增强CT影像组学在鉴别肝转移性腺癌来源的运用  

The Application of Contrast-Enhanced CT Radiomics in the Differential Diagnosis of Hepatic Metastatic Adenocarcinomas Source

作　　者：侯承师 胡景卉 黄京城 杨鑫 王文剑 吴晶涛 孙骏 陈磊 王芳 罗先富 HOU Chengshi;HU Jinghui;HUANG Jingcheng(Dalian Medical University,Dalian,Liaoning Province 116044,P.R.China)

机构地区：[1]大连医科大学,116044 [2]苏北人民医院医学影像科,扬州225001 [3]扬州大学医学院,225001 [4]上海联影智能医疗科技有限公司,200232

出　　处：《临床放射学杂志》2024年第1期73-78,共6页Journal of Clinical Radiology

基　　金：江苏省人力资源和社会保障厅江苏省“333”项目(编号:2022-3-6-139)。

摘　　要：目的探讨基于机器学习的增强CT影像组学模型对肝转移性腺癌来源预测的可行性。方法回顾性分析317例肝转移瘤患者的增强CT图像及临床影像资料,其中153例非胃肠道来源腺癌(25例乳腺腺癌,128例肺腺癌)和164例胃肠道来源腺癌(95例结直肠腺癌,41例胃腺癌,28例胰腺腺癌)。在增强CT三期图像中分别分割肿瘤体积。使用联影科研平台(uAI)提取影像组学特征,用最小绝对收缩与选择算子算法(LASSO)进行特征筛选。结合年龄及性别构建支持向量机分类器预测模型。两位影像医师根据影像特征进行预测。受试者工作特征(ROC)曲线分析各类模型效能,Delong检验对比模型诊断效能。决策曲线分析(DCA)探索模型临床应用价值,校准曲线评估模型预测精度。结果经LASSO算法从三期图像中共获得6个影像组学特征,建立的影像组学联合模型曲线下面积(AUC)为0.738,结合年龄及性别建立临床影像组学模型的AUC值、敏感度、特异度和准确度分别达到0.833、0.740、0.804和0.771。两位影像医师诊断的AUC值分别为0.643和0.664。临床影像组学模型诊断效能高于两位影像医师诊断,差异有统计学意义(P<0.05)。结论增强CT影像组学联合模型能鉴别肝转移瘤来源于胃肠道及非胃肠道腺癌,在与年龄及性别联合后支持向量机模型诊断效能提高,明显优于影像医师诊断效能。Objective To explore the feasibility of contrast-enhanced CT(CECT)radiomics models based on machine learning in tumor source prediction of different liver metastatic adenocarcinomas.Methods The clinical and CECT image data of 317 cases were analyzed retrospectively,including 153 from non-gastrointestinal(25 from breast adenocarcinoma and 128 from lung adenocarcinoma)and 164 from gastrointestinal(95 from colorectal adenocarcinoma,41 from gastric adeno-carcinoma and 28 from pancreatic adenocarcinoma).The volumes of the tumors were segmented in the CECT images.The uAI research platform was used to extract radiomics features.Least absolute shrinkage and selection operator regression(LASSO)was used to select features.Combing with age and gender,SVM(support vector machine)classifiers models were built.Two radiologists predicted the metastatic tumor type on the basis of the image performance respectively.The ef-fectiveness of models was analyzed using the receiver operating characteristic curve(ROC).Delong test was used to evalu-ate models.Decision curve analysis was used to further explore the clinical utility of models.Calibration curves were used to assess predictive accuracy of models.Results Six radiomics features were obtained from triple-phase images by LASSO Regression.Area under the receiver operating characteristic curve(AUC)values of the combing radiomics model were 0.738.Combing with age and gender,the AUC,sensitivity,specificity and accuracy of clinical radiomics model were 0.833、0.740、0.804 and 0.771.The AUC values of two radiologists for the differential diagnosis were 0.643 and 0.664.The diagnos-tic effectiveness of the clinical radiomics model was higher than two radiologists reading,and the difference was statistically significant(P<0.05).Conclusion Combing radiomics models of CECT showed good performance in liver metastases source prediction of gastrointestinal or non-gastrointestinal adenocarcinoma.The effectiveness of the SVM models was im-proved when combing with age and gender,obviously higher

关 键 词：影像组学 计算机体层成像 原发灶未知 肝转移瘤 

分 类 号：R735.7[医药卫生—肿瘤] R730.44[医药卫生—临床医学]