摘要
目的探讨超声影像组学对胰腺神经内分泌肿瘤(pNEN)肝转移的预测价值。方法回顾性分析2012年1月至2022年6月天津医科大学肿瘤医院经病理证实的269例pNEN患者的临床、病理及超声资料,其中肝转移94例,无肝转移175例。应用ITKSNAP软件在肿瘤最大径切面勾画感兴趣区(ROI),使用Pyradiomics提取影像组学特征。保留组内相关系数>0.90的组学特征,采用最大相关最小冗余(MRMR)筛选最优特征。将数据集按7∶3的比例随机分为训练集和验证集,应用随机森林算法(Rfs)进行pNEN肝转移的预测,共构建临床超声模型、影像组学模型、临床超声与组学特征结合的综合模型共三个模型。通过ROC曲线分析不同模型对pNEN肝转移的预测性能,通过Delong检验对不同模型的预测性能进行比较。结果从ROI中共提取874个特征,经观察者内和观察者间相关性分级及特征选择,保留12个高鲁棒性的影像组学特征用于构建模型。影像组学模型、临床超声模型和综合模型预测NEN患者肝转移的曲线下面积(AUC)、敏感性、特异性及准确性分别为0.800、0.574、0.789、0.714,0.780、0.596、0.874、0.777,0.890、0.694、0.874、0.810。Delong检验显示,综合模型AUC优于影像组学模型(Z=3.845,P=0.00012)及临床超声模型(Z=3.506,P=0.00045),具有最佳预测效能。结论基于超声的影像组学模型具有较好地预测pNEN肝转移的性能,联合临床超声特征和影像组学特征的综合模型可进一步提高模型的预测性能。
Objective To explore the predictive value of ultrasound-based radiomics for liver metastasis in pancreatic neuroendocrine tumors(pNEN).Methods A retrospective analysis was conducted on clinical,pathological,and ultrasound data of 269 pNEN patients confirmed by pathology at Tianjin Medical University Cancer Institute and Hospital from January 2012 to June 2022,including 94 patients with liver metastasis and 175 without liver metastasis.The regions of interest(ROI)were delineated on the maximum diameter section of the tumor using ITKSNAP software,and radiomics features were extracted using Pyradiomics.Radiomics features with an intra-group correlation coefficient greater than 0.90 were retained,and the optimal features were selected using the maximum relevance minimum redundancy(MRMR)algorithm.The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3,and the random forest algorithm(Rfs)was used to predict pNEN liver metastasis.Three models were constructed,including the clinical ultrasound model,the radiomics model,and the comprehensive model that combined clinical ultrasound and radiomics features.The predictive performance of different models for pNEN liver metastasis was analyzed using the ROC curve,and the predictive performance of different models was compared using the Delong test.Results A total of 874 features were extracted from the ROI,and 12 highly robust radiomics features were retained for model construction based on inter-and intra-observer correlation grading and feature selection.The area under curve(AUC),sensitivity,specificity,and accuracy of the radiomics model,the clinical ultrasound model,and the comprehensive model for predicting liver metastasis in pNEN patients were 0.800,0.574,0.789,0.714;0.780,0.596,0.874,0.777;and 0.890,0.694,0.874,0.810,respectively.The Delong test showed that the comprehensive model had the best predictive performance,with an AUC superior to that of radiomics model(Z=3.845,P=0.00012)and clinical ultrasound model(Z=3.506,P=0.00045).Con
作者
赵利辉
张岱
穆洁
毛怡然
杨凡
侯文静
王子阳
魏玺
王海玲
Zhao Lihui;Zhang Dai;Mu Jie;Mao Yiran;Yang Fan;Hou Wenjing;Wang Ziyang;Wei Xi;Wang Hailing(Department of Ultrasound Diagnosis and Treatment,Tianjin Medical University Cancer Institute&Hospital,National Clinical Research Center for Cancer,Tianjin Key Laboratory of Cancer Prevention and Therapy,Tianjin′s Clinical Research Center for Cancer,Tianjin 300060,China;Department of Nuclear Medicine,Tianjin Cancer Hospital Airport Hospital,National Clinical Research Center for Cancer,Tianjin 300308,China)
出处
《中华超声影像学杂志》
CSCD
北大核心
2023年第8期685-691,共7页
Chinese Journal of Ultrasonography
基金
国家自然科学基金面上项目(82272008)
天津市医学重点学科(专科)建设项目(TJYXZDXK-009A)。
