分析孤立性肺结节的危险因素并探讨肺部结节良恶性预测模型。

收集雅安市人民医院2017年1月至2018年8月经胸外科手术切除且有明确病理诊断的112例孤立性肺结节患者的临床资料。回顾性分析其年龄、性别、吸烟史、肿瘤家族史、既往肿瘤史、血清癌胚抗原(CEA)、神经元特异性烯醇化酶(NSE)、细胞角蛋白19片段(CYFRA21-1)，以及肺部结节密度、直径、位置、分叶、毛刺、胸膜凹陷征、血管集束征、空泡征、空气支气管征、钙化等影像学特征。根据病理诊断分为良性、恶性两组，进行单因素分析，将单因素分析中有显著性差异的临床信息纳入Logistic回归分析，筛选出恶性结节的独立危险因素并建立预测模型。

单因数分析中年龄、既往肿瘤史、CEA、CYFRA21-1、结节密度、分叶、毛刺、胸膜凹陷征、血管集束征、空泡征、钙化征有统计学差异(P<0.05)。Logjistic回归分析显示患者年龄、CEA、CYFRA21-1、磨玻璃密度、分叶为恶性结节的独立危险因素。恶性肺部结节的预测模型公式为：P＝e^x/(1+e^x)，x＝-8.816+(3.018×密度)+(0.073×年龄)+(0.482×CEA)+(0.426×CRFRA21-1)+(1.421×分叶)。

患者年龄、血CEA、血CYFRA21-1、磨玻璃密度、分叶为恶性结节的独立危险因素，预测模型对恶性肺结节有较好的敏感性及特异性，诊断准确性较高。

To analyze the risk factors of solitary pulmonary nodules and establish a predictive model for benign and malignant pulmonary nodules.

The clinical data of 112 patients with solitary pulmonary nodules, who had a definite pathological diagnosis and underwent thoracic surgery in Ya′an People′s Hospital from January 2017 to August 2018, were reviewed respectively. Their age, gender, smoking history, family history of cancer, past cancer history, serum cancer biomarkers including carcinoembryonic antigen (CEA), neuron specific enolase (NSE) and cytokeratin 19 fragment (CYFRA21-1), and the radiological charateristics including the nodule density, diameter, location, lobulation, burr, pleural indentation, vascular cluster sign, vacuole sign, air information of bronchial sign and calcification sign, were summarized. The patients were divided into two groups according to the pathological diagnosis of their benign or malignant pulmonary nodules. After a univariate analysis, the clinical information with significant differences was chosen for logistic regression analysis and these independent risk factors for malignant pulmonary nodules were screened. Finally, a predicative model for benign and malignant pulmonary nodules was established.

There were significant differences in age, past cancer history, serum CEA and CYFRA21-1, nodule density, lobulation, burr, pleural indentation sign, vascular cluster sign, vacuole sign and calcification sign between the patients with benign and malignant pulmonary nodules (P<0.05). Logistic regression analysis showed that age, CEA, CYFRA21-1, ground glass density and lobulation were independent risk factors for malignant pulmonary nodules. One model was established as following: P=e^x/(1+ e^x), x=-8.816+ (3.018×density)+ (0.073×age)+ (0.482×CEA)+ (0.426×CRFRA21-1)+ (1.421×lobulation).

Age, CEA, CYFRA21-1, ground glass density and lobulation are independent risk factors for malignant pulmonary nodules. And the predictive model of malignant pulmonary nodules has a better sensitivity and specificity and a high diagnostic accuracy.