基于钼靶影像组学列线图预测乳腺癌Her-2表达状态  

Prediction of Her-2 expression status in breast cancer based on mammography radiomics nomogram

作　　者：王玲玲 李程辉 杨丽 汤晓敏[1,2] 朱芸 谢宗玉[1,2] 赵楠楠 WANG Ling-ling;LI Cheng-hui;YANG Li;TANG Xiao-min;ZHU Yun;XIE Zong-yu;ZHAO Nan-nan(School of Medical Imaging,Bengbu Medical College,Bengbu Anhui 233030;Department of Radiology,The First Affiliated Hospital of Bengbu Medical College,Bengbu Anhui 233004,China)

机构地区：[1]蚌埠医学院医学影像学院,安徽蚌埠233030 [2]蚌埠医学院第一附属医院放射科,安徽蚌埠233004

出　　处：《蚌埠医学院学报》2023年第10期1421-1426,共6页Journal of Bengbu Medical College

基　　金：蚌埠医学院自然科学研究重点项目(2020byzd012);安徽省大学生创新创业训练计划项目(S202110367027);安徽省高等学校自然科学研究项目(2022AH051473,2023AH051947)。

摘　　要：目的:探讨基于钼靶影像组学列线图在术前预测乳腺癌Her-2表达状态的应用价值。方法:分析手术或穿刺前行乳腺钼靶检查的262例女性浸润性导管癌(IDC)病人。按照7∶3比例随机分为训练集183例和测试集79例。利用钼靶图像手动勾画感兴趣区(ROI),通过最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归提取影像组学特征,通过统计和LASSO机器学习方法降维,保留纳入模型的最优预测特征,采用logistic回归作为分类器,建立影像组学模型;结合影像资料,通过单-多因素logistic回归,筛选独立危险因素建立影像特征模型;将影像组学特征结合独立危险因素建立影像组学列线图模型。采用受试者操作特征(ROC)曲线分析各模型的预测效能并计算曲线下面积(AUC),并绘制校准曲线及决策曲线评估其效能。结果:列线图模型的预测效能最佳,训练集敏感度84.62%,特异度84.75%,AUC值为0.920,测试集敏感度84.00%,特异度83.33%,AUC值为0.916。校准曲线中列线图模型的预测曲线与理想曲线一致性较好,决策曲线有良好的净收益。结论:钼靶影像组学列线图可以作为术前评估乳腺癌病人Her-2状态的有效工具。Objective:To explore the application value of mammography radiomics nomogram in predicting the expression status of breast cancer Her-2 before surgery.Methods:A retrospective analysis was performed for 262 women with invasive ductal carcinoma(IDC)who underwent a mammogram before surgery or puncture.According to the 7∶3 ratio,183 cases were randomly divided into training set,and 79 cases into test set.Region of interest(ROI)was manually delineated by mammogram image,radiomics features were extracted by least absolute shrinkage and selection operator(LASSO)regression,dimensionality reduction was retained through statistical and LASSO machine learning methods,and logistic regression was used as a classifier to estabish radiomics models;combined with image data,single-multivariate logistic regression was used to screen independent risk factors to establish image feature models.The radiomics nomogram model was established by combining the radiomics features with independent risk factors.The ROC curve was used to analyze the predictive performance of each model,calculate the area under the curve(AUC),and draw the calibration curve and decision curve to evaluate its efficiency.Results:The nomogram model had the best prediction performance,with the training set sensitivity of 84.62%and specificity of 84.75%,the AUC value of 0.920,the sensitivity of the test set of 84.00%,the specificity of 83.33%,and the AUC value of 0.916.In the calibration curve,the prediction curve of the nomogram model was in good agreement with the ideal curve,and the decision curve had a good net benefit.Conclusions:Mammograms can be a useful tool for preoperative assessment of Her-2 status in breast cancer patients.

关 键 词：乳腺肿瘤 钼靶 HER-2表达 影像组学 列线图 

分 类 号：R737.9[医药卫生—肿瘤]