天津医药

天津医药 ›› 2022, Vol. 50 ›› Issue (5): 533-538.doi: 10.11958/20212203

• 应用研究 • 上一篇    下一篇

前哨淋巴结阳性及全乳切除的乳腺癌患者非前哨淋巴结转移的术中预测模型

陈佩贤1，陈凯2，周丹1，潘瑞琳1，叶国麟1△，陈小松3，苏逢锡2   

  1. 1佛山市第一人民医院乳腺外一科（邮编528000）；2中山大学孙逸仙纪念医院乳腺肿瘤中心；3上海交通大学医学院附属瑞金医院乳腺疾病诊治中心
  • 收稿日期:2021-09-24 修回日期:2021-12-07 出版日期:2022-05-15 发布日期:2022-07-04
  • 基金资助:
    广东省医学科研基金项目（A2019329）；佛山市医学类科技攻关项目（2018AB002821）

The intraoperative prediction model of non-sentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients receiving mastectomy

CHEN Peixian1, CHEN Kai2, ZHOU Dan1, PAN Ruilin1, YE Guolin1△, CHEN Xiaosong3, SU Fengxi2   

  1. 1 Department of Breast Surgery, the First People’s Hospital of Foshan, Foshan 528000, China; 2 Breast Cancer Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University; 3 Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
  • Received:2021-09-24 Revised:2021-12-07 Published:2022-05-15 Online:2022-07-04

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摘要： 目的　建立术中预测模型并评估前哨淋巴结阳性及全乳切除的乳腺癌患者非前哨淋巴结（NSLN）转移的风险。方法　回顾性收集在国内三甲医院行全乳切除术的601例乳腺癌患者资料，其中建模组221例，内部验证组189例，外部验证组191例。通过Logistic回归分析挖掘影响建模组患者NSLN转移的风险因素，建立多因素模型并进行内外验证。同时比较本研究模型与现有的3个NSLN转移术后预测模型（MSKCC模型、TENON模型及MDA模型）在本研究人群中的应用效果。结果　建模组、内部验证组、外部验证组的NSLN转移率分别是32.6%、32.3%和50.3%。多因素分析显示，建模组中年龄（OR=0.968，95%CI：0.941~0.996）、孕激素受体（PR）状态（OR=0.484，95%CI：0.247~0.951）、肿瘤大小（OR=1.491，95%CI：1.151~1.932）、SLN阳性数量（OR=1.868，95%CI：1.278~2.730）、SLN阴性数量（OR=0.763，95%CI：0.631~0.922）是NSLN转移的影响因素。根据上述指标建立的NSLN转移预测模型在建模组的校准度（Hosmer-Lemeshow χ2=8.309，P=0.404）及区分度［受试者工作特征（ROC）曲线下面积（AUC）=0.752，95%CI：0.685~0.819］均较好。该模型在内部验证组和外部验证组的AUC分别为0.751（95%CI：0.676~0.825）和0.681（95%CI：0.606~0.757）。本模型和MSKCC模型、TENON评分系统的预测价值相当，略优于MDA模型。结论　针对全乳切除术且SLN阳性的早期乳腺癌患者建立的NSLN转移术中预测模型的内外验证结果均较好，准确度与现有术后预测模型相当。

关键词: 乳腺肿瘤, 前哨淋巴结活组织检查, 淋巴转移, 术中预测模型

Abstract: Objective　To establish an intraoperative predictive model to assess the risk of non-sentinel lymph node (NSLN) metastasis in breast cancer patients with positive sentinel lymph nodes and total mastectomy. Methods　Data of 601 breast cancer patients who underwent total mastectomy in China were retrospectively collected, which including 221 cases in the modeling group, 189 cases in the internal validation group, and 191 cases in the external validation group. Logistic regression analysis was used to mine the risk factors related to NSLN metastasis in patients in the modeling group, and a multi-factor model was established and verified internally and externally. At the same time, the application effect of three existing prediction models after NSLN metastasis (MSKCC model, Tenon scoring system and MDA model) in population of this study was compared. Results　The positive NSLN rates were 32.6%, 32.3% and 50.3% in the modeling group, the internal validation group and the external validation group, respectively. Multivariate analysis showed that age (OR=0.968, 95%CI: 0.941-0.996), PR status (OR=0.484, 95%CI: 0.247-0.951), tumor size (OR=1.491, 95%CI: 1.151-1.932), the number of positive SLN (OR=1.868, 95%CI: 1.278-2.730) and the number of negative SLN (OR=0.763, 95%CI: 0.631-0.922) were the influencing factors of NSLN metastasis. The calibration degree (Hosmer-Lemeshow χ2=8.309, P=0.404) and differentiation degree (AUC=0.752, 95%CI: 0.685-0.819) of the NSLN metastasis prediction model based on the above indicators were good in the modeling group. The AUC values of the internal validation group and the external validation group were 0.751 (95%CI: 0.676-0.825) and 0.681 (95%CI: 0.606-0.757), respectively. The predictive value of this model was similar to that of MSKCC model and TENON scoring system, and slightly better than MDA model. Conclusion　The intraoperative prediction model of NSLN metastasis established for SLN positive patients with early breast cancer undergoing total mastectomy has good internal and external validation results, and its accuracy is comparable to existing postoperative prediction models.

Key words: breast neoplasms, sentinel lymph node biopsy, lymphatic metastasis, intraoperative prediction model

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