ArticleStatistics in medicine2017
Statistical assessment of treatment response in a cancer patient based on pre-therapy and post-therapy FDG-PET scans.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma.Scientific reports · 2024Article
- Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application.Frontiers in oncology · 2020Article
- Efficient Bandwidth Estimation in 2D Filtered Backprojection Reconstruction.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2019Article
- Sample size calculations based on a difference in medians for positively skewed outcomes in health care studies.BMC medical research methodology · 2017Article
- Allosteric inhibitor remotely modulates the conformation of the orthestric pockets in mutant IDH2/R140Q.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This work arises from consideration of sarcoma patients in which fluorodeoxyglucose positron emission tomography (FDG-PET) imaging pre-therapy and post-chemotherapy is used to assess treatment response. Our focus is on methods for evaluation of the statistical uncertainty in the measured response for an individual patient. The gamma distribution is often used to describe data with constant coefficient of variation, but it can be adapted to describe the pseudo-Poisson character of PET measurements. We propose co-registering the pre-therapy and post- therapy images and modeling the approximately paired voxel-level data using the gamma statistics. Expressions for the estimation of the treatment effect and its variability are provided. Simulation studies explore the performance in the context of testing for a treatment effect. The impact of misregistration errors and how test power is affected by estimation of variability using simplified sampling assumptions, as might be produced by direct bootstrapping, is also clarified. The results illustrate a marked benefit in using a properly constructed paired approach. Remarkably, the power of the paired analysis is maintained even if the pre-image and post- image data are poorly registered. A theoretical explanation for this is indicated. The methodology is further illustrated in the context of a series of fluorodeoxyglucose-PET sarcoma patient studies. These data demonstrate the additional prognostic value of the proposed treatment effect test statistic. Copyright © 2016 John Wiley & Sons, Ltd.
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Registered trials
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