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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.
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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
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Imaging Studies III: Computed Tomography01:27

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
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Updated: Nov 13, 2025

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis
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Evaluation of an Automatic Classification Algorithm Using Convolutional Neural Networks in Oncological Positron

Pierre Pinochet1, Florian Eude1, Stéphanie Becker1,2

  • 1Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France.

Frontiers in Medicine
|March 15, 2021
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Summary

A new AI tool, the PET Assisted Reporting System (PARS), shows promise in detecting cancer sites on PET/CT scans. While it can predict patient survival, manual review of its automated segmentations is still necessary for accuracy.

Keywords:
artificial intelligence-AIconvolutional neural networkdiffuse large B cell lymphoma (DLBCL)fluorodeoxyglucose (18F-FDG)positron emission tomography

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Positron emission tomography (PET)/computed tomography (CT) is crucial for cancer staging and monitoring.
  • Accurate segmentation of tumor volumes is essential for treatment response assessment and prognosis.
  • Automated tools are being developed to improve the efficiency and consistency of PET/CT analysis.

Purpose of the Study:

  • To evaluate the performance of the PET Assisted Reporting System (PARS), an AI-based prototype using convolutional neural networks (CNNs).
  • To assess PARS's ability to detect cancer sites and segment total metabolic tumor volumes (TMTVs) in 18F-FDG PET/CT scans.
  • To compare the prognostic value of automated versus manual TMTV segmentation for patient survival.

Main Methods:

  • Retrospective analysis of two patient cohorts: 119 patients with diffuse large B-cell lymphoma (DLBCL) in a research setting and 430 patients with various cancers in a clinical routine setting.
  • Assessed correlation and overlap (Dice score) between manual and automated tumor segmentations.
  • Compared the predictive value of manual and automated TMTVs for progression-free survival (PFS) and overall survival (OS) in the research cohort.

Main Results:

  • In the research cohort, the median Dice score was 0.65 and intraclass correlation coefficient was 0.68, indicating moderate agreement between automated and manual segmentations.
  • Both automated and manual TMTVs were predictive of PFS and OS in DLBCL patients.
  • In the clinical routine cohort, the median Dice score was 0.48 and intraclass correlation coefficient was 0.61.

Conclusions:

  • Automated TMTV segmentation using PARS shows prognostic value for DLBCL patients.
  • The AI-generated segmentations and TMTVs require verification and potential correction to match manual segmentations for clinical application.
  • Further refinement of the AI algorithm is needed for reliable use in routine clinical practice.