Distinguishing benign from malignant mesothelial cells in effusions by Glut-1, EMA, and Desmin expression: an

Michael Kuperman1, Roxanne R Florence, Liron Pantanowitz

  • 1Department of Pathology, Baystate Medical Center, Tufts University School of Medicine, Springfield, Massachusetts 01199, USA.

Diagnostic Cytopathology
|September 25, 2012
PubMed

Insights

Immunocytochemistry (IC) using Glut-1 and EMA reliably distinguishes malignant mesothelioma (MM) from reactive mesothelial hyperplasia (RM) in effusions. A risk score (RS) model derived from these markers shows high accuracy in external validation, aiding differential diagnosis.

Area of Science:

  • Cytopathology
  • Oncology
  • Immunohistochemistry

Background:

  • Differentiating malignant mesothelioma (MM) from reactive mesothelial hyperplasia (RM) in effusion cytology is diagnostically challenging.
  • Immunocytochemistry (IC) on cell blocks (CB) offers a potential tool for improved diagnostic accuracy.

Purpose of the Study:

  • To evaluate the efficacy of IC markers (Glut-1, EMA, Desmin) in distinguishing MM from RM in effusion CBs.
  • To develop and externally validate a risk score (RS) model for differential diagnosis.

Main Methods:

  • IC analysis of Glut-1, EMA, and Desmin expression in 43 MM and 36 RM effusion CBs.
  • Receiver Operating Characteristic (ROC) curve analysis to assess individual marker performance (AUC).
  • Logistic regression (LR) analysis to combine Glut-1 and EMA, developing a risk score (RS) and performing external validation.

Main Results:

  • Individual markers showed good diagnostic performance: Glut-1 (AUC=0.90), EMA (AUC=0.82), Desmin (AUC=0.84).
  • A combined RS model using Glut-1 and EMA achieved a higher AUC of 0.93.
  • The externally validated RS model demonstrated strong performance with an AUC of 0.91.

Conclusions:

  • A risk score (RS) derived from logistic regression of Glut-1 and EMA immunocytochemistry significantly enhances the distinction between MM and RM in effusions.
  • The developed RS model is robust, showing high accuracy in external validation, supporting its clinical utility in differential diagnosis.

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