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Artificial Intelligence-Based 18F-FDG PET/CT Radiomics for Mediastinal Lymph Node Staging in Non-Small Cell Lung

Alessia-Stephania Rosian1,2, Agneta-Maria Pusztai2, Amalia Constantinescu1,3

  • 1Doctoral School, "Victor Babes" University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square 2, 300041 Timisoara, Romania.

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Summary

Artificial intelligence (AI) using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET/CT) radiomics shows promise for staging non-small cell lung cancer (NSCLC) mediastinal lymph nodes. While performance dips in external validation, AI models integrated with clinical data offer significant diagnostic improvement.

Keywords:
18F-FDG PET/CTartificial intelligencedeep learninglymph node metastasismachine learningmediastinal lymph node stagingnon-small cell lung cancerradiomics

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate mediastinal lymph node staging is critical for non-small cell lung cancer (NSCLC) treatment and prognosis.
  • Artificial intelligence (AI)-based radiomics using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) are emerging tools for this purpose.

Purpose of the Study:

  • To systematically review the diagnostic performance, validation strategies, and clinical significance of AI-based 18F-FDG PET/CT radiomics for mediastinal nodal staging in NSCLC.
  • To assess the effectiveness of these AI models compared to conventional methods.

Main Methods:

  • Systematic literature search conducted in PubMed, ScienceDirect, and Scopus following PRISMA 2020 guidelines.
  • Included studies utilized radiomic or AI approaches for mediastinal lymph node (LN) evaluation in NSCLC, with histopathology as the reference standard.
  • Methodological quality was assessed using the QUADAS-2 tool.

Main Results:

  • Thirteen retrospective studies were included, with varying cohort sizes.
  • AI models generally showed lower performance on external/prospective validation compared to training/internal datasets, but maintained clinically significant discriminative ability.
  • Composite models (AI radiomics + clinical factors + conventional PET metrics) outperformed radiomics-only models; some AI approaches improved specificity and reduced false positives compared to conventional PET/CT.

Conclusions:

  • AI-based 18F-FDG PET/CT radiomics demonstrate promising discriminative capacity for NSCLC mediastinal nodal staging, particularly when integrated with clinical and conventional imaging data.
  • While clinically significant in independent cohorts, performance attenuation is noted compared to training data.
  • Methodological heterogeneity, retrospective designs, and lack of prospective multicenter validation currently limit routine clinical adoption.