Ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases.
Ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases.
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乳腺癌超声检查及临床病理特征预测腋窝淋巴结转移
DOI：
10.1186/s12885-022-10240-z
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发表时间：
2022-11-09
期刊：
影响因子：
3.8
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中科院分区：
文献类型：
--
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乳腺癌腋窝淋巴结转移的早期诊断一直是临床上的一个难点。前哨淋巴结活检（SLNB）目前尚无较好的替代方法。本研究的目的是根据原发肿瘤的超声（US）和临床病理特征，开发并验证一个诺模图来预测术前ALNM的概率。收集2019年9月至2022年4月期间1076例接受手术治疗的T1-T2 BC患者的术前US）和临床病理资料。将患者分为训练集（2019年9月至2021年10月的875例患者）和验证集（2021年11月至2022年4月的201例患者）。根据腋窝手术病理将患者分为腋窝淋巴结（ALN）阳性组和阴性组。比较两组的超声及临床病理特征。采用多因素Logistic回归分析确定ALNM的危险因素，并构建列线图。使用AUC和校准来评估其性能。经单因素和多因素Logistic回归分析，年龄（p = 0.009）、组织学分级（p = 0.000）、分子亚型（p = 0.000）、肿瘤位置（p = 0.000）、最大直径（p = 0.000）、毛刺边缘（p = 0.000）和距皮肤距离（p = 0.000）是ALNM的独立危险因素。然后绘制了列线图。该模型具有良好的区分力，训练集和验证集的AUC分别为0.705和0.745。校准曲线显示高度一致。然而，在进一步预测重淋巴结疾病负担（> 2个淋巴结）时，没有一个变量是显著的。基于US和临床病理学数据的列线图可以预测T1-T2 BC患者中ALNM的存在。但它不能有效地预测一个沉重的淋巴结疾病负担（> 2个节点）。
Early identification of axillary lymph node metastasis (ALNM) in breast cancer (BC) is still a clinical difficulty. There is still no good method to replace sentinel lymph node biopsy (SLNB). The purpose of our study was to develop and validate a nomogram to predict the probability of ALNM preoperatively based on ultrasonography (US) and clinicopathological features of primary tumors. From September 2019 to April 2022, the preoperative US) and clinicopathological data of 1076 T1-T2 BC patients underwent surgical treatment were collected. Patients were divided into a training set (875 patients from September 2019 to October 2021) and a validation set (201 patients from November 2021 to April 2022). Patients were divided into positive and negative axillary lymph node (ALN) group according pathology of axillary surgery. Compared the US and clinicopathological features between the two groups. The risk factors for ALNM were determined using multivariate logistic regression analysis, and a nomogram was constructed. AUC and calibration were used to assess its performance. By univariate and multivariate logistic regression analysis, age (p = 0.009), histologic grades (p = 0.000), molecular subtypes (p = 0.000), tumor location (p = 0.000), maximum diameter (p = 0.000), spiculated margin (p = 0.000) and distance from the skin (p = 0.000) were independent risk factors of ALNM. Then a nomogram was developed. The model was good discriminating with an AUC of 0.705 and 0.745 for the training and validation set, respectively. And the calibration curves demonstrated high agreement. However, in further predicting a heavy nodal disease burden (> 2 nodes), none of the variables were significant. This nomogram based on the US and clinicopathological data can predict the presence of ALNM good in T1-T2 BC patients. But it cannot effectively predict a heavy nodal disease burden (> 2 nodes).
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影响因子：
5.9
作者：
Jiang, Meng;Li, Chang-Li;Dietrich, Christoph F.
通讯作者：
Dietrich, Christoph F.
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Reynolds, Hayley M
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2.4
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Taylor, G. Ian
影响因子：
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作者：
Giuliano, Armando E.;Connolly, James L.;Hortobagyi, Gabriel N.
通讯作者：
Hortobagyi, Gabriel N.