ArticleTranslational oncology2014
A Virtual Clinical Trial of FDG-PET Imaging of Breast Cancer: Effect of Variability on Response Assessment.
Article in Translational oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy.Physics in medicine and biology · 2017Trial
- An integrated PET/CT simulation framework for virtual imaging trials and quantitative performance evaluation.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2026Article
- A Virtual Clinical Trial to Detect Changes in Tumor Uptake with PET using Lesion Embedding.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Virtual clinical trials in medical imaging: a review.Journal of medical imaging (Bellingham, Wash.) · 2020Review
- Virtual clinical trials identify effective combination therapies in ovarian cancer.Scientific reports · 2019Article
- Sensitivity of Image Features to Noise in Conventional and Respiratory-Gated PET/CT Images of Lung Cancer: Uncorrelated Noise Effects.Technology in cancer research & treatment · 2017Article
- Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography.Journal of medical imaging (Bellingham, Wash.) · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
introductionThere is growing interest in using positron emission tomography (PET) standardized uptake values (SUVs) to assess tumor response to therapy. However, many error sources compromise the ability to detect SUV changes. We explore relationships between these errors and overall SUV variability.
methodsWe used simulations in a virtual clinical trial framework to study impacts of error sources from scanning and analysis effects on assessment of SUV changes. We varied tumor diameter, scan duration, pretherapy SUV, magnitude of change in SUV, image reconstruction filter, and SUV metric. Poisson noise was added to the raw data before image reconstruction. Variance from global sources of error, e.g., scanner calibration, was incorporated. Two thousand independent noisy sinograms per scenario were generated and reconstructed. We used SUVs to create receiver operating characteristic (ROC) curves to quantify ability to assess response. Integrating area under the ROC curve summarized ability to detect SUV changes.
resultsScan duration and image reconstruction method had relatively little impact on ability to measure response. SUVMAX is nearly as effective as SUVMEAN, especially with increased image smoothing and despite size-matched region of interest placement. For an effective variability of 15%, we found the Positron Emission Tomography Response Criteria in Solid Tumors criteria for measuring response (±30%) similar to the European Organization for Research and Treatment of Cancer criteria (±25%).
conclusionsFor typical PET variance levels, tumor response must be 30% to 40% to be reliably determined using SUVs. PET scan duration and image reconstruction method had relatively little effect.
Identifiers
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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.