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Microcomputed tomography as a diagnostic tool for detection of lymph node metastasis in non-small cell lung cancer: A

Ayten Kayı Cangır1,2, Süleyman Gökalp Güneş1, Kaan Orhan3

  • 1Department of Thoracic Surgery, Ankara University Faculty of Medicine, Ankara, Turkey.

Journal of Pathology Informatics
|April 18, 2024
PubMed
Summary
This summary is machine-generated.

Three-dimensional micro-CT imaging can identify hidden lymph node metastases in non-small cell lung cancer (NSCLC) patients. This advanced technique improves the accuracy of staging and prognosis for NSCLC by analyzing formalin-fixed paraffin-embedded tissues.

Keywords:
Lung cancerLymph node metastasisMicro-CT

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Area of Science:

  • Oncology
  • Radiology
  • Pathology

Background:

  • Current 2D pathological examination of lymph nodes (LNs) in non-small cell lung cancer (NSCLC) may miss metastatic foci.
  • This can lead to survival rate discrepancies in pN0 patients with similar T stages.
  • Three-dimensional (3D) evaluation using micro-computed tomography (micro-CT) offers a comprehensive assessment of all LNs.

Purpose of the Study:

  • To evaluate the metastatic status of formalin-fixed paraffin-embedded (FFPE) LNs using quantitative micro-CT parameters.
  • To determine if micro-CT can differentiate between tumoral and non-tumoral regions within LNs.

Main Methods:

  • Micro-CT scans were performed on FFPE LNs from 8 NSCLC patients.
  • 47 regions of interest (ROIs) including metastatic foci, normal lymphoid tissue, adipose tissue, and anthracofibrosis were analyzed.
  • Quantitative structural variables from tumoral and non-tumoral ROIs were compared.

Main Results:

  • Significant differences in linear density, connectivity, connectivity density, and closed porosity were found between tumoral and non-tumoral ROIs.
  • Receiver operating characteristic analysis confirmed the ability to differentiate ROIs based on thickness, linear density, connectivity, connectivity density, and closed porosity.

Conclusions:

  • Quantitative micro-CT parameters can effectively distinguish tumoral from non-tumoral regions in FFPE LNs.
  • These parameters show potential for developing AI algorithms for 3D identification of LN metastases in FFPE tissues.