Wang Bin, Jiang Siyuan, Zhu Lizhe, Sheng Wei, Qiao Yan, Zhang Huimin, Zhang Jian, Liu Yang, Hao Na, Ma Xiaoxia, Zhou Can, Ren Yu
Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Cancer Manag Res. 2020 Nov 3;12:11191-11201. doi: 10.2147/CMAR.S273728. eCollection 2020.
Nipple discharge is a common symptom of breast disease. We aimed to perform a descriptive statistical analysis of the cases we evaluated and establish a model to predict intraductal tumors.
We conducted a retrospective study of patients from 2007 to 2019. In total, 1333 patients who completed the fiberoptic ductoscopy (FDS) were evaluated. The variables were analyzed by test. Logistic regression was used to analyze the relationship between the patient's clinical characteristics and intraductal tumors and establish a predictive model. Receiver operating characteristic (ROC) curve analysis was used to assess the sensitivity and specificity of the predictive ability of the model. Calibration curves and decision curve analysis (DCA) were used to evaluate the model.
Patients with spontaneous, single-duct, bloody discharge and a smooth ductal wall were more likely to be diagnosed with tumors by ductoscopy. A model was established based on five variables: age, side of discharge, spontaneous discharge status, duration of discharge, and color of discharge. The model was subsequently validated in 183 patients with complete data on the variables in the validation cohort. The area under the ROC curve (AUC) was calculated to be 0.716, indicating good predictive ability.
Patients with the clinical characteristics of unilateral, bloody, single-duct, spontaneous discharge and a smooth ductal wall were more likely to have intraductal tumors by ductoscopy. Our nomogram can effectively predict intraductal tumors in patients with nipple discharge.
乳头溢液是乳腺疾病的常见症状。我们旨在对所评估的病例进行描述性统计分析,并建立一个预测导管内肿瘤的模型。
我们对2007年至2019年的患者进行了回顾性研究。总共评估了1333例完成纤维乳管镜检查(FDS)的患者。通过检验分析变量。采用逻辑回归分析患者的临床特征与导管内肿瘤之间的关系,并建立预测模型。采用受试者操作特征(ROC)曲线分析评估模型预测能力的敏感性和特异性。使用校准曲线和决策曲线分析(DCA)来评估该模型。
自发性、单导管、血性溢液且导管壁光滑的患者通过乳管镜检查更有可能被诊断为肿瘤。基于五个变量建立了一个模型:年龄、溢液侧、自发性溢液状态、溢液持续时间和溢液颜色。随后在183例验证队列中具有变量完整数据的患者中对该模型进行了验证。计算得出ROC曲线下面积(AUC)为0.716,表明具有良好的预测能力。
具有单侧、血性、单导管、自发性溢液且导管壁光滑临床特征的患者通过乳管镜检查更有可能患有导管内肿瘤。我们的列线图可以有效预测乳头溢液患者的导管内肿瘤。