ArticleCancer imaging : the official publication of the International Cancer Imaging Society2021
Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- A surgical strategy-based nomogram for primary pulmonary mucoepidermoid carcinoma.Discover oncology · 2025Article
- An elderly woman with acute respiratory failure and diffuse pulmonary changes.Breathe (Sheffield, England) · 2024Article
- Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung.Cancer management and research · 2024Article
- Clinical, laboratory, pathological, and radiological characteristics and prognosis of patients with pulmonary salivary gland-type tumors.Journal of cancer research and clinical oncology · 2023Article
- High-grade pulmonary mucoepidermoid carcinoma supplied by the pulmonary artery and vein in a 6-year-old child: a case description and literature analysis.Quantitative imaging in medicine and surgery · 2023Article
- Endoscopic resection of bronchial mucoepidermoid carcinoma in a young adult man: A case report and review of literature.World journal of clinical cases · 2022Article
- Pulmonary Salivary Gland Tumor, Mucoepidermoid Carcinoma: A Literature Review.Journal of oncology · 2022Review
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7 authors at 2 institutions in 2 countries.
Funding
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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.
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