基于CT影像组学特征的机器学习模型预测胃肠间质瘤的危险度  

Machine learning models for predicting the risk stratification of gastrointestinal stromal tumor based on the radiomic features of CT

作　　者：张晨辰 印宏坤 余蕊 鲍奕清 赵硕 范国华[1] ZHANG Chenchen;YIN Hongkun;YU Rui;BAO Yiqing;ZHAO Shuo;FAN Guohua(Department of Radiology,the Second Affiliated Hospital of Soochow University,Suzhou,Jiangsu Province 215004,China;Infervision Medical Technology Co.,Ltd.,Beijing 100000,China)

机构地区：[1]苏州大学附属第二医院放射科,江苏苏州215004 [2]推想医疗科技股份有限公司,北京100000

出　　处：《实用放射学杂志》2024年第7期1111-1115,共5页Journal of Practical Radiology

摘　　要：目的构建基于平扫与增强CT影像组学特征的机器学习模型,评估其对胃肠间质瘤(GIST)危险度的预测价值。方法将182例经病理证实为GIST患者以7︰3的比例随机分为训练集与验证集。于平扫期、动脉期、静脉期勾画感兴趣区体积(VOI)并提取影像组学特征。采用最小绝对收缩和选择算子(LASSO)算法筛选最有价值的影像组学特征。使用逻辑回归(LR)分类器构建单期相和多期相联合的预测模型。通过受试者工作特征(ROC)曲线比较不同模型的预测效能。结果平扫期、动脉期、静脉期分别选取了4、3、4个影像组学特征,共构建4个模型。单期相模型中,静脉期有较好的预测效能,训练集和验证集的曲线下面积(AUC)分别为0.932[95%置信区间(CI)0.873~0.969]和0.924(95%CI 0.819~0.979)。联合模型的预测效能有所提高,AUC分别为0.946(95%CI 0.891~0.978)和0.938(95%CI 0.838~0.986)。结论静脉期模型可较准确地预测GIST危险度,联合平扫期和动脉期可提高预测效能。Objective To construct the machine learning models based on the radiomic features of non-contrast and enhanced CT and to evaluate the predictive value in the risk stratification of gastrointestinal stromal tumor(GIST).Methods A total of 182 patients with pathologically confirmed GIST were randomly divided into a training set and a validation set at a ratio of 7︰3.The volume of interest(VOI)was outlined in the non-contrast phase,arterial phase and venous phase,and its radiomic features were extracted.The most valuable radiomic features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm.The logistic regression(LR)classifier was used to construct the prediction models based on single-phase or multi-phase images.The predictive efficacy of the different models was compared by using receiver operating characteristic(ROC)curves.Results Four,three,and four radiomic features were selected in the non-contrast phase,arterial phase and venous phase,and 4 models were constructed in total.Among the single-phase models,the venous phase had better predictive efficacy,with the area under the curve(AUC)of 0.932[95%confidence interval(CI)0.873-0.969]and 0.924(95%CI 0.819-0.979)in the training and validation sets.The predictive efficacy of the combined model was improved,with the AUC of 0.946(95%CI 0.891-0.978)and 0.938(95%CI 0.838-0.986).Conclusion The venous phase model can predict the risk stratification of GIST accurately,and the prediction efficacy can be improved by combining the noncontrast and arterial phases.

关 键 词：胃肠间质瘤 影像组学 机器学习 危险分级 

分 类 号：R735.2[医药卫生—肿瘤] R445[医药卫生—临床医学] TP181[自动化与计算机技术—控制理论与控制工程]