Diagnostic Accuracy of Contrast-enhanced Computed Tomography and Positron Emission Tomography with 18-FDG in Identifying Malignant Solitary Pulmonary Nodules

Overview

Journal Medicine (Baltimore)

Specialty General Medicine

Date 2015 Apr 18

PMID 25881842

Citations 11

Authors

M Dabrowska

R Krenke

P Korczynski

M Maskey-Warzechowska

M Zukowska

J Kunikowska

T Orlowski

R Chazan

Affiliations

Soon will be listed here.

Abstract

Contrast-enhanced computed tomography (CECT) and positron emission tomography with 18-FDG (FDG-PET/CT) are used to identify malignant solitary pulmonary nodules. The aim of the study was to evaluate the accuracy of CECT and FDG-PET/CT in diagnosing the etiology of solitary pulmonary nodule (SPN). Eighty patients with newly diagnosed SPN >8 mm were enrolled. The patients were scheduled for either or both, CECT and FDG-PET/CT. The nature of SPN (malignant or benign) was determined either by its pathological examination or radiological criteria. In 71 patients, the etiology of SPN was established and these patients were included in the final analysis. The median SPN diameter in these patients was 13 mm (range 8-30 mm). Twenty-two nodules (31%) were malignant, whereas 49 nodules were benign. FDG-PET/CT was performed in 40 patients, and CECT in 39 subjects. Diagnostic accuracy of CECT was 0.58 (95% confidence interval [CI] 0.41-0.74). The optimal cutoff level discriminating between malignant and benign SPN was an enhancement value of 19 Hounsfield units, for which the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of CECT were 100%, 37%, 32%, and 100%, respectively. Diagnostic accuracy of FDG-PET/CT reached 0.9 (95% CI 0.76-0.9). The optimal cutoff level for FDG-PET/CT was maximal standardized uptake value (SUV max) 2.1. At this point, the sensitivity, specificity, PPV, and NPV were 77%, 92%, 83%, and 89%, respectively. The diagnostic accuracy of FDG-PET/CT is higher than that of CECT. The advantage of CECT is its high sensitivity and negative predictive value.

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