切换至 "中华医学电子期刊资源库"

中华乳腺病杂志(电子版) ›› 2026, Vol. 20 ›› Issue (02) : 82 -90. doi: 10.3877/cma.j.issn.1674-0807.2026.02.002

论著

基于多种超声参数构建乳腺癌高负荷腋窝淋巴结转移的预测模型
莫遵玉, 赖莉萍, 李水平(), 葛肖艳   
  1. 364000 龙岩,龙岩市第一医院超声科
  • 收稿日期:2025-03-27 出版日期:2026-04-01
  • 通信作者: 李水平
  • 基金资助:
    福建省自然科学基金联合资助项目(2024J011623)

Prediction model of high load axillary lymph node metastasis in breast cancer based on multiple ultrasound parameters

Zunyu Mo, Liping Lai, Shuiping Li(), Xiaoyan Ge   

  1. Department of Ultrasound, Longyan First Hospital, Longyan 364000, China
  • Received:2025-03-27 Published:2026-04-01
  • Corresponding author: Shuiping Li
引用本文:

莫遵玉, 赖莉萍, 李水平, 葛肖艳. 基于多种超声参数构建乳腺癌高负荷腋窝淋巴结转移的预测模型[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(02): 82-90.

Zunyu Mo, Liping Lai, Shuiping Li, Xiaoyan Ge. Prediction model of high load axillary lymph node metastasis in breast cancer based on multiple ultrasound parameters[J/OL]. Chinese Journal of Breast Disease(Electronic Edition), 2026, 20(02): 82-90.

目的

基于多种超声特征参数构建乳腺癌高负荷腋窝淋巴结转移(ALNM)的预测模型。

方法

根据纳入及排除标准,收集2020年1月至2024年6月在龙岩市第一医院术前行超声检查(常规超声、声辐射力脉冲成像、自动乳腺全容积成像、超声造影)的250例乳腺癌患者临床资料,进行回顾性研究。以7∶3比例分为训练组(n=175)与验证组(n=75)。采用单因素及多因素Logistic回归分析训练组乳腺癌患者高负荷ALNM的危险因素,构建预测模型并绘制列线图,利用受试者操作特征曲线(ROC)与Hosmer-Lemeshow检验评价模型的区分度和校准度,采用临床决策曲线(DCA)评估临床决策效益,通过验证组数据对模型进行验证。

结果

多因素Logistic回归分析显示,病灶边缘剪切波速度最大值(SWVmax)(OR=2.742,95%CI:1.175~6.399)、淋巴管超声造影(LCEUS)增强类型(Ⅲ~Ⅳ型)(OR=11.993,95%CI:4.407~32.632)、静脉超声造影(ICEUS)增强类型(向心型/混合型)(OR=10.424,95%CI:3.583~30.326)、前哨淋巴结(SLN)门结构消失(OR=12.305,95%CI:2.758~54.901)、SLN边缘血流(OR=5.280,95%CI:1.852~15.050)是训练组中高负荷ALNM的影响因素,基于此构建列线图模型,模型公式为Logit(P)=1.009 × 病灶边缘SWVmax+1.664× SLN边缘血流+2.510 × SLN门结构消失+2.344 × ICEUS增强类型(向心型/混合型)+2.484 × LCEUS增强类型(Ⅲ~Ⅳ型)-7.276。训练组中,预测模型的ROC曲线下面积(AUC)为0.920(95%CI:0.878~0.962);Hosmer-Lemeshow检验显示χ2=6.951,P=0.542。验证组预测模型的AUC为0.907(95%CI:0.842~0.973),Hosmer-Lemeshow检验显示χ2=8.965,P=0.345。DCA分析显示,在训练组及验证组中,阈值概率分别为3%~97%、8%~95%时以模型为参考可获得净效益。

结论

基于病灶边缘SWVmax、LCEUS增强类型(Ⅲ~Ⅳ型)、ICEUS增强类型(向心型/混合型)、SLN门结构消失、SLN边缘血流构建的预测模型对乳腺癌患者高负荷ALNM有较好的预测效果。

Objective

To construct a prediction model of high load axillary lymph node metastasis (ALNM) in breast cancer based on multiple ultrasound characteristic parameters.

Methods

According to the inclusion and exclusion criteria, a retrospective study was conducted on the clinical data of 250 patients with breast cancer who underwent preoperative ultrasound examinations (conventional ultrasound, acoustic radiation force impulse imaging, automated breast volume scanner, and contrast-enhanced ultrasound) in the Longyan First Hospital between January 2020 and June 2024. The patients were divided into a training group (n=175) and a validation group (n=75) at a ratio of 7∶3. The risk factors of high load ALNM in breast cancer patients of the training group were analyzed by univariate and multivariate logistic regression analyses. The prediction model was constructed accordingly and the nomogram was drawn. Receiver operating characteristic curve (ROC) and Hosmer-Lemeshow test were used to evaluate the discrimination degree and calibration degree of the model. Clinical decision curve analysis (DCA) was used to evaluate the clinical net benefit. The model was verified by the data of the validation group.

Results

Multivariate logistic regression analysis revealed that lesion edge maximum shear wave velocity (SWVmax) (OR=2.742, 95%CI: 1.175-6.399) , lymphatic contrast-enhanced ultrasound (LCEUS) enhancement type (Ⅲ-Ⅳ type) (OR=11.993, 95%CI: 4.407-32.632) , intravenous contrast-enhanced ultrasound (ICEUS) enhancement type (concentric/mixed type) (OR=10.424, 95%CI: 3.583-30.326) , portal structure disappearance of sentinel lymph node (SLN) (OR=12.305, 95%CI: 2.758-54.901) , and blood flow of SLN edge (OR=5.280, 95%CI: 1.852-15.050) were predictive factors of high load ALNM in the training group. Based on this, a nomogram model was constructed and the model formula was as follow: Logit (P) =1 009×lesion edge SWVmax+1.664×blood flow of SLN edge flow+2.510×SLN portal structure disappearance+2.344×ICEUS enhancement type (concentric/mixed type) +2.484×LCEUS enhancement type (Ⅲ-Ⅳ type) -7.276. In the training group, the area under the ROC curve (AUC) of the prediction model was 0.920 (95%CI: 0.878-0.962), and the Hosmer-Lemeshow test revealed a chi-square value of 6.951 (P=0.542) . In the validation group, the AUC was 0.907 (95%CI: 0.842-0.973), and the Hosmer-Lemeshow test manifested a chi-square value of 8.965 (P=0.345). DCA results showed that when the threshold probabilities were 3%-97% and 8%-95% in the training group and validation group, the model could be used as a reference to obtain net benefits.

Conclusion

The prediction model based on lesion edge SWVmax, LCEUS enhancement type (Ⅲ-Ⅳ type) , ICEUS enhancement type (concentric/mixed type) , SLN portal structure disappearance and blood flow of SLN edge has a high predictive ability for high load ALNM in breast cancer patients.

图1 1例50岁浸润性乳腺癌患者的超声检查结果 A图示左乳内下象限(7点方向)有低回声团块,边界欠清;B图示病灶内可见丰富血流信号(3级);C图示病灶呈红色,VTI硬度评分为5分;D图示自动乳腺全容积成像扫描内下象限(7点方向)可见“汇聚征”;E图示健侧淋巴结,左侧为常规超声图(示淋巴结边界清晰,形状规则),右侧为LCEUS图(示周围有规则的环形增强);F图示患侧淋巴结,左侧为常规超声图(示淋巴结边界模糊,形状不规则),右侧为LCEUS图(示周围有灌注缺陷的不均匀增强)
表1 2名医师超声图像判读一致性检验
表2 训练组与验证组乳腺癌患者的临床病理特征及超声检查特征比较(例)
表3 训练组乳腺癌患者腋窝淋巴结转移负荷的单因素分析(例)
表4 分类变量赋值表
表5 训练组中预测高负荷腋窝淋巴结转移的多因素分析
图2 预测乳腺癌患者高负荷腋窝淋巴结转移的列线图 注:LCEUS为淋巴管超声造影;ICEUS为静脉超声造影;SLN为乳腺腋窝前哨淋巴结;SWV为剪切波速度
图3 预测模型的受试者操作特征曲线 A、B图分别为训练组和验证组 注: 训练组,AUC=0.92, 95%CI: 0.88~0.96;验证组,AUC=0.91, 95%CI: 0.84~0.97
图4 预测模型的校准曲线 A、B图分别为训练组和验证组
图5 预测模型的决策曲线 A、B图分别为训练组和验证组
[1]
Katsura COgunmwonyi IKankam HK,et al. Breast cancer: presentation, investigation and management[J]. Br J Hosp Med(Lond)202283(2):1-7.
[2]
Tinterri CCanavese GGatzemeier W,et al. Sentinel lymph node biopsy versus axillary lymph node dissection in breast cancer patients undergoing mastectomy with one to two metastatic sentinel lymph nodes: sub-analysis of the SINODAR-ONE multicentre randomized clinical trial and reopening of enrolment[J]. Br J Surg2023110(9):1143-1152.
[3]
Magnoni FGalimberti VCorso G,et al. Axillary surgery in breast cancer:an updated historical perspective[J]. Semin Oncol202047(6):341-352.
[4]
Giuliano AEBallman KVMcCall L,et al. Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis:the ACOSOG Z0011(alliance)randomized clinical trial[J]. JAMA2017318(10):918-926.
[5]
张国锋,徐向升,刘蕾,等. 早期浸润性乳腺癌保留乳房患者的腋窝分期研究[J/OL]. 中华乳腺病杂志(电子版)202519(2): 92-96.
[6]
李昕宇,李玉东,刘强. 乳腺癌前哨淋巴结活组织检查的临床应用[J/OL]. 中华乳腺病杂志(电子版)202519(2): 108-112.
[7]
张群,李俊杰. 乳腺癌外科十大热点[J/OL]. 中华乳腺病杂志(电子版)202519(1): 6-11.
[8]
Adler DDCarson PLRubin JM,et al. Doppler ultrasound color flow imaging in the study of breast cancer:preliminary findings[J]. Ultrasound Med Biol199016(6):553-559.
[9]
郭星妍,闫国珍,李爱华,等. 多模态超声对乳腺癌腋窝淋巴结转移的诊断价值研究[J]. 中国超声医学杂志202541(6): 626-630.
[10]
吕菲菲,朱淑庆,崔月,等. 多模态超声成像对乳腺癌前哨淋巴结转移的诊断价值[J]. 中国临床医学202330(4): 669-675.
[11]
王荣,王兴田,胡春梅,等. 声脉冲辐射力成像对乳腺肿块鉴别诊断价值的初步研究[J]. 中华超声影像学杂志201221(2):142-145.
[12]
郑逢洋,黄备建,严丽霞,等. 乳腺癌冠状面汇聚征和生物学行为指标间的相关性研究[J]. 中华超声影像学杂志201625(6):496-501.
[13]
Jin LWang RZhuang L,et al. Evaluation of whole axillary status with lymphatic contrast-enhanced ultrasound in patients with breast cancer[J]. Eur Radiol202232(1):630-638.
[14]
Sidhu PSCantisani VDietrich CF,et al. The EFSUMB guidelines and recommendations for the clinical practice of contrast-enhanced ultrasound(CEUS)in non-hepatic applications:update 2017(long version)[J]. Ultraschall Med201839(2):e2-e44.
[15]
Liang YChen XTong Y,et al. Higher axillary lymph node metastasis burden in breast cancer patients with positive preoperative node biopsy:may not be appropriate to receive sentinel lymph node biopsy in the post-ACOSOG Z0011 trial era[J]. World J Surg Oncol201917(1):37.
[16]
彭娟,刘娜,蒲欢,等. 常规超声联合声脉冲辐射力成像预测新辅助化疗对乳腺癌的效果[J]. 中国介入影像与治疗学202118(7):416-420.
[17]
马兆丽,王爽,史娜,等. 自动乳腺全容积成像结合乳腺影像报告和数据系统量化评分在乳腺癌诊断中的应用[J]. 中国超声医学杂志202440(5):521-524.
[18]
杨雁雯,李伟伟,陶玲玲,等. 小乳腺癌的超声造影特征与病理组织学分级的相关性研究[J]. 中国医学计算机成像杂志202430(5):594-598.
[19]
桑田,任学刚,王烨,等. 超声联合病理参数预测乳腺癌腋窝淋巴结转移的价值[J]. 中华超声影像学杂志202231(8):691-697.
[20]
周菊英,郭强,张子宁,等. 超声造影联合声触诊组织成像量化技术诊断乳腺癌前哨淋巴结的效能[J]. 临床与病理杂志202242(11):2648-2653.
[21]
方静,唐文静,韦玉亚,等. 超声造影及动态增强MRI定量参数与乳腺癌的组织学分级、淋巴结转移及LOXL2、PTK6表达水平的相关性分析[J]. 放射学实践202439(9):1172-1177.
[22]
王美晨,史丽群,李照喜. 基于ABVS联合VTIQ技术构建乳腺癌腋窝淋巴结高转移负荷预测模型的研究[J]. 中国超声医学杂志202238(12):1354-1357.
[1] 卢瑞芳, 林欣欣, 王昱, 许芊芊, 吴少虹, 王伟, 陈立达, 程美清. 2023年美国肝病研究学会肝细胞癌筛查指南的验证研究[J/OL]. 中华医学超声杂志(电子版), 2025, 22(11): 1046-1054.
[2] 陈子为, 邹南鑫, 宋禄达, 姜波, 张奥, 张夕, 李秋洋. 基于超声的Bosniak分级对肾脏囊性病变的诊断价值及一致性分析[J/OL]. 中华医学超声杂志(电子版), 2025, 22(11): 1062-1070.
[3] 中国临床肿瘤学会乳腺癌专家委员会, 中国医师协会肿瘤多学科诊疗专业委员会, 广东省药学会. 乳腺癌内分泌治疗超说明书用药专家共识[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(02): 69-81.
[4] 中华医学会超声医学分会, 中国研究型医院肿瘤介入治疗委员会, 中国妇幼保健协会乳腺保健专业委员会, 中华医学会肿瘤学分会乳腺癌专业委员会, 中国抗癌协会乳腺癌专业委员会, 中国人体健康科技促进会乳腺专业委员会. 无手术适应证乳腺癌消融治疗专家共识(2025版)[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 1-8.
[5] 陈阔, 齐晓伟, 宋达疆, 吕鹏威. 机器人技术在乳腺外科的临床应用[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 9-15.
[6] 伍秋苑, 张敏, 陈芷彦, 程妹, 陈佩贤, 黄慧琦, 杨树青, 叶国麟, 邓裕华, 熊亚明, 金亚彬, 周丹. KIF18A表达影响三阴性乳腺癌细胞恶性生物学行为[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 16-24.
[7] 董晓培, 袁洋, 李健斌, 宋华, 李凡, 郝艺, 边莉, 王涛, 江泽飞, 张少华. 激素受体阳性、HER-2阴性乳腺癌首发单纯骨转移患者不同一线治疗方式的预后分析[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 25-33.
[8] 李荣, 肖正权, 王龙, 张欢. 乳腺癌放射治疗患者放射性皮炎发生率及影响因素的Meta分析[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 34-43.
[9] 邵长华, 刘艳芳, 李恒宇. 产后乳腺癌的临床特征和干预策略[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 44-48.
[10] 栗家兴, 程紫轩, 吕欣悦, 王江芬, 张晶晶, 张亚芬. 肿瘤浸润淋巴细胞在乳腺癌免疫治疗中的作用及其预后预测价值[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 49-54.
[11] 仲洋杨, 邓舒瑶, 李永杰, 李庄. 男性隐匿性乳腺癌一例[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 60-63.
[12] 张玉冬, 鲁磊, 尚宏清, 李伟, 刘湘晨, 王冰涛, 朱莉丽, 付马墨阳, 许宸玮. 复合式冷冻-热消融技术治疗乳腺纤维腺瘤一例[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 64-66.
[13] 李鸿略, 林家森, 耿唯宽, 李宇翔, 邓兴豪, 侯景义. 改良的B超引导下弯针线环腱鞘松解术技术[J/OL]. 中华关节外科杂志(电子版), 2026, 20(01): 110-115.
[14] 罗仲燃, 曾智豪, 黄梦娟, 何晓艺. 乳腺癌术后腋窝淋巴结负荷的多因素分析及预测模型的建立及验证[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(01): 46-50.
[15] 姚霞, 聂庆文, 贺芳. 妊娠合并乳腺癌胎盘转移一例并文献复习[J/OL]. 中华产科急救电子杂志, 2026, 15(01): 49-54.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?