Xiaohua Ban1, Xinping Shen2, Huijun Hu3, Rong Zhang1, Chuanmiao Xie1, Xiaohui Duan4, Cuiping Zhou5. 1. Department of Medical Imaging Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China. 2. Department of Radiology, The University of Hong Kong-Shenzhen Hospital, No.1, Haiyuan Road Futian District, Shenzhen, 518000, People's Republic of China. 3. Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, Guangdong, 510120, People's Republic of China. 4. Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, Guangdong, 510120, People's Republic of China. duanxiaohui-128@163.com. 5. Department of Radiology, The University of Hong Kong-Shenzhen Hospital, No.1, Haiyuan Road Futian District, Shenzhen, 518000, People's Republic of China. zhoucuiping0@126.com.
Abstract
BACKGROUND: To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). MATERIALS AND METHODS: CT 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. RESULTS: CT 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). CONCLUSION: The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.
BACKGROUND: To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). MATERIALS AND METHODS: CT 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. RESULTS: CT 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). CONCLUSION: The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.
Authors: William D Travis; Elisabeth Brambilla; Andrew G Nicholson; Yasushi Yatabe; John H M Austin; Mary Beth Beasley; Lucian R Chirieac; Sanja Dacic; Edwina Duhig; Douglas B Flieder; Kim Geisinger; Fred R Hirsch; Yuichi Ishikawa; Keith M Kerr; Masayuki Noguchi; Giuseppe Pelosi; Charles A Powell; Ming Sound Tsao; Ignacio Wistuba Journal: J Thorac Oncol Date: 2015-09 Impact factor: 15.609
Authors: Julian R Molina; Marie Christine Aubry; Jean E Lewis; Jason A Wampfler; Brent A Williams; David E Midthun; Ping Yang; Stephen D Cassivi Journal: Cancer Date: 2007-11-15 Impact factor: 6.860