¹⁸F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

Area of Science:

• Radiology and Nuclear Medicine
• Oncology
• Diagnostic Imaging

Background:

• Pulmonary nodules (PNs) require accurate differentiation between benign and malignant types.
• Existing positron emission tomography (PET)/computed tomography (CT)-based models aim to improve this diagnostic process.
• This meta-analysis evaluates the diagnostic utility of conventional PET/CT parameter models for PNs.

Approach:

• A systematic literature search was conducted across major databases (Web of Science, PubMed, Wanfang) up to September 2023.
• Data from 13 retrospective studies involving 1,731 malignant and 693 benign PNs were pooled for analysis.
• Stata v 12.0 was utilized for comprehensive pooled statistical analyses.

Key Points:

• PET/CT models demonstrated pooled sensitivity of 88% and specificity of 78% (AUC=0.91) for PN differentiation.
• PET-alone analysis yielded pooled sensitivity of 92% but lower specificity of 51% (AUC=0.82).
• Significant heterogeneity was observed across pooled endpoints for both PET/CT and PET-alone analyses.

Conclusions:

• PET/CT-based models exhibit superior diagnostic performance over PET imaging alone for distinguishing benign from malignant pulmonary nodules.
• The findings support the clinical utility of integrated PET/CT models in the differential diagnosis of PNs.