CT影像组学特征预测食管癌调强放疗放射性肺炎的价值  

Value of CT radiomics features for predicting radiation pneumonitis in esophageal cancer patients treated with intensity-modulated radiotherapy

作　　者：李凯新[1] 毛润芝 高炳宗 陈雅云 蔡文杰[1] Li Kaixin;Mao Runzhi;Gao Bingzong;Chen Yayun;Cai Wenjie(Department of Radiation Oncology,First Hospital of Quanzhou Affiliated to Fujian Medical University,Quanzhou 362000,China)

机构地区：[1]福建医科大学附属泉州市第一医院放疗科,泉州362000

出　　处：《中华放射肿瘤学杂志》2023年第11期978-983,共6页Chinese Journal of Radiation Oncology

基　　金：泉州市科技计划项目(2018N049S);泉州市高层次人才创新创业项目(2018C057R)。

摘　　要：目的建立包含治疗前CT影像组学特征的食管癌调强放疗放射性肺炎(RP)的预测模型,并评价其应用价值。方法前瞻性分析2019年1月—2021年12月在福建医科大学附属泉州市第一医院连续入组的267例(前206例为训练集,后61例为验证集)食管癌调强放疗患者的临床资料,提取放疗定位CT的双肺影像组学特征。通过单因素分析选择可能预测症状性RP的变量,分别采用最小绝对收缩和选择算子(LASSO)、极致梯度提升(XGboost)、支持向量机(SVM)三种机器学习算法筛选影像组学特征,选择最佳的分类器组成影像组学标签(RS),并分别建立临床、影像组学以及联合预测模型。通过受试者操作特征曲线(ROC)的曲线下面积(AUC)、校准曲线以及决策曲线分析比较三种模型的预测效能和临床获益情况,并在验证集中验证。多变量分析采用logistic回归进行评估;不同ROC曲线采用DeLong检验进行比较。结果与RP独立相关的因素包括心血管疾病、食管最小内径、辅助化疗以及影像组学标签。临床、影像组学与联合预测模型的AUC,训练集分别为0.772、0.745与0.842,验证集分别为0.851、0.811与0.901。决策曲线分析显示联合预测模型的临床获益优于临床预测模型。结论治疗前CT的影像组学特征具有提高食管癌调强放疗RP预测模型预测效能的潜力。Objective To construct a predictive nomogram incorporating pretreatment CT-based radiomics for radiation pneumonitis(RP)in esophageal cancer(EC)patients treated with intensity-modulated radiotherapy(IMRT),and to evaluate the value of CT radiomics in predicting RP.Methods Clinical data of 267 EC patients sequentially treated with IMRT in Quanzhou First Hospital affiliated to Fujian Medical University from January 2019 to December 2021 were prospectively analyzed.Among them,the first 206 patients were assigned into the training cohort and the last 61 patients were enrolled in the validation cohort.Radiomics features of bilateral lungs were extracted by radiotherapy CT simulation.Univariate analysis was performed to screen the potential predictive variables for symptomatic RP.Machine learning algorithms,such as least absolute shrinkage and selection operator(LASSO),extreme gradient boosting(XGboost),and support vector machine(SVM),were performed for radiomic features selection,respectively.The best classifier was chosen to construct a radiomic signature(RS).Clinical,radiomics and combined nomogram predictive model were developed,respectively.The predictive efficiency and clinical benefits of three models were compared by calculating the area under the receiver operating characteristic(ROC)curve(AUC),calibration curve and decision curve analysis(DCA),and then validated in the validation cohort.Multivariate logistic regression analysis was conducted.Different ROC curves were compared by Delong test.Results Cardiovascular disease,minimum internal diameter of esophagus and adjuvant chemotherapy and RS were the independent related factors of RP.The AUC of clinical,radiomics and combined models were 0.772,0.745,0.842 in the training cohort,and 0.851,0.811,0.901 in the validation cohort,respectively.DCA showed that combined radiomic model yielded better clinical benefits compared with clinical model.Conclusion Radiomics features from pretreatment CT have the potential of improving the efficiency of RP prediction models for

关 键 词：食管肿瘤 调强放射疗法 放射性肺炎 CT 影像组学 

分 类 号：R735.1[医药卫生—肿瘤]