基于CT的影像组学特征预测间变性淋巴瘤激酶阳性Ⅲ/Ⅳ期非小细胞肺癌患者脑转移

Application of radiomics signature captured from CT to predicting brain metastases in stage Ⅲ/Ⅳ anaplastic lymphoma kinase-positive non-small cell lung cancer patients

背景与目的:间变性淋巴瘤激酶(anaplastic lymphoma kinase,ALK)阳性的非小细胞肺癌(non-small cell lung cancer,NSCLC)患者的脑转移发生概率为20%~35%,脑部也是患者靶向治疗耐药时常见的转移部位之一。这类患者脑转移情况的早期预测对其预后评价及疗效评估具有重要意义。影像组学是从图像中高通量提取定量特征以宏观描述肿瘤表型和异质性。探讨影像组学在预测ALK阳性的Ⅲ/Ⅳ期NSCLC患者治疗前脑转移情况方面的应用。方法:回顾性纳入2014年6月—2017年9月在复旦大学附属肿瘤医院经病理学检查证实的ALK阳性Ⅲ/Ⅳ期NSCLC患者。在治疗前基线检查中获得患者脑转移信息。收集患者治疗前胸部CT图像,并由两名经验丰富的放疗科医师勾画大体肿瘤体积(gross tumor volume,GTV),进行影像组学特征的提取。按一定比例将患者分为训练集和验证集(4∶1)。在RIDER NSCLC数据集中进行testretest,筛选出132个稳定的影像组学特征。进行最小绝对收缩和选择运算符(least absolute shrinkage and selection operator,LASSO)COX回归和leave-one-out cross-validation以确定用于logistic回归分析的最佳组学特征,随后通过logistic回归分析影像组学特征及其他临床特征与患者治疗前脑转移发生的关系。结果:共纳入132例患者,其中27例患者治疗前发生脑转移。在训练集中,发现一个影像组学特征(W_GLCM_LH_Correlation)与脑转移显著相关(P=0.014),受试者工作特征(receiver operating characteristic,ROC)曲线的曲线下面积(area under curve,AUC)为0.687。该组学特征在验证集中预测性能也尚可(AUC=0.642)。将该影像组学特征与N分期相结合能在一定程度上提高对患者治疗前脑转移的预测能力(训练集:AUC=0.697;验证集:AUC=0.675)。结论:治疗前胸部CT的影像组学特征可能在预测ALK阳性Ⅲ/Ⅳ期NSCLC患者治疗前脑转移的发生方面有一定的价值,这可能有利于此类患者的风险分层。

Background and purpose:The probability of brain metastasis(BM)in anaplastic lymphoma kinase(ALK)-positive non-small cell lung cancer(NSCLC)patients is 20%-35%.Brain is also one of the main metastatic sites in patients with acquired resistance to targeted therapy.Therefore,early prediction of brain metastases in such patients is important for the assessment of overall prognosis and efficacy of targeted therapy.Radiomics demonstrates the idea of high-throughput extraction of quantitative features from images to macroscopically describe tumor phenotype and heterogeneity.The purpose of this study was to develop a radiomics approach to predict pre-treatment brain metastasis for stageⅢ/ⅣALK-positive NSCLC patients.Methods:Patients with pathologically confirmed ALK-positiveⅢ/ⅣNSCLC from Apr.2014 to Sep.2017 in Fudan University Shanghai Cancer Center were enrolled retrospectively.Their pretreatment thoracic CT images were collected,and the gross tumor volume(GTV)was defined by two experienced radiation oncologists.Patients were divided into training set and test set(4∶1).A test-retest in RIDER NSCLC dataset was performed to identify stable radiomics features.The least absolute shrinkage and selection operator(LASSO)COX regression and a leave-one-out cross-validation were conducted to identify optimal features for the logistic regression analyses to evaluate the predictive value of radiomics and clinical features for pretreatment BM.Results:In total,132 patients were included,among which 27 patients had pretreatment BM.In the training set,one radiomics feature(W_GLCM_LH_Correlation)was significantly correlated with BM[P value=0.014,area under curve(AUC)=0.687].It also exhibited moderate performance in the test set(AUC=0.642).Combining radiomics feature with N stage would further enhance predictive power(train set:AUC=0.697;test set:AUC=0.675).Conclusion:We identified one radiomics feature derived from pretreatment thoracic CT that was predictive of pretreatment BM in stageⅢ/ⅣALK-positive NSCLC patients,which could be beneficial to risk stratification for such patients.Further investigation is necessary.