CT征象联合纹理分析鉴别硬化性肺泡细胞瘤与周围型肺癌的价值

The value of CT features combined with texture analysis in differential diagnosis of pulmonary sclerosing pneumocytoma and peripheral lung cancer

目的探讨CT征象联合纹理分析鉴别硬化性肺泡细胞瘤(pulmonary sclerosing pneumocytoma,PSP)与周围型肺癌(peripheral lung cancer,PLC)的价值。方法选取经病理证实的PSP患者136例及PLC患者131例,以7∶3的比例将所有病例随机分为训练集和验证集。分析两组病变CT特征及静脉期薄层CT图像纹理特征,用筛选出的特征参数构建多因素二元logistic回归模型并绘制ROC曲线,计算AUC值,评价各模型诊断PSP与PLC的效能。结果训练集和验证集两组间形态、钙化、液化坏死、分叶、毛刺、胸膜凹陷征、空洞、纵隔/肺门淋巴结肿大和16组最佳纹理参数差异有统计学意义(P<0.05)。CT特征模型由分叶、毛刺、胸膜凹陷征及纵膈/肺门淋巴结肿大组成;纳入训练集回归模型的最佳纹理参数共5组,分别为Perc.10%、S(3,0)SumAverg、S(4,0)SumAverg、S(4,4)SumAverg及WavEnLH_s-1。训练集中CT特征模型与CT纹理参数模型诊断PSP与PLC的AUC分别为0.847、0.851,二者间差异无统计学意义(P=0.912);CT特征联合纹理参数模型诊断两组病变的AUC最高,为0.939,其准确度、灵敏度和特异度分别为85.0%、82.1%和93.5%。验证集中影像学特征联合纹理参数模型诊断两组病变的AUC为0.923,高于CT影像特征模型(AUC=0.864;Z=2.627,P=0.009)和CT纹理参数模型(AUC=0.832;Z=2.147,P=0.031)。结论CT征象联合纹理分析对于鉴别PSP与PLC具有较好的诊断价值。

Objective To explore the value of CT features combined with texture analysis in differentiating pulmonary sclerosing pneumocytoma(PSP)from peripheral lung cancer(PLC).Methods 136 PSP patients and 131 PLC patients confirmed in the First Affiliated Hospital of Zhengzhou University were collected.All cases were randomly divided into training set and verification set according to the ratio of 7∶3.The imaging features of two groups of CT images and the texture of CT thin-layer images in venous phase were analyzed.The image features and texture parameters with statistical differences were used to construct multivariate binary logistic regression model.And then,we drew ROC curve,calculated AUC value,and evaluated and compared the effectiveness of each model in diagnosing PSP and PLC.Results There were statistical differences in morphology,calcification,liquefaction necrosis,lobulation,burr,pleural indentation,cavity,mediastinal/hilar lymph node enlargement and the best texture parameters of 16 groups between the two groups.The feature model of CT image consisted of lobulation,burr,pleural indentation and mediastinal/hilar lymph node enlargement.The five optimal texture parameters that included in the regression model of training set were Perc.10%,S(3,0)SumAverg,S(4,0)SumAverg,S(4,4)SumAverg and WavEnLH_s-1.In the training set,the AUC of PSP and PLC diagnosed by CT image feature model and CT texture parameter model were 0.847 and 0.851,respectively,and there was no significant difference between them(P=0.912).The AUC of imaging features combined with texture parameter model was the highest,which was 0.939,and its accuracy,sensitivity and specificity were 85.0%,82.1%and 93.5%respectively.In the verification set,the AUC of image features combined with texture parameter model in diagnosing two groups of lesions was 0.923,which was higher than that of CT image feature model(AUC=0.864;Z=2.627,P=0.009)and CT texture parameter model(AUC=0.832;Z=2.147,P=0.031).Conclusion CT image combined with texture analysis has good diagnostic value for distinguishing PSP from PLC.