实性孤立性肺结节诊断模型的建立  

Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules

作　　者：喻微[1] 叶波[2] 续力云[3] 王兆宇[4] 乐涵波[1] 王善军[5] 曹捍波[5] 柴振达 陈志军[1] 罗清泉[2] 张永奎[1] 

机构地区：[1]温州医科大学附属舟山医院胸心外科,舟山316021 [2]上海交通大学附属胸科医院,上海200030 [3]温州医科大学附属舟山医院肺癌研究中心,舟山316021 [4]温州医科大学附属舟山医院病理诊断中心,舟山316021 [5]温州医科大学附属舟山医院放射诊断中心,舟山316021

出　　处：《中国肺癌杂志》2016年第10期705-710,共6页Chinese Journal of Lung Cancer

基　　金：国家卫生计生委科学研究基金-浙江省医药卫生重大科技计划(省部共建计划)(No.WKJ2014-2-021);浙江省科技厅公益技术社会发展项目(No.2015C33254);上海市卫生局局级青年课题(No.20134y126)资助~~

摘　　要：背景与目的孤立性肺结节(solitary pulmonary nodule,SPN)是一个常见并富有挑战性的临床问题,其中尤以实性SPN为甚,本研究旨在建立实性SPN临床预测模型。方法回顾性分析2015年1月-2015年12月上海胸科医院胸外科经手术病理证实的实性SPN患者317例(A组),分析其临床和计算机断层扫描(computed tomography,CT)影像特征:年龄、性别、吸烟史、肿瘤家族史、肿瘤既往史、结节直径、位置(上叶或者非上叶,左肺或者右肺)、边界清楚、边缘光滑、分叶征、毛刺征、血管集束征、胸膜牵拉征、空气支气管征、空泡征、空洞和钙化。通过单因素和多因素分析,寻找恶性实性SPN的独立预测因子,并建立临床预测模型。随后,利用温州医科大学附属舟山医院经手术病理证实的139例实性SPN患者作为B组,用于验证本模型的诊断效能,并绘制受试者工作特征曲线(receiver operating characteristic curve,ROC曲线)。结果多因素Logistic回归分析筛选出年龄、肿瘤家族史、肿瘤既往史、边界清晰、分叶、毛刺、空气支气管征及钙化为恶性实性SPN患者的独立预测因子。利用筛选出的预测因子建立的诊断模型ROC曲线下面积为0.922(95%CI:0.865-0.961),本模型的诊断准确率是84.89%,敏感性是90.41%,特异性是78.79%,阳性预测值是80.50%,阴性预测值是88.14%。结论本研究建立的预测模型能较准确的诊断实性SPN,可为SPN患者的诊断提供有利帮助。Background and objective The solitary pulmonary nodule（SPN） is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs（group A） having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography（CT） images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location（upper lobe or non-upper lobe, left or right）, clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic（ROC） curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics（age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification） as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model（0.922; 95%CI： 0.865-0.961）. In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients wi

关 键 词：孤立性肺结节 预测模型 独立预测因子 

分 类 号：R734.2[医药卫生—肿瘤]