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中华临床医师杂志(电子版)

中华临床医师杂志(电子版) ›› 2024, Vol. 18 ›› Issue (04) : 355 -362. doi: 10.3877/cma.j.issn.1674-0785.2024.04.004

临床研究

基于自动乳腺超声的列线图模型早期预测HER-2阳性乳腺癌新辅助化疗病理完全缓解的临床价值
赵阳1, 肖迎聪2, 巨艳3, 党晓智3, 蔡林利2, 薛文欣3, 李洋3, 肖瑶3, 郭妤绮3, 宋宏萍3,†()   
  1. 1. 710032　西安，空军军医大学第一附属医院（西京医院）超声医学科;712046　陕西　咸阳，陕西中医药大学医学技术学院
    2. 712046　陕西　咸阳，陕西中医药大学医学技术学院
    3. 710032　西安，空军军医大学第一附属医院（西京医院）超声医学科
  • 收稿日期:2024-01-17 出版日期:2024-04-15
  • 通信作者: 宋宏萍
  • 基金资助:
    国家自然科学基金面上项目(82071934); 陕西省科技计划项目国合重点项目(2020KWZ-022); 陕西省高等教育教学改革研究重点项目(21JZ009); 空军军医大学临床研究项目(2021LC2210)

Clinical value of a nomogram model based on automated breast ultrasound in early prediction of pathological complete response to neoadjuvant chemotherapy in HER-2 positive breast cancer

Yang Zhao1, Yingcong Xiao2, Yan Ju3, Xiaozhi Dang3, Linli Cai2, Wenxin Xue3, Yang Li3, Yao Xiao3, Yuqi Guo3, Hongping Song3,†()   

  1. 1. Department of Ultrasound Medicine, The First Affiliated Hospital of Air Force Military Medical University (Xijing Hospital), Xi'an 710032, China;Shaanxi University of Traditional Chinese Medicine, Xianyang 712046, China
    2. Shaanxi University of Traditional Chinese Medicine, Xianyang 712046, China
    3. Department of Ultrasound Medicine, The First Affiliated Hospital of Air Force Military Medical University (Xijing Hospital), Xi'an 710032, China
  • Received:2024-01-17 Published:2024-04-15
  • Corresponding author: Hongping Song
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引用本文：

赵阳, 肖迎聪, 巨艳, 党晓智, 蔡林利, 薛文欣, 李洋, 肖瑶, 郭妤绮, 宋宏萍. 基于自动乳腺超声的列线图模型早期预测HER-2阳性乳腺癌新辅助化疗病理完全缓解的临床价值[J]. 中华临床医师杂志(电子版), 2024, 18(04): 355-362.

Yang Zhao, Yingcong Xiao, Yan Ju, Xiaozhi Dang, Linli Cai, Wenxin Xue, Yang Li, Yao Xiao, Yuqi Guo, Hongping Song. Clinical value of a nomogram model based on automated breast ultrasound in early prediction of pathological complete response to neoadjuvant chemotherapy in HER-2 positive breast cancer[J]. Chinese Journal of Clinicians(Electronic Edition), 2024, 18(04): 355-362.

目的

探讨自动乳腺超声（ABUS）早期预测HER-2阳性乳腺癌患者新辅助化疗（NAC）后获得病理完全缓解（pCR）的临床价值。

方法

回顾性分析2019年3月至2023年5月于空军军医大学附属西京医院乳腺外科收治的248例HER-2阳性女性乳腺癌患者，比较pCR组和非病理完全缓解（npCR）组NAC前各项参数的差异，并行多因素二元Logistic回归分析确定HER-2阳性乳腺癌pCR的独立预测因素，构建基于ABUS特征的列线图预测模型。应用Bootstrap 方法（1000次重抽样）对模型进行内部验证；采用受试者工作特征（ROC）曲线评估模型的区分度，校准曲线评估模型的准确性，临床决策曲线（DCA）评价模型的临床获益。

结果

HER-2阳性乳腺癌NAC前2组肿瘤的ER状态、PR状态、分子亚型、皮肤侵犯、后方回声、冠状面汇聚征、冠状面白墙征比较差异有统计学意义（均P<0.05）。行二元Logistic回归分析显示分子亚型、皮肤侵犯、冠状面汇聚征、冠状面白墙征是HER-2阳性乳腺癌pCR的独立预测因素（均P<0.05）。基于这些变量构建列线图模型，其ROC曲线下面积为0.805（95%CI：0.751~0.859）。Hosmer-Lemeshow检验表明模型良好的拟合度（χ2=6.597，P=0.360）。采用Bootstrap法迭代1000次进行内部验证，平均AUC为0.806（95%CI：0.742~0.855），表明模型稳定性良好，校准曲线表明列线图模型的预测概率与实际概率一致性好，决策曲线（DCA）线显示模型的临床获益及应用价值较高。

结论

基于NAC前肿瘤ABUS特征的列线图模型可以在一定程度上早期准确预测HER-2阳性乳腺癌NAC后pCR，可为乳腺癌患者临床治疗方案的制定提供超声影像依据。

关键词: HER-2阳性乳腺癌, 自动乳腺超声, 病理完全缓解, 新辅助化疗
Objective

To assess the clinical usefulness of automated breast ultrasound (ABUS) in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in HER-2 positive breast cancer patients.

Methods

A retrospective analysis was performed on 248 HER-2 positive female breast cancer patients admitted to the Department of Breast Surgery, Xijing Hospital Affiliated to Air Force Military Medical University from March 2019 to May 2023. The differences in parameters before NAC were compared between patients with pCR and non-pCR (npCR) patients. Multivariate binary Logistic regression analysis was performed to identify the independent predictors of pCR in HER-2 positive breast cancer. Additionally, a nomogram prediction model was developed based on features obtained from ABUS. The Bootstrap method (1000 times of sampling) was used to verify the model internally. Receiver operating characteristic (ROC) curve analysis was employed for assessing the discriminative performance of the model, and decision curve analysis (DCA) was used to evaluate the clinical benefit and application value of the model.

Results

There were significant differences in ER status, PR status, molecular subtype, skin invasion, posterior echo, retraction phenomenon in the coronal plane, and white wall sign in the coronal plane between the the pCR and npCR groups before NAC (P<0.05 for all). Binary Logistic regression analysis showed that molecular subtype, skin invasion, retraction phenomenon in the coronal plane, and white wall sign in the coronal plane were independent predictors of pCR in HER-2 positive breast cancer (P<0.05 for all). A nomogram model was constructed based on these variables, and the area under the ROC curve of this model for predicting pCR in HER-2 positive breast cancer was 0.805 (95% confidence interval [CI]: 0.751~0.859). The Hosmer-Lemeshow test showed that the model had a good fit (χ2=6.597, P=0.360). The calibration curve demonstrated an excellent correspondence between the predicted and observed probabilities of the joint model, thereby indicating its high accuracy and reliability. The Bootstrap method was used to iterate for 1000 times for internal verification, and the calculated average AUC was 0.806 (95%CI: 0.742-0.855), indicating that the model was stable. The DCA showed that the clinical benefit and application value of the model were high.

Conclusion

The nomogram model based on the ABUS features of tumors before NAC can accurately predict the pCR to NAC in HER-2 positive breast cancer to a certain extent, thus providing an ultrasound imaging basis for the formulation of clinical treatment plans for breast cancer patients.

Key words: HER-2 positive breast cancer, Neoadjuvant chemotherapy, Automated breast ultrasound, Pathological complete remission
表1 npCR组与pCR组临床病理特征比较
临床病理特征 npCR（n=115） pCR（n=133） P值
年龄（岁） 51.00（39.00，56.00） 50.00（45.00，58.00） 0.391
绝经状态[例（%）] 0.388
未绝经 60（52.2） 61（45.9）
已绝经 55（47.8） 72（54.1）
ER状态[例（%）] <0.001
阴性 26（22.6） 74（55.6）
阳性 89（77.4） 59（44.4）
PR状态[例（%）] <0.001
阴性 44（38.2） 95（71.4）
阳性 71（61.8） 38（28.6）
Ki-67状态[例（%）] 0.085
≤14% 14（12.2） 7（5.3）
>14% 101（87.8） 126（94.7）
分子分型[例（%）] <0.001
HR（-） 25（21.7） 76（57.1）
HR（+） 90（78.3） 57（42.9）
表2 npCR组与pCR组ABUS特征比较
ABUS特征 npCR（n=115） pCR（n=133） P值
肿瘤最长经（cm） 3.4（2.6，4.3） 3.1（2.4，3.7） 0.063
皮肤侵犯[例（%）] 0.004
否 97（84.3） 127（95.5）
是 18（15.7） 6（4.5）
形态[例（%）] 0.292
不规则 113（98.2） 127（95.5）
规则 2（1.8） 6（4.5）
生长方向[例（%）] 0.578
不平行 8（7.0） 6（4.5）
平行 107（93.0） 127（95.5）
内部回声[例（%）] 0.883
低回声 113（98.3） 131（98.5）
混合回声 2（1.7） 2（1.5）
后方回声[例（%）] 0.005
无改变 91（79.1） 81（60.9）
增强 15（13.1） 39（29.3）
衰减 9（7.8） 13（9.8）
钙化[例（%）] 0.363
无 42（36.5） 57（42.9）
有 73（63.5） 76（57.1）
冠状面汇聚征[例（%）] <0.001
无 75（65.2） 113（85.0）
有 40（34.8） 20（15.0）
冠状面跳跃征[例（%）] 0.771
无 97（84.3） 115（86.5）
有 18（15.7） 18（13.5）
冠状面白墙征[例（%）] <0.001
无 107（93.0） 77（57.9）
有 8（7.0） 56（42.1）
图1 HER-2阳性乳腺癌患者新辅助化疗pCR独立影响因素森林图 注：HER-2为人表皮生长因子受体-2；pCR为病理完全缓解
图2 列线图模型预测pCR的ROC曲线 注：pCR为病理完全缓解
图3 基于分子亚型、皮肤侵犯、冠状面汇聚征、冠状面白墙征构建的列线图 注：分子亚型0、1分别代表HR阴性、HR阳性；皮肤侵犯、冠状面汇聚征、冠状面白墙征的0、1分别代表无、有
图4 列线图模型的校准曲线
图5 列线图模型的DCA曲线 注：DCA为临床决策曲线
图6 患者55岁，女性，浸润性乳腺癌（HR阴性），完全NAC后pCR。图a为ABUS图像显示冠状面白墙象，无冠状面汇聚征和皮肤侵犯；图b为列线图计算总得分为254分（51+70+33+100=254），对应于NAC后pCR率为93% 注：HR为激素受体；NAC为新辅助化疗；pCR为病理完全和缓解；ABUS为自动乳腺超声
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