Abstract
BackgroundTo determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs).Materials and methodsCT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis.ResultsCT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906).ConclusionThe prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.
Highlights
To determine the predictive Computed tomography (CT) imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs)
Necrosis was observed in 3 patients (8.1%) with PMECs, 30 patients (39.5%) with Squamous cell carcinoma (SCC), and 18 patients (23.7%) with adenocarcinomas
Tumor calcification was found in 6 patients with PMECs, 16 patients with SCCs, and 10 patients with adenocarcinomas
Summary
To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Primary pulmonary mucoepidermoid carcinoma (PMEC) is a rare malignant neoplasm of the lung and accounts for approximately 0.1–0.2% of all lung malignancies [1,2,3,4,5], arising from the minor salivary glands of the (2021) 21:2 known about the most important imaging features for differential diagnosis of PMECs from pulmonary SCCs and adenocarcinomas. We retrospectively reviewed the CT findings of PMECs, SCCs and adenocarcinomas in order to identify the independent predictive radiological features for the diagnosis of PMECs
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