病理参数联合ABVS影像特征列线图预测乳腺癌Luminal分型  

Prediction of Luminal Classification of Breast Cancer by Combining Pathological Parameters with ABVS Imaging Feature Nomogram

作　　者：范莉芳 张超学[2] 黄磊 吴艺敏 吴树剑 朱向明[4] Fan Lifang;Zhang Chaoxue;Huang Lei;Wu Yimin;Wu Shujian;Zhu Xiangming(School of Medical Imageology,Wannan Medical College,Wuhu,Anhui 241002,China;Department of Ultrasound,The First Affiliated Hospital of Anhui Medical University,Hefei 230032,China;Department of Ultrasound,The Second People’s Hospital,Wuhu,Anhui 241000,China;Yijishan Hospital of Wannan Medical College,Wuhu,Anhui 241000,China)

机构地区：[1]皖南医学院医学影像学院,安徽省芜湖市241002 [2]安徽医科大学第一附属医院超声科,合肥市230032 [3]芜湖市第二人民医院超声科,安徽省芜湖市241000 [4]皖南医学院弋矶山医院,安徽省芜湖市241000

出　　处：《中国超声医学杂志》2024年第2期153-157,共5页Chinese Journal of Ultrasound in Medicine

基　　金：癌症转化医学安徽省重点实验室开放课题(蚌埠医学院)(No.KFKT202307);安徽省教育厅高校自然科学重点科研项目(No.2023AH051743);安徽省教育厅高校高峰学科建设重大项目(No.GXXK-2020-44)。

摘　　要：目的分析病理参数联合超声自动乳腺全容积扫描(ABVS)影像特征构建的列线图预测乳腺癌Luminal分型的价值。方法回顾性分析212例行超声ABVS检查的Luminal型乳腺癌患者的病理及超声资料,以雌激素受体(ER)阳性、人表皮生长因子受体2(HER-2)阴性、Ki-67<14%及孕激素受体(PR)>20%作为Luminal A型乳腺癌的分型标准,将患者分为Luminal A型(64例)与Luminal B型(148例),按7∶3将患者随机分为训练组(n=148)与验证组(n=64)。单因素与多因素Logistic回归分析筛选预测乳腺癌Luminal分型的独立影响因素,基于独立影响因素构建联合预测模型,并绘制模型的列线图与校准曲线。结果单因素与多因素Logistic回归分析发现脉管侵犯、长径及冠状面汇聚征为预测乳腺癌Luminal分型的独立影响因素,联合模型曲线下面积(AUC)训练组为0.725(95%CI:0.639~0.812),验证组为0.777(95%CI:0.640~0.915)。结论病理参数联合超声ABVS影像特征区分乳腺癌Luminal分型有一定价值,列线图能够将预测结果可视化。Objective To analyze the value of nomogram based on pathological parameters combined with automatic breast volume scanning(ABVS)imaging features in predicting Luminal classification of breast cancer.Methods The pathological and ultrasound data of 212 patients with Luminal breast cancer who underwent ABVS were retrospectively analyzed.The patients were divided into Luminal A type(64 cases)and Luminal B type(148 cases)based on the classification criteria:estrogen receptor(ER)positive,human epidermal growth factor receptor 2(HER-2)negative,Ki-67<14%,and progesterone receptor(PR)>20%.They were randomly assigned to a training group(n=148)or a validation group(n=64)in a 7∶3 ratio.Univariate and multivariate Logistic regression analysis were employed to identify independent factors predicting Luminal classification of breast cancer.An integrated prediction model was developed based on these factors,and a nomogram and calibration curve were generated for the model.Results The univariate and multivariate logistic regression analysis identified vascular invasion,long diameter,and coronal convergence as independent predictors of Luminal classification in breast cancer.The combined model had an AUC of 0.725(95%CI:0.639-0.812)in the training group and 0.777(95%CI:0.640-0.915)in the validation group.Conclusions The combination of pathological parameters and ultrasound ABVS imaging features has value in distinguishing Luminal classification of breast cancer,and the nomogram can visualize the predicted results.

关 键 词：病理参数 自动乳腺全容积扫描 列线图 乳腺癌 Luminal分型 

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]