Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Positron Emission Tomography01:29

Positron Emission Tomography

3.9K
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...
3.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Overall Survival with [<sup>177</sup>Lu]Lu-PSMA-617 Versus [<sup>177</sup>Lu]Lu-PSMA I&T: A Propensity Score-Matched Real-World Analysis.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same author

Direct Androgen Receptor Antagonism Enhances Therapeutic PSMA Radioligand Uptake in Prostate Cancer Models.

Molecular imaging and biology·2026
Same author

CXCR4-targeted PET/CT in early systemic sclerosis-associated interstitial lung disease: a prospective proof-of-concept study of in vivo inflammatory activity.

European journal of nuclear medicine and molecular imaging·2026
Same author

Factors Influencing Double-Strand Break Focus Biodosimetry for Detection of Internal Low-LET Irradiation in Human Blood Samples.

Radiation research·2026
Same author

Impact of Neuroendocrine Neoplasm-Specific Systemic Treatments on Somatostatin Receptors Expression and Function in Neuroendocrine Tumor Cells.

Cancers·2026
Same author

Diagnostic Performance of Somatostatin Receptor-directed PET/CT for Tumor-induced Osteomalacia.

Molecular imaging and biology·2026

Related Experiment Video

Updated: May 17, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

Detection of Local Prostate Cancer Recurrence from PET/CT Scans Using Deep Learning.

Marko Korb1, Hülya Efetürk2, Tim Jedamzik3

  • 1Center for Computational and Theoretical Biology, Julius-Maximilians-University Würzburg, 97070 Würzburg, Germany.

Cancers
|May 14, 2025
PubMed
Summary

An artificial intelligence model using [18F]-PSMA-1007 PET scans showed limited accuracy in detecting prostate cancer recurrence. The study found 1404 scans insufficient for reliable results, but shared code to aid future research.

Keywords:
DenseNetPCPET/CT[18F]-PSMAartificial learningdeep learningpositron emission tomographyprostate cancer

More Related Videos

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
12:24

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

12.3K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.3K

Related Experiment Videos

Last Updated: May 17, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
12:24

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

12.3K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate cancer (PC) is a significant cause of cancer-related mortality in men globally.
  • Positron emission tomography (PET) with prostate-specific membrane antigen (PSMA) tracers aids in detecting PC recurrence and metastasis.
  • Accurate detection is crucial for effective diagnosis and treatment planning.

Purpose of the Study:

  • To evaluate an artificial intelligence (AI) model for detecting local prostate cancer recurrence using [18F]-PSMA-1007 PET/CT data.
  • To assess the impact of region-of-interest selection and patient status (post-prostatectomy vs. non-operated) on AI model performance.
  • To determine if the dataset size was adequate for developing a reliable AI model.

Main Methods:

  • Retrospective analysis of 1404 [18F]-PSMA-1007 PET/CT scans from patients with confirmed prostate cancer.
  • Training artificial neural networks to identify local recurrence based on PET imaging features.
  • Comparing models with varying input data: full scan (Model A), bladder-centric region (Model B), and prostatectomy status-specific models (Model C and D).

Main Results:

  • Model A achieved 56% accuracy, while Model B (bladder-centric) improved to 71% validation accuracy.
  • Specialized models for post-prostatectomy (Model C) and non-operated (Model D) patients reached 77% accuracy each.
  • All models exhibited near 100% accuracy on training data, indicating significant overfitting. Low F1-scores and AUC values suggest unreliability.

Conclusions:

  • The dataset of 1404 [18F]-PSMA-1007 PET/CT scans was insufficient to achieve over 90% accuracy for detecting local prostate cancer recurrence.
  • Current AI models developed in this study do not produce reliable results, as indicated by low F1-scores and AUC values.
  • The study's source code and pre-trained models are openly shared to facilitate future research and the development of improved AI models for prostate cancer detection.