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Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
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Related Experiment Video

Updated: Dec 8, 2025

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET
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Adding the temporal domain to PET radiomic features.

Wyanne A Noortman1,2, Dennis Vriens1, Cornelis H Slump3

  • 1Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.

Plos One
|September 23, 2020
PubMed
Summary
This summary is machine-generated.

Dynamic radiomic features from [18F]FDG PET/CT scans offer new insights into non-small cell lung carcinoma biology. Some dynamic texture features provide additional information beyond static imaging, unlike parametric features.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Radiomics

Background:

  • Radiomic features characterize tumor biology from PET scans.
  • Current radiomics use static images, but temporal changes may offer more insights.
  • Dynamic radiomic features explore novel temporal uptake patterns.

Purpose of the Study:

  • To investigate additional information from dynamic radiomic features compared to static or metabolic rate images.
  • To assess the value of temporal radiomic features in non-small cell lung carcinoma.

Main Methods:

  • Dynamic [18F]FDG PET/CT scans from 35 non-small cell lung carcinoma patients.
  • Radiomic features extracted from static PET and parametric metabolic rate PET.
  • Dynamic grey level cooccurrence matrix (GLCM) and grey level run length matrix (GLRLM) features calculated across time frames.
  • Correlation analysis between dynamic, parametric, and static features; survival analysis performed.

Main Results:

  • Parametric features showed high correlations with static features, indicating minimal additional information.
  • Five dynamic GLCM features exhibited negligible to moderate correlations with static features, suggesting novel information.
  • Dynamic GLRLM features were highly correlated with static features, implying redundancy.
  • Kaplan-Meier survival analyses for dichotomized features were insignificant.

Conclusions:

  • Dynamic GLCM radiomic features may offer distinct information compared to static features.
  • Parametric features provide limited additional value over static radiomics.
  • Larger studies are needed to confirm the clinical benefit of temporal radiomics.