Wang X, Xu Y H, Du Z Y, Qian Y J, Xu Z H, Chen R, Shi M H
Department of Respiration, Second Affiliated Hospital to Soochow University, Suzhou 215000, China.
Department of Thoracic Surgery, Second Affiliated Hospital to Soochow University, Suzhou 215000, China.
Zhonghua Zhong Liu Za Zhi. 2018 Feb 23;40(2):115-120. doi: 10.3760/cma.j.issn.0253-3766.2018.02.007.
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. 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. 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(<0.05). We calculated a prediction model for the probability of malignancy as follow: =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 (>0.05). 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.
本研究旨在分析孤立性肺结节患者的临床特征、影像学特征与病理诊断之间的关系，并建立恶性概率预测模型。回顾性分析372例行手术切除且术后病理诊断明确的孤立性肺结节患者的临床资料。在这些病例中，我们收集了临床和影像学特征，包括性别、年龄、吸烟史、肿瘤病史、癌症家族史、病变部位、磨玻璃影、最大直径、钙化、血管集束征、空泡征、胸膜凹陷征、毛刺征和分叶征。病例分为建模组（268例）和验证组（104例）。通过对建模组数据进行逻辑回归分析建立了一个新的预测模型。然后计划用验证组的数据验证新模型的有效性，并与三个经典模型（梅奥模型、VA模型和李云模型）进行比较。根据验证组各模型计算出的概率值，使用SPSS 22.0绘制受试者工作特征曲线，以评估这个新模型的预测价值。建模组纳入112例良性SPN和156例恶性SPN。多变量逻辑回归分析显示，性别、年龄、肿瘤病史、磨玻璃影、最大直径和毛刺征是SPN患者恶性肿瘤的独立预测因素（P<0.05）。我们计算出恶性概率预测模型如下：=e(x)/(1+e(x))，x=-4.8029 - 0.743×性别 + 0.057×年龄 + 1.306×肿瘤病史 + 1.305×磨玻璃影 + 0.051×最大直径 + 1.043×毛刺征。当将验证组数据加入到四元数学预测模型中时，我们数学预测模型的曲线下面积为0.742，大于其他模型（梅奥模型0.696、VA模型0.634、李云模型0.681），而四个模型中任意两个模型之间的差异均无统计学意义（P>0.05）。患者年龄、性别、肿瘤病史、磨玻璃影、最大直径和毛刺征是孤立性肺结节患者恶性肿瘤的独立预测因素。这个逻辑回归预测数学模型在估计SPN的预后方面不逊色于那些经典模型。
