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Related Concept Videos

Positron Emission Tomography01:29

Positron Emission Tomography

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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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Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A

Yuekai Li1, Fengcai Li2, Shaoli Han3

  • 1Department of Nuclear Medicine, Qilu Hospital of Shandong University, No. 107, Cultural West Road, Jinan, 250012 China.

Phenomics (Cham, Switzerland)
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Summary

Machine learning models using 18F-DCFPyL PET/CT radiomics can predict prostate cancer malignancy and risk without biopsy. This noninvasive approach aids personalized treatment decisions for prostate cancer patients.

Keywords:
18F-DCFPyL positron emission tomography/computerized tomographyHigh-risk tumorProstate cancerRadiomicsThree layer-machine learning

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Area of Science:

  • Nuclear Medicine
  • Radiomics
  • Machine Learning in Oncology

Background:

  • Accurate prostate cancer grading and staging are crucial for treatment decisions.
  • Traditional methods like biopsy can be invasive and may not capture tumor heterogeneity.
  • 18F-DCFPyL PET/CT offers detailed imaging for prostate cancer assessment.

Purpose of the Study:

  • To evaluate machine learning models based on 18F-DCFPyL PET/CT radiomics.
  • To predict malignancy, high pathological grade (Gleason score > 7), and clinical risk (D'Amico classification).
  • To assess the potential of noninvasive virtual biopsy for prostate cancer management.

Main Methods:

  • Inclusion of 138 treatment-naïve prostate cancer patients with positive 18F-DCFPyL scans.
  • Delineation of primary lesions and extraction of radiomic features using five binning approaches.
  • Application of three-layer machine learning models to identify predictive features and patient characteristics.

Main Results:

  • Developed predictive models for malignancy (Mm), high Gleason score (Mgs), and high D'Amico risk (Mamico).
  • Validated models using Monte Carlo cross-validation.
  • Achieved areas under the curve (AUC) of 0.97 for Mm, 0.73 for Mgs, and 0.82 for Mamico.

Conclusions:

  • 18F-DCFPyL PET/CT radiomics models show significant potential in distinguishing benign from malignant prostate tumors.
  • These models can effectively identify high-risk prostate tumors without the need for biopsy.
  • 18F-DCFPyL PET/CT serves as a noninvasive tool for virtual biopsy, supporting personalized prostate cancer treatment.