ArticleClinical imaging
Inter-operator variability in compartmental kinetic analysis of
Article in Clinical imaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Deep learning for automatic organ and tumor segmentation in nanomedicine pharmacokinetics.Theranostics · 2024Article
- Review
- Longitudinal PET imaging of tumor hypoxia during the course of radiotherapy.European journal of nuclear medicine and molecular imaging · 2018Review
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.
Funding
Abstract
purposeTo assess the inter-operator variability in compartment analysis (CA) of dynamic-FMISO (dyn-FMISO) PET.
methodsStudy-I: Five investigators conducted CA for 23 NSCLC dyn-FMISO tumor time-activity-curves. Study-II: Four operators performed CA for four NSCLC dyn-FMISO datasets. Repeatability of Kinetic-Rate-Constants (KRCs) was assessed.
resultsStudy-I: Strong correlation (ICC > 0.9) and interchangeable results among operators existed for all KRCs. Study-II: Up to 103% variability in tumor segmentation, and weaker ICC in KRCs (ICC-V
conclusionsInter-operator variability in CA of dyn-FMISO was shown to be within statistical errors.
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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.