摘要
目的:探讨基于增强CT影像组学的机器学习(machine learning,ML)模型鉴别低风险胃间质瘤(gastric stromal tumor,GST)与良性胃黏膜下肿瘤(gastric submucosal tumor,GSMT)的价值。方法:回顾性分析2013年1月至2022年3月皖南医学院弋矶山医院84例低风险GST及51例良性GSMT患者的临床及影像资料。将患者随机分为训练集(n=94)和测试集(n=41)。基于增强CT静脉期利用ITK-SNAP软件分割图像,AK软件提取影像组学特征,mRMR、Spearman秩相关及LASSO回归对特征降维,建立影像组学标签评分(Radscore)。采用单因素及多因素Logistic回归筛选独立危险因素,使用支持向量机(suppor vector machine,SVM)建立预测模型,用测试集检测模型的泛化能力,受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under curve,AUC)评估模型效能。结果:多因素分析年龄、形态、生长部位、长径/短径(long diameter,LD/short diameter,SD)及Radscore为独立危险因素,SVM建立预测模型的AUC训练集为0.933、测试集为0.913。结论:基于增强CT影像组学的SVM算法构建的模型能够有效地鉴别低风险GST与良性GSMT,且模型具有较好的泛化能力。
Objective:To explore the clinical application value of a machine learning(ML)model based on enhanced computed tomography(CT)radiomics in differentiating low-risk gastric stromal tumors(GST)from benign gastric submucosal tumors(GSMT).Methods:The clinical and imaging data for 84 cases of low-risk GST and 51 cases of benign GSMT from Yijishan Hospital Affiliated to Wannan Medical College were retrospectively analyzed.Patients were randomly assigned into a training set(n=94)and a test set(n=41).Based on enhanced CT vein phase,ITK-SNAP software was used to segment the images;AK software was used to extract the image group characteristics;mRMR,Spearman rank correlation,and LASSO regression were used to reduce the dimensions of the features;and the image group label score(Radscore)was established.Single-and multi-factor Logistic regression was used to screen independent risk factors.Support vector machine(SVM)was used to establish a prediction model,and the test set was used to test the generalization ability of the model.The area under the receiver operating characteristic(ROC)curve(AUC)of the subjects was used to evaluate the effectiveness of the model.Results:Multivariate analysis showed that age,shape,location,long diameter/short diameter(LD/SD),and Radscore were independent risk factors.The AUC for training set and test set of the prediction model established by SVM were 0.933 and 0.913,respectively.Conclusions:The model,based on SVM algorithm of enhanced CT radiomics,was able to effectively identify low-risk GST and benign GSMT with good generalization ability.
作者
范莉芳
赵劲松
吴树剑
徐晓燕
徐争元
傅雨晨
Lifang Fan;Jinsong Zhao;Shujian Wu;Xiaoyan Xu;Zhengyuan Xu;Yuchen Fu(Department of Medical Imaging,Wannan Medical College,Wuhu 241002,China;Imaging Center,Yijishan Hospital Affiliated to Wannan Medical College,Wuhu 241001,China)
出处
《中国肿瘤临床》
CAS
CSCD
北大核心
2023年第8期411-417,共7页
Chinese Journal of Clinical Oncology
关键词
影像组学
机器学习
鉴别诊断
低风险胃间质瘤
radiomics
machine learning(ML)
differential diagnosis
low-risk gastric stromal tumors
