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Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

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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.
Pulmonary Angiogram
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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.
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Related Experiment Video

Updated: Mar 31, 2026

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
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Artificial Intelligence Assisted 18F-FDG PET Radiomics in Classifying Histological Subtypes of Lung Cancer:

Pooja Dwivedi1,2,3, Sagar Barage2, Ashish Jha3,4

  • 1Department of Nuclear Medicine and Molecular Imaging, Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Navi Mumbai, India.

Nuclear Medicine and Molecular Imaging
|March 30, 2026
PubMed
Summary

AI-assisted radiomics using PET scans shows promise for classifying lung cancer subtypes. Machine learning models achieved good diagnostic accuracy, but external validation is needed for clinical use.

Keywords:
18F-FDG PETLung cancerMachine learningRadiomics

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • AI-assisted radiomics is a developing field in precision oncology.
  • Previous studies show promising results, but clinical applicability remains uncertain.

Purpose of the Study:

  • To systematically review and meta-analyze the diagnostic accuracy of PET-based radiomics in machine learning (ML) models for lung cancer histological subtype classification.

Main Methods:

  • Systematic review and meta-analysis of 14 studies, with 8 eligible for meta-analysis.
  • QUADAS-2 and RQS used for quality and bias assessment.
  • Random-effects model applied to estimate pooled sensitivity, specificity, and diagnostic odds ratio (DOR).

Main Results:

  • Pooled sensitivity: 0.81, specificity: 0.79, DOR: 22.42.
  • Area Under the Curve (AUC) of the SROC curve was 0.89, indicating good diagnostic performance.
  • Significant heterogeneity was observed (p < 0.001).

Conclusions:

  • ML models using 18F-FDG PET radiomics demonstrate potential for predicting non-small cell lung cancer histological subtypes.
  • External validation studies are recommended to confirm generalizability and clinical utility.