勾画方法对^(18)F-FDG PET/CT影像组学预测胰腺导管腺癌病理分化程度的影响  

Impact of segmentation methods on pathological grade prediction in pancreatic ductal adenocarcinoma based on ^(18)F-FDG PET/CT radiomics

作　　者：郝志鑫 刘垒 邢海群 朱文佳 张辉[3] 霍力 Hao Zhixin;Liu Lei;Xing Haiqun;Zhu Wenjia;Zhang Hui;Huo Li(Department of Nuclear Medicine,Peking Union Medical College Hospital,Peking Union Medical College,Chinese Academy of Medical Sciences Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine,Beijing 100730,China;Sinounion Healthcare Inc.,Beijing 100192,China;Department of Biomedical Engineering,School of Medicine,Tsinghua University,Beijing 100084,China)

机构地区：[1]中国医学科学院、北京协和医学院北京协和医院核医学科、核医学分子靶向诊疗北京市重点实验室,100730 [2]赛诺联合医疗科技(北京)有限公司,100192 [3]清华大学医学院生物医学工程系,北京100084

出　　处：《中华核医学与分子影像杂志》2021年第8期454-459,共6页Chinese Journal of Nuclear Medicine and Molecular Imaging

基　　金：国家自然科学基金(82071967);中国医学科学院医学与健康科技创新工程(CAMS-2018-I2M-3-001);清华大学自主科研计划(20191080605)。

摘　　要：目的:探讨在18F-脱氧葡萄糖(FDG)PET/CT图像中采用不同方法勾画胰腺导管腺癌(PDAC)肿瘤区域对使用影像组学特征预测病理分化程度的影响。方法:回顾性分析2010年9月至2016年1月间于北京协和医院经病理证实的72例PDAC患者(男46例、女26例,年龄:25~87岁)的术前18F-FDG PET/CT图像及病理资料。根据PDAC病理分化程度将患者分为高分化和非高分化组。入组患者按3∶1的比例随机划分至训练集和验证集。所有病例由2位医师手动勾画感兴趣区(ROI;记为ROI_M1和ROI_M2),再分别基于标准摄取值(SUV)梯度(记为ROI_G)和40%最大SUV(SUV max)阈值(记为ROI_S)半自动勾画ROI。计算并比较4种勾画结果的体积、戴斯相似性系数(DSC)。从PET/CT原始和预处理图像中提取形状、一阶、纹理等特征,并以组间相关系数(ICC)评估每个特征在不同勾画结果间的一致性。使用Kruskal-Wallis秩和检验、两独立样本t检验或z检验分析数据。采用受试者工作特征曲线下面积评估模型准确性,并通过交叉验证评估模型泛化能力。结果:训练集共55例患者(高分化14例,非高分化41例);验证集共17例患者(高分化4例,非高分化13例)。20个特征组中共筛选出44个对PDAC分化程度有预测价值的特征。ROI_M1、ROI_M2、ROI_G和ROI_S勾画的轮廓体积分别为10.29(4.01,19.43)、9.34(4.26,17.27)、11.86(5.52,19.74)和15.08(9.62,27.44)cm 3,差异有统计学意义(H=18.641,P<0.05)。手动勾画(ROI_M1和ROI_M2)的肿瘤轮廓重合度和特征一致性均较好[DSC=0.86(0.76,0.90);ICC=0.86(0.74,0.94)]。与手动勾画结果比较,ROI_G的轮廓重合度和特征一致性较好[DSC:0.86(0.75,0.91)、0.91(0.85,0.96);ICC:0.87(0.72,0.94)、0.94(0.88,0.98)];ROI_M1与ROI_G的预测模型准确性和泛化能力差异无统计学意义(z=1.052,t=0.712,均P>0.05);ROI_M2的预测模型准确性优于ROI_G(z=3.031,P=0.002),但泛化能力不足(t=3.086,P=0.012)。结论:基于手动勾画构建的预测模型Objective To investigate the segmentation methods of pancreatic ductal adenocarcinoma(PDAC)tumor regions in 18F-fluorodeoxyglucose(FDG)PET/CT images,as well as their impact on radiomic features-based pathological grade prediction.Methods A total of 72 patients(46 males,26 females,age range:25-87 years)with pathologically confirmed PDAC and a preoperative 18F-FDG PET/CT scan in Peking Union Medical College Hospital between September 2010 and January 2016 were enrolled retrospectively.The cohort of patients was classified as well differentiated group and non-well differentiated group based on the pathological grade of PDAC,and patients were divided into training set and validation set in the ratio of 3∶1 randomly.Two physicians performed manual contours in the tumor region(referred as region of interest(ROI)_M1 and ROI_M2)and semi-automatic ROIs based on standardized uptake value(SUV)gradient edge search(referred as ROI_G)and 40%threshold applied to the maximum SUV(SUVmax;referred as ROI_S)were drawn.The four types of segmentation results were compared in terms of volume and Dice similarity coefficient(DSC).Shape,first-order,and texture features were extracted from PET/CT original and preprocessed images,and the interclass correlation coefficient(ICC)was used to assess each feature′s consistency across all segmentations.Kruskal-Wallis rank sum test,independent-sample t test or z test were used to analyze the data.The area under the receiver operating characteristic curve was used to assess model accuracy,and cross validation was used to assess generalization ability.Results There were 55 patients in the training set(14 well differentiated cases and 41 non-well differentiated cases)and 17 patients in the validation set(4 well differentiated cases and 13 non-well differentiated cases).A total of 44 selected features were predictive of the pathological grade of PDAC among 20 feature groups.There was significant difference among the volumes of ROI_M1,ROI_M2,ROI_G and ROI_S(10.29(4.01,19.43),9.34(4.26,17.27),11.86(5

关 键 词：癌 胰腺管 图像处理 计算机辅助 正电子发射断层显像术 体层摄影术 X线计算机 脱氧葡萄糖 预测 

分 类 号：R730.44[医药卫生—肿瘤] R735.9[医药卫生—临床医学]