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目的 构建一种含肿瘤代谢因素的孤立性肺结节(SPN)恶性风险预测模型并验证其效能.方法 回顾性分析2013年1月到2017年7月362例(男194例、女168例,中位年龄61岁)PET/CT示SPN患者的临床和影像学资料,其中恶性291例,良性71例(按病理学或随访结果确诊).分析良、恶性SPN患者间多种因素差异,通过多因素非条件logistic回归分析筛选危险因素,建立回归方程.以受试者工作特征(ROC)曲线进行模型自身验证,通过k折交叉验证进行模型的组外验证.结果 良、恶性结节患者间的年龄及病灶的最大标准摄取值(SUVmax)、大小、分叶、毛刺、胸膜牵拉、血管连接、钙化、空泡、肺气肿等差异具有统计学意义(均P＜0.05).通过多因素logistic回归分析得出,年龄、SUVmax、大小、分叶、钙化、空泡为恶性结节的危险因素,其比值比[OR(95％ CI)]分别为:1.040(1.007～ 1.075)、1.612(1.287～2.017)、1.149(1.074～ 1.230)、4.650(2.138～ 10.115)、0.216(0.085～0.548)、3.043(1.302～ 7.111).建立模型:P=1/(1+e-x),x=-5.583+0.039×年龄+0.477×SUVmax+0.139×大小+1.537×分叶-1.532×钙化+1.113×空泡.模型曲线下面积[AUC(95％ CI)]为:0.915(0.883～0.947),灵敏度为89.7％、特异性为78.9％;k折交叉验证显示,训练准确性为0.899±0.011,预测准确性为0.873±0.053.结论 患者的年龄及病灶的SUVmax、大小、分叶、钙化和空泡为恶性结节的危险因素;经过验证,该模型具有良好的准确性和稳定性,可辅助临床作出更准确的决策.

Objective To develop a model of malignant risk prediction of solitary pulmonary nodules(SPN) with the metabolic characteristics of the lesion.Methods A total of 362 patients (291 malignant cases and 71 benign cases;194 males,168 females;median age:61 years) who underwent PET/CT imaging from January 2013 to July 2017 were analyzed.The diagnosis of malignant SPN was based on pathological results,and that of benign SPN was based on pathological or follow-up results.Differences of clinical/imaging characteristics in patients with benign and those with malignant SPN were analyzed.Risk factors were screened by multivariate non-conditional logistic regression analysis.The self-verification of the model was done by the receiver operating characteristic (ROC) curve analysis,out-of-group verification was performed by k-fold cross-validation.Results There were statistically significant differences in age,maximum standardized uptake value (SUVmax),size,lobulation,spiculation,pleural traction,vessel connection,calcification,vacuole,and emphysema between patients with benign and malignant nodules (all P＜0.05).The risk factors for malignant nodules included age,SUVmax,size,lobulation,calcification and vacuole.The odds ratio (OR) values (95％ CI) were 1.040(1.007-1.075),1.612(1.287-2.017),1.149(1.074-1.230),4.650(2.138-10.115),0.216(0.085-0.548),and 3.043(1.302-7.111),respectively.The logistic regression model was as follows:P=1/(1+e-x),x=-5.583+0.039×age+0.477×SUVmaxx+0.139×size+1.537×lobulation-1.532×calcification+ 1.1 13×vacuole.The estimated area under the curve (AUC) for the model was 0.915(95％ CI:0.883-0.947),sensitivity was 89.7％,specificity was 78.9％.K-fold cross-validation showed that the training accuracy was 0.899±0.011,the predictive accuracy was 0.873±0.053.Conclusions The risk factors for malignant nodules included age,SUVmax,size,lobulation,calcification and vacuole.After verification,the model has a satisfactory accuracy.It may help clinics make accurate decisions.