基于术前超声、炎症指标及超声影像组学联合模型预测乳腺癌腋窝淋巴结转移  

Combined models based on preoperative ultrasound,inflammatory indicators and ultrasound radiomics for predicting axillary lymph node metastasis of breast cancer

作　　者：孙婧 杨瑞敏[2] 王聪 张月[4] 罗兵[2] SUN Jing;YANG Ruimin;WANG Cong;ZHANG Yue;LUO Bing(Graduate School,Hebei North University,Zhangjiakou 075000,Hebei,China;Department of Ultrasound,The First Affiliated Hospital of Hebei North University,Zhangjiakou 075000,Hebei,China;Department of Radiotherapy,The First Affiliated Hospital of Hebei North University,Zhangjiakou 075000,Hebei,China;Department of Breast Surgery,The First Affiliated Hospital of Hebei North University,Zhangjiakou 075000,Hebei,China)

机构地区：[1]河北北方学院研究生学院,河北张家口075000 [2]河北北方学院附属第一医院超声医学科,河北张家口075000 [3]河北北方学院附属第一医院放射治疗科,河北张家口075000 [4]河北北方学院附属第一医院乳腺外科,河北张家口075000

出　　处：《山东大学学报(医学版)》2025年第1期73-80,共8页Journal of Shandong University:Health Sciences

基　　金：河北省医学科学研究课题计划项目(No.20220595)。

摘　　要：目的 探讨基于术前超声特征、炎症指标及超声影像组学特征构建的模型预测乳腺癌腋窝淋巴结(axillary lymph node, ALN)转移的价值。方法 回顾性分析175例乳腺癌患者的乳腺超声图像和临床资料,使用3D Slicer软件勾画感兴趣区并提取影像组学特征,运用组间相关系数、皮尔森相关系数和递归特征消除法筛选特征,计算影像组学评分(radiomics score, Radscore),构建影像组学模型。通过单因素、多因素逻辑回归筛选临床危险因素构建临床模型,加入Radscore构建联合预测模型。使用受试者工作特征(receiver operating characteristic, ROC)曲线、校准曲线及决策曲线分析评估各模型的预测效能和临床价值。结果 18个影像组学特征被纳入影像组学模型,肿瘤大小、超声ALN状态和血小板/淋巴细胞比值(platelet to lymphocyte ratio, PLR)被纳入临床模型,肿瘤大小、超声ALN状态、PLR与Radscore被纳入联合预测模型。联合预测模型的预测效能最高,在训练集和验证集的曲线下面积(area under the curve, AUC)分别为0.935、0.858。结论 基于肿瘤大小、超声ALN状态、炎症指标PLR及超声影像组学构建的联合预测模型能有效预测乳腺癌患者ALN转移。Objective To investigate the value of models based on preoperative ultrasound characteristics,inflammatory indicators and ultrasound radiomics features in predicting axillary lymph node(ALN)metastasis of breast cancer.Methods The breast ultrasound images and clinical data of 175 breast cancer patients were retrospectively analyzed.The 3D Slicer software was used to outline the region of interest and extract the radiomics features.The interclass correlation coefficient,Pearson correlation coefficients and recursive feature elimination were used to select the features.After the radiomics score(Radscore)was calculated,the radiomics model was constructed.The clinical model was constructed by screening clinical risk factors through univariate and multivariate Logistic regression,and then the Radscore was added to construct a combined prediction model.The predictive efficacy and clinical value of the models were assessed with the receiver operating characteristic (ROC) curve, calibration curve, and decision curve. Results Eighteen radiomicsfeatures were included in the radiomics model, and tumor size, ultrasound ALN status and platelet to lymphocyteratio (PLR) were included in the clinical model. The tumor size, ultrasound ALN status, PLR and Radscore wereincluded in the combined prediction model. The combined prediction model had the highest prediction efficacy. In thetraining and validation sets, the area under the curve (AUC) of the combined prediction model were 0.935 and 0.858,respectively. Conclusion The combined prediction model based on tumor size, ultrasound ALN status, inflammatoryindicator PLR and ultrasound radiomics is effective in predicting ALN metastasis in breast cancer patients.

关 键 词：乳腺癌 腋窝淋巴结转移 超声检查 影像组学 炎症指标 

分 类 号：R445.1[医药卫生—影像医学与核医学] R737.9[医药卫生—诊断学]