基于^(18)F-FDG PET/CT影像组学特征的机器学习对胃癌和原发性胃淋巴瘤的鉴别诊断价值

Value of machine learning model based on^(18)F-FDG PET/CT radiomics features in differential diagnosis between gastric cancer and primary gastric lymphoma

目的探讨基于^(18)F-FDG PET/CT影像组学特征的机器学习模型在胃癌(GC)和原发性胃淋巴瘤(PGL)的术前鉴别诊断中的价值。方法回顾性分析2012年1月至2020年12月于天津医科大学肿瘤医院术前行^(18)F-FDG PET/CT检查且经病理证实的155例GC患者[男104例、女51例,年龄(59.3±12.8)岁]和82例PGL患者[男40例、女42例,年龄(56.8±14.6)岁],使用Python3.7.1软件将患者随机分为训练集和测试集。分别对PET和CT图像进行感兴趣体积(VOI)勾画,提取三维和二维影像组学特征。使用多层感知机(MLP)、支持向量机(SVM)2种机器学习模型分别对CT影像组学特征、PET影像组学特征和PET/CT影像组学特征进行学习,以鉴别GC和PGL。通过ROC曲线分析评估各模型的预测性能。结果训练集166例,测试集71例。基于PET/CT影像组学特征的SVM模型在GC和PGL的鉴别诊断方面(AUC=0.88,95%CI:0.83~0.94)有优于MLP机器学习模型(AUC=0.80,95%CI:0.73~0.87)的趋势(z=1.15,P=0.337)。基于PET/CT影像组学特征的SVM预测模型对2种疾病的预测效果优于单独CT影像组学特征模型(CT-SVM:AUC=0.74,95%CI:0.67~0.81;z=2.28,P=0.022)。结论基于^(18)F-FDG PET/CT影像组学特征的机器学习模型有望成为GC和PGL患者术前无创且有效的鉴别诊断工具。

Objective To investigate the value of machine learning model based on^(18)F-FDG PET/CT radiomics features in preoperative differential diagnosis of gastric cancer(GC)and primary gastric lymphoma(PGL).Methods A total of 155 patients with GC(104 males,51 females;age(59.3±12.8)years)and 82 patients with PGL(40 males,42 females;age(56.8±14.6)years)who underwent^(18)F-FDG PET/CT imaging before treatment from January 2012 to December 2020 in Tianjin Medical University Cancer Institute and Hospital were included in this retrospective study.Patients were randomly divided into training set and test set by using Python3.7.1 software.Volumes of interest(VOIs)in PET and CT images were drawn and two-dimensional and three-dimensional radiomics features were extracted.Two machine learning models,including multi-layer perceptron(MLP)and support vector machine(SVM),were established based on CT radiomics features alone,PET radiomics features alone and PET/CT radiomics features to differentiate GC and PGL,respectively.The predictive performance of each model was evaluated by ROC curve analysis.Results There were 166 patients in training set and 71 patients in test set.Generally,SVM machine learning model based on PET/CT radiomics features showed a trend to be superior to MLP machine learning model in the differential diagnosis of GC and PGL(PET-SVM:AUC=0.88,95%CI:0.83-0.94);PET/CT-MLP:AUC=0.80,95%CI:0.73-0.87;z=1.15,P=0.337).The AUC of PET/CT-SVM machine learning model was significantly higher than that of CT-SVM machine learning model(CT-SVM:AUC=0.74,95%CI:0.67-0.81;z=2.28,P=0.022).Conclusion Machine learning model based on^(18)F-FDG PET/CT radiomics features is expected to be a non-invasive,effective tool for preoperative differential diagnosis of GC and PGL.