FAPI-46 PET in pancreaticobiliary lesions: distinguishing cancer from pancreatitis.European journal of nuclear medicine and molecular imaging · 2026Article
- Whole-Body Dynamic Positron Emission and Computed Tomography (WBD-PET/CT): Latest Developments, Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Differences in the quantitative parameter of lung lesions in dynamic chest FDG PET/CT imaging: framing protocol, reconstruction algorithm, software processing.Frontiers in oncology · 2026Article
- Automated segmentation of the primary tumor in nasopharyngeal carcinoma using a deep learning framework in positron emission tomography imaging: a comparative study.Quantitative imaging in medicine and surgery · 2025Article
- Long Axial Field of View PET: A New Era of Quantitative PET Imaging for Prospective Applications Beyond Classical Theranostics.PET clinics · 2025Review
- Long Axial Field-of-view PET: A New Era of Quantitative PET Imaging for Theranostic Applications in Clinic.PET clinics · 2025Review
- Optimizing Attenuation Correction inDiagnostics (Basel, Switzerland) · 2025Article
- Realistic total-body J-PET geometry optimization: Monte Carlo study.Medical physics · 2025Article
- Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging.Clinical nuclear medicine · 2025Article
- Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Distinguishing benign from malignant lesions with high [European journal of nuclear medicine and molecular imaging · 2025Article
- Self-supervised neural network for Patlak-based parametric imaging in dynamic [European journal of nuclear medicine and molecular imaging · 2025Article
- Validating image-derived input functions of dynamicFrontiers in nuclear medicine · 2025Article
- EARL compliance on the Biograph Vision Quadra PET-CT: phantom study for static and continuous bed motion acquisitions.Frontiers in nuclear medicine · 2025Article
- Deep-learning-based Attenuation Correction forMolecular imaging and radionuclide therapy · 2024Article
- Application of Dynamic [Molecular imaging and biology · 2024Article
- Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.EJNMMI physics · 2024Article
9 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
2 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Positron emission tomography (PET) has, since its inception, established itself as the imaging modality of choice for the in vivo quantitative assessment of molecular targets in a wide range of biochemical processes underlying tumour physiology. PET image quantification enables to ascertain a direct link between the time-varying activity concentration in organs/tissues and the fundamental parameters portraying the biological processes at the cellular level being assessed. However, the quantitative potential of PET may be affected by a number of factors related to physical effects, hardware and software system specifications, tracer kinetics, motion, scan protocol design and limitations in current image-derived PET metrics. Given the relatively large number of PET metrics reported in the literature, the selection of the best metric for fulfilling a specific task in a particular application is still a matter of debate. Quantitative PET has advanced elegantly during the last two decades and is now reaching the maturity required for clinical exploitation, particularly in oncology where it has the capability to open many avenues for clinical diagnosis, assessment of response to treatment and therapy planning. Therefore, the preservation and further enhancement of the quantitative features of PET imaging is crucial to ensure that the full clinical value of PET imaging modality is utilized in clinical oncology. Recent advancements in PET technology and methodology have paved the way for faster PET acquisitions of enhanced sensitivity to support the clinical translation of highly quantitative four-dimensional (4D) parametric imaging methods in clinical oncology. In this report, we provide an overview of recent advances and future trends in quantitative PET imaging in the context of clinical oncology. The pros/cons of the various image-derived PET metrics will be discussed and the promise of novel methodologies will be highlighted.
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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.