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Classifying multiple lung cancers using morphological features: a meta-analysis.

Mona Mlika1, Majdi Zorgati2, Faouzi El Mezni3

  • 1Pathology department, Abderrahman Mami Hospital , Tunis, Tunisia.

Journal of Immunoassay & Immunochemistry
|June 25, 2020
PubMed
Summary
This summary is machine-generated.

Distinguishing multiple lung cancers as metastases or primaries is challenging. Morphologic features show diagnostic value, especially in surgical specimens, despite molecular testing

Keywords:
Multiple lung cancersmolecular testsmorphologic criterianext-generation sequencing

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Area of Science:

  • Pulmonary Medicine
  • Pathology
  • Oncology

Background:

  • Differentiating intrapulmonary metastases from multiple primary lung cancers is critical for patient management and prognosis.
  • Microscopic subtype similarity can make this distinction difficult, even with the 2015 WHO classification.
  • Accurate pathological diagnosis is essential for guiding treatment strategies.

Purpose of the Study:

  • To evaluate the diagnostic utility of morphologic features versus molecular testing in distinguishing intrapulmonary metastases from multiple primary lung cancers.
  • To conduct a meta-analysis of published studies comparing diagnostic methods.
  • To assess the reliability of morphologic assessment in routine pathology.

Main Methods:

  • A systematic literature search was performed on PubMed, Embase, and Cochrane Library (1999-2020).
  • Meta-analysis was conducted using Meta-Disc software 5.1.32, calculating sensitivity, specificity, and diagnostic odds ratio.
  • Heterogeneity was assessed using Q test and I-squared statistics, with meta-regression to explore sources of variation.

Main Results:

  • Twelve studies with 309 patients were included in the meta-analysis.
  • Pooled sensitivity (pSEN) was 65% (I²=53%), pooled specificity (pSPE) was 49% (I²=56%).
  • Diagnostic odds ratio (dOR) was 2.13 [1.07-4.25] with low heterogeneity (I²=26.5%). Area Under the Curve (AUC) was 0.63.

Conclusions:

  • Morphologic features demonstrate diagnostic value in differentiating multiple lung cancers, particularly in surgical specimens.
  • Molecular testing did not significantly improve diagnostic accuracy in this meta-analysis.
  • Further research may explore other covariates contributing to diagnostic heterogeneity.