基于CT纹理特征的联合模型在预测高危胃肠间质瘤肝转移中的价值

目的 探讨基于CT纹理特征的联合模型在高危胃肠间质瘤(GISTs)肝转移中的预测价值。方法 回顾性分析2015年1月至2020年12月经病理证实的高危GISTs 204例,其中中国科学院大学附属肿瘤医院153例,绍兴市中心医院医共体总院51例。按照7∶3比例随机分为训练集(142例)和测试集(62例)。根据手术或穿刺病理结果是否有肝脏转移分为肝转移组(76例)和无转移组(128例)。采用ITK-SNAP软件勾画高危GISTs三维容积感兴趣区,通过A-K软件提取纹理特征。采用最小绝对收缩与选择算子(LASSO)筛选出有效特征,建立纹理特征标签。采用Logistic回归,构建临床病理模型、CT纹理特征模型、临床病理联合CT纹理特征模型。采用受试者工作特征曲线(ROC)、校正曲线分析评估模型对高危GISTs肝脏转移的预测效能。采用Delong检验验证曲线下面积(AUC)是否有统计学差异。结果 高危GISTs肝转移组与无转移组的体重指数、肿瘤大小、Ki-67、发生部位、腹部包块、消化道出血、CA125水平差异有统计学意义(P均<0.05)。从CT图像中共提取107个纹理特征,使用LASSO算法降维后筛选出13个平扫、7个增强图像的纹理特征。通过多因素Logistic回归构建预测模型,其中,临床病理模型的AUC在训练集和测试集中为0.870、0.855;CT平扫和增强纹理特征模型的AUC在训练集和测试集中分别为0.918、0.836和0.920、0.846;临床病理联合CT平扫纹理特征模型的AUC在训练集和测试集中为0.930、0.889。经Delong检验,CT平扫与CT增强纹理模型间的AUC差异无统计学意义(P=0.762);临床病理联合CT纹理特征模型与临床病理模型、CT纹理特征模型间的AUC差异有统计学意义(P=0.001,P=0.023)。结论 CT平扫纹理特征可无创预测高危GISTs肝转移的风险,且临床病理联合CT纹理特征模型的预测效能更优。

Performance of the Combined Model Based on Both Clinicopathological and CT Texture Features in Predicting Liver Metastasis of High-risk Gastrointestinal Stromal Tumors

Objective To investigate the performance of the combined model based on both clinicopathological features and CT texture features in predicting liver metastasis of high-risk gastrointestinal stromal tumors(GISTs). Methods The high-risk GISTs confirmed by pathology from January 2015 to December 2020 were analyzed retrospectively,including 153 cases from the Cancer Hospital of the University of Chinese Academy of Sciences and 51 cases from the Shaoxing Central Hospital.The cases were randomly assigned into a training set(n=142)and a test set(n=62)at a ratio of 7∶3.According to the results of operation or puncture,they were classified into a liver metastasis group(76 cases)and a non-metastasis group(128 cases).ITK-SNAP was employed to delineate the volume of interest of the stromal tumors.Least absolute shrinkage and selection operator(LASSO)was employed to screen out the effective features.Multivariate logistic regression was adopted to construct the models based on clinicopathological features,texture features extracted from CT scans,and the both(combined model),respectively.Receiver operating characteristic(ROC)curve and calibration curve were established to evaluate the predictive performance of the models.The area under the curve(AUC)was compared by Delong test. Results Body mass index(BMI),tumor size,Ki-67,tumor occurrence site,abdominal mass,gastrointestinal bleeding,and CA125 level showed statistical differences between groups(all P<0.05).A total of 107 texture features were extracted from CT images,from which 13 and 7 texture features were selected by LASSO from CT plain scans and CT enhanced scans,respectively.The AUC of the prediction with the training set and the test set respectively was 0.870 and 0.855 for the model based on clinicopathological features,0.918 and 0.836 for the model based on texture features extracted from CT plain scans,0.920 and 0.846 for the model based on texture features extracted from CT enhanced scans,and 0.930 and 0.889 for the combined model based on both clinicopathological features and texture features extracted from CT plain scans.Delong test demonstrated no significant difference in AUC between the models based on the texture features extracted from CT plain scans and CT enhanced scans(P=0.762),whereas the AUC of the combined model was significantly different from that of the clinicopathological feature-based model and texture feature-based model(P=0.001 and P=0.023,respectively). Conclusion Texture features extracted from CT plain scans can predict the liver metastasis of high-risk GISTs,and the model established with clinicopathological features combined with CT texture features has best prediction performance.