肿瘤预防与治疗2019，Vol.32Issue(2)：139-143,5.DOI:10. 3969／j. issn. 1674-0904. 2019. 02. 006
超声征象评价在卵巢良恶性肿瘤鉴别诊断价值的Logistic回归分析
Ultrasound Image in the Differential Diagnosis of Benign and Malignant Ovarian Tumors: Logistic Regression Analysis
Chen Jing 1Zeng Lingling 1Luo Guanghui 1Peng Xin 1Peng Shunli1
作者信息
- 1. Department of Ultrasound, The People’s Hospital of Yubei District in Chongqing, Chongqing 401120, China
- 折叠
摘要
Abstract
Objective: To establish a Logistic regression model to predict benign and malignant ovarian tumors based on ultrasound images, and evaluate the value of Logistic model in the differential diagnosis of benign and malignant ovarian tumors. Methods: We retrospectively selected 189 cases of ovarian tumors at The People’s Hospital of Yubei District in Chongqing from January 2013 to March 2016. Among them, 69 cases was malignant and 120 cases was benign. Ultrasound features of benign and malignant ovarian tumors were compared. With pathologic diagnosis as gold standard, a Logistic model was established to calculate the accuracy, sensitivity, specificity and other indicators of the prediction model. A receiver op-erating characteristic (ROC) curve was drawn to calculate the area under the curve. Results: Univariate and multivariate Logistic regression analysis showed that morphology (OR=7. 149), internal echo (OR=7. 085), blood flow (OR=8. 908) and RI (OR=13. 224) were the main ultrasonographic features in differential diagnosis of benign and malignant ovarian tumors. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of Logistic regression model were 93. 7% (177／189), 92. 5% (111／120), 95. 7% (66／69), 97. 4% (111／114) and 88% (66／75), respec- tively. The area under the ROC curve was 0. 945 ± 0. 019 ( P<0. 001, 95% CI: 0. 910 ~0. 976). Conclusion: Logistic model based on ultrasound features for the differential diagno- sis of benign and malignant ovarian tumors is highly valuable, and can be used to guide clinical practice.关键词
卵巢肿瘤/超声/Logistic回归模型/鉴别诊断Key words
Ovarian tumors/ Ultrasonography/ Logistic regression model/ Differential diagnosis分类
医药卫生引用本文复制引用
Chen Jing,Zeng Lingling,Luo Guanghui,Peng Xin,Peng Shunli..超声征象评价在卵巢良恶性肿瘤鉴别诊断价值的Logistic回归分析[J].肿瘤预防与治疗,2019,32(2):139-143,5.
