基于3D增强CT影像组学的肾癌亚型三分类预测模型

A three categories prediction model for renal cell carcinoma subtype based on 3D enhanced CT radiomics

目的探讨可靠的基于3D多期增强CT影像组学特征的肾癌亚型三分类预测模型。方法210例肾细胞癌患者(透明细胞癌143例,乳头状癌25例,嫌色细胞癌29例,其他亚型的肾细胞癌13例)被纳入研究。使用ITK-SNAP软件,获取患者的3D增强CT病灶分割图像,使用PyRadiomics计算平台进行特征提取,使用集成学习分层bagging法来筛选特征和构建肾细胞癌亚型三分类预测模型:首先用100次5折交叉验证将模型分为训练集和测试集,然后将Lasso回归作为基学习器对影像组学特征的进行筛选,最后使用logistic回归作为基学习器进行建模和校正。根据不同期像的CT图像,构建平扫期模型、皮髓质期模型、实质期模型、排泄期模型和全期模型。使用准确率、精确率、敏感度和Kappa值评估测试集上不同期像预测模型的性能。结果每期CT图像中提取到了105个影像组学特征。在5个模型中,全期模型的预测效能最好,准确率为0.81,AUC为0.85;精确度为0.717;敏感度为0.799,kappa值为0.679。全期模型的影像组学特征中,有4个皮髓质期特征、3个实质期特征、1个排泄期特征和1个平扫期特征,且与其他4个单期模型中的特征没有重叠。在4个单期模型中,实质期模型的性能最好,准确性0.786,精确度0.689,敏感度0.734,AUC 0.811,Kappa值0.532;皮髓质期模型和排泄期模型的性能相似,但是排泄期模型的Kappa值0.285,明显低于皮髓质期的Kappa值0.446。平扫期模型的性能最差,AUC为0.693。结论基于3D多期增强CT影像组学特征的全期模型是区分肾细胞癌亚型的可靠和有效的方法。

Objective To construct an effective and reliable three categories prediction model to distinguish RCC subtypes based on 3D multi-phase enhanced computed tomography(CT)radiomic features(RFs).Methods A total of 210 RCC were included in this study(143 clear cell,25 papillary,29 chromophobe,and 13 other (RCC).The 3D multi-phase enhanced CT-based RFs were used to construct a prediction model.CT included a non-contrast phase(NCP),cortico-medullary phase(CMP),parenchyma phase(PP),excretory phase(EP),and all-phase(ALL-P),which contains all the single-phase information.The ensemble learning stratified bagging method was used to predict the RCC subtype by using LASSO regression and a 1 vs.rest logistic regression algorithm.Five-fold and external stratified cross-validation was used to assess the performance of the different prediction models.Results There were 105 RFs extracted from each single phase of the CT scan.The 4 CMP,3 PP,1 EP,and 1 NCP RF were selected in the ALL-P model,and these RFs was no overlap with the other 4 single-phase models.The prediction efficiency of ALL-P was the best,with a diagnostic accuracy of 0.81[area under the receiver operating characteristic curve(AUC)=0.853,precision=0.717,sensitivity=0.799,kappa=0.679].Among the four single phase models,the PP model had the best performance,with accuracy of 78.3%(AUC=0.811,precision=0.689,sensitivity=0.735,kappa=0.532).The performance of the CMP model was similar to that of the EP model,but the kappa value of the EP model was 0.285,which was significantly lower than that of the CMP model(0.446).The performance of the model in NCP was the worst,AUC was 0.693.Conclusion The ALL-P prediction model based on 3D CT RFs is an effective and reliable method for distinguishing RCC subtypes.