表现为磨玻璃结节的孤立性肺结节诊断模型的建立  

Establishment of the Diagnostic Model in Solitary Pulmonary Nodule Appearing as Ground-glass Nodule

作　　者：喻微[1] 陈天翔[2] 续力云 王兆宇[4] 曹捍波[5] 张永奎[1] YU Wei CHEN Tianxiang XU Liyun WANG Zhaoyu CAO Hanbo ZHANG Yongkui(Department of Cardiothomcic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhoushan 316021, China)

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

出　　处：《中国医学影像学杂志》2017年第6期435-440,共6页Chinese Journal of Medical Imaging

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)(WKJ2014-2-021);浙江省科技厅公益技术社会发展项目(2015C33254);国家自然科学基金青年基金项目(81301996)

摘　　要：目的探讨表现为磨玻璃结节(GGN)的恶性孤立性肺结节(SPN)的独立预测因子,并建立一个预测模型。资料与方法回顾性研究2014年1月—2015年12月上海交通大学附属胸科医院经病理证实的362例(A组)表现为GGN的SPN患者的临床和CT影像特征,筛选出恶性SPN的独立预测因子并建立预测模型。同时收集温州医科大学附属舟山医院119例SPN患者作为B组,用于验证模型诊断效能。结果多因素Logistic回归分析筛选出边界清楚(OR=6.274,P<0.01)、边缘光滑(OR=0.391,P<0.01)、分叶征(OR=3.387,P<0.01)、胸膜牵拉征(OR=2.430,P<0.01)及空泡征(OR=3.076,P<0.01)为恶性SPN患者的独立预测因子。根据独立预测因子建立的模型受试者工作特性(ROC)曲线下面积为0.859(95%CI:0.804~0.903),诊断准确率85.92%,敏感度91.03%,特异度81.97%,阳性预测值92.03%,阴性预测值73.53%。结论本研究筛选出表现为GGN的恶性SPN的独立预测因子,并建立了可用于准确鉴别SPN的预测模型,可为SPN的早期诊断提供帮助。Purpose To explore the independent predictors of malignant solitary pulmonary nodule（SPN） manifesting as ground-glass nodule（GGN）, and to establish a prediction model. Materials and Methods Materials and Methods The clinical data and CT images of 362 patients（group A） with pathological-confirmed SPN appearing as GGN in Shanghai Chest Hospital Shanghai Jiaotong University from January 2014 to December 2015 were retrospectively analyzed. The independent predictors of malignant SPN were identifi ed, and the clinical prediction model was established. Another 119 SPN patients in Affiliated Zhoushan Hospital of Wenzhou Medical University were selected as group B to verify the diagnostic effi ciency of the prediction model. Results Using multivariate Logistic regression analysis, clear border（OR=6.274, P〈0.01）, smooth edge（OR=0.391, P〈0.01）, lobulation（OR=3.387, P〈0.01）, pleural retraction sign（OR=2.430, P〈0.01）, and vocule sign（OR=3.076, P〈0.01） were identifi ed as independent predictors of malignant SPN. The area of the model under the ROC curve was 0.859 with 95% CI（0.804-0.903）. The diagnostic accuracy rate, sensitivity, specifi city, positive predictive value and negative predictive value were 85.92%, 91.03%, 81.97%, 92.03% and 73.53%, respectively. Conclusion In this study, the independent predictors of malignant SPN appearing as GGN were identified, and the prediction model was established. The model can accurately identify SPN and provide effective help for early diagnosis of SPN.

关 键 词：结节病 肺 肺肿瘤 体层摄影术 螺旋计算机 LOGISTIC模型 预测 

分 类 号：R816.41[医药卫生—放射医学] R445[医药卫生—临床医学]