[Risk factor analysis of the patients with solitary pulmonary nodules and establishment of a prediction model for the probability of malignancy].

Objective: This study aims to analyze the relationship among the clinical features, radiologic characteristics and pathological diagnosis in patients with solitary pulmonary nodules, and establish a prediction model for the probability of malignancy. Methods: Clinical data of 372 patients with solitary pulmonary nodules who underwent surgical resection with definite postoperative pathological diagnosis were retrospectively analyzed. In these cases, we collected clinical and radiologic features including gender, age, smoking history, history of tumor, family history of cancer, the location of lesion, ground-glass opacity, maximum diameter, calcification, vessel convergence sign, vacuole sign, pleural indentation, speculation and lobulation. The cases were divided to modeling group (268 cases) and validation group (104 cases). A new prediction model was established by logistic regression analying the data from modeling group. Then the data of validation group was planned to validate the efficiency of the new model, and was compared with three classical models(Mayo model, VA model and LiYun model). With the calculated probability values for each model from validation group, SPSS 22.0 was used to draw the receiver operating characteristic curve, to assess the predictive value of this new model. Results: 112 benign SPNs and 156 malignant SPNs were included in modeling group. Multivariable logistic regression analysis showed that gender, age, history of tumor, ground -glass opacity, maximum diameter, and speculation were independent predictors of malignancy in patients with SPN(P<0.05). We calculated a prediction model for the probability of malignancy as follow: p=e(x)/(1+ e(x)), x=-4.8029-0.743×gender+ 0.057×age+ 1.306×history of tumor+ 1.305×ground-glass opacity+ 0.051×maximum diameter+ 1.043×speculation. When the data of validation group was added to the four-mathematical prediction model, The area under the curve of our mathematical prediction model was 0.742, which is greater than other models (Mayo 0.696, VA 0.634, LiYun 0.681), while the differences between any two of the four models were not significant (P>0.05). Conclusions: Age of patient, gender, history of tumor, ground-glass opacity, maximum diameter and speculation are independent predictors of malignancy in patients with solitary pulmonary nodule. This logistic regression prediction mathematic model is not inferior to those classical models in estimating the prognosis of SPNs.目的： 研究孤立性肺结节(SPN)的临床和CT影像学特征与其病理诊断结果的关联，建立SPN良恶性预测的数学模型。 方法： 选取372例经手术切除并获取明确病理诊断的肺结节病例，分析患者的临床特征(性别、年龄、吸烟史、肿瘤史、病理结果)和CT影像学特征(结节位置、是否为磨玻璃结节、结节长径、钙化征、血管集束征、气管充气征、空泡征、胸膜凹陷征、毛刺征、分叶征)。将病例分为建模组(268例)和验证组(104例)。利用建模组患者的资料，进行多因素Logistic回归分析，构建预测结节良恶性的模型。将验证组的数据代入该模型进行验证，并和经典模型(Mayo模型、VA模型、李运模型)进行比较，绘制受试者工作特征曲线(ROC)，评估预测价值。 结果： 建模组268例肺结节中，良性112例(41.8%)，恶性156例(58.2%)。多因素Logistic回归分析显示，性别、年龄、肿瘤史、结节长径、磨玻璃结节、毛刺征是预测SPN良恶性的独立因素(P<0.05)，SPN良恶性预测模型为p＝e(x)/(1＋e(x))，x＝－4.8029－0.743×性别＋0.057×年龄＋1.306×肿瘤史＋1.305×磨玻璃结节＋0.051×结节长径＋1.043×毛刺征。验证结果显示，所建立模型的ROC曲线下面积为0.742，与Mayo模型(0.696)、VA模型(0.634)、李运模型(0.681)比较，差异均无统计学意义(均P>0.05)。 结论： 患者的性别、年龄、肿瘤史、磨玻璃结节、结节长径、毛刺征是预测结节恶性的危险因素。建立的预测模型用来预测SPN良恶性，效力不劣于其他经典模型。.