基于治疗前增强CT图像纹理特征预测非小细胞肺癌免疫治疗疗效

Predicting response to non-small cell lung cancer immunotherapy using pre-treatment contrast-enhanced CT texture-based classification

目的探讨基于治疗前增强CT图像纹理特征预测非小细胞肺癌(NSCLC)免疫治疗疗效的可行性。方法回顾性分析2018年1—7月在上海市胸科医院接受二线单药免疫治疗的42例NSCLC患者51个病灶的治疗前增强CT图像资料。应用MaZda软件计算感兴趣区内病灶图像的纹理特征参数,分别采用Fisher系数法、交互信息法、分类错误概率联合平均相关系数法筛选10个纹理特征参数。根据首次免疫治疗疗效,将51个病灶分为无进展组(26个)和进展组(25个),比较两组间各纹理特征参数的差异。分别采用主成分分析(PCA)、线性判别分析(LDA)和非线性判别分析(NDA)方法对靶病灶的免疫治疗效果进行分类,计算各分类方法的灵敏度、特异度、准确性,并采用受试者工作特征曲线分析比较各分类方法预测疗效的效能。结果在Fisher系数、相关信息法和分类错误概率联合平均相关系数法提取的3组纹理特征中,进展组的Perc.50%、Perc.90%、S(4,4)SumEntrp和S(5,5)SumEntrp均高于无进展组(均P<0.05)。以分类错误概率联合平均相关系数法提取的纹理特征进行NDA法分类预测NSCLC免疫治疗疗效的效能最佳(曲线下面积为0.802,95%CI为0.674~0.930),其预测疾病进展的灵敏度、特异度、准确性、阳性预测值和阴性预测值分别为72.0%、88.5%、80.4%、85.7%和76.7%。结论治疗前增强CT图像纹理特征可以用于预测NSCLC免疫治疗的疗效。

Objective To explore the value of pre-treatment contrast-enhanced computed tomography(CT)-based texture analysis in predicting response to non-small cell lung cancer(NSCLC)immunotherapy.Methods From January to July 2018,a total of 51 lesions from 42 patients with advanced non-small cell lung cancer receiving immunotherapy at Shanghai Chest Hospital were selected in this retrospective study.Pre-treatment contrast-enhanced CT-based texture features were extracted by MaZda software.Ten optimal texture features were chosen based on three different methods:Fisher coefficient,mutual information measure(MI)and minimization of classification error probability combined average correlation coefficients(POE+ACC),respectively.According to the efficacy of the first immunotherapy,51 lesions were divided into non-progressive disease(non-PD,n=26)and progressive disease(PD,n=25).The differences were tested in each texture feature set between the two groups.The immunotherapy effects of target lesions were analyzed by principal component analysis(PCA),linear discriminant analysis(LDA)and nonlinear discriminant analysis(NDA).The sensitivity,specificity,accuracy,positive-predictive value(PPV)and negative-predictive value(NPV)were calculated.The area under the curve(AUC)was used to quantify the predictive accuracy of the three analysis models for each texture feature set and compare them with the actual classification results.Results In all of three texture feature sets,the texture parameter differences of Perc.50%,Perc.90%,"S(5,5)SumEntrp"and"S(4,4)SumEntrp"were higher in PD group than those in non-PD group(all P<0.05).The classification result of texture feature set chosen by POE+ACC and analyzed by NDA was identified as the best model(AUC=0.802,95%CI:0.674-0.930),and its sensitivity,specificity,accuracy,PPV and NPV were 72%,88.5%,80.4%,85.7%,76.7%,respectively.Conclusion Pre-treatment contrast-enhanced CT-based texture characteristics of NSCLC may function as non-invasive biomarkers for the evaluation of response to immunotherapy.