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腋窝淋巴结超声特征及乳腺肿瘤特征作为恶性肿瘤预测指标:一种预测风险的列线图

Axillary Lymph Node Sonographic Features and Breast Tumor Characteristics as Predictors of Malignancy: A Nomogram to Predict Risk.

作者信息

Akissue de Camargo Teixeira Patricia, Chala Luciano F, Shimizu Carlos, Filassi José R, Maesaka Jonathan Y, de Barros Nestor

机构信息

Breast Imaging Center, Department of Radiology, University of São Paulo Medical School, São Paulo, Brazil.

Breast Imaging Center, Department of Radiology, University of São Paulo Medical School, São Paulo, Brazil.

出版信息

Ultrasound Med Biol. 2017 Sep;43(9):1837-1845. doi: 10.1016/j.ultrasmedbio.2017.05.003. Epub 2017 Jun 17.

Abstract

The purpose of this study was to build a mathematical model to predict the probability of axillary lymph node metastasis based on the ultrasonographic features of axillary lymph nodes and the tumor characteristics. We included 74 patients (75 axillae) with invasive breast cancer who underwent axillary ultrasonography ipsilateral to the tumor and fine-needle aspiration of one selected lymph node. Lymph node pathology results from sentinel lymph node biopsy or surgical dissection were correlated with lymph node ultrasonographic data and with the cytologic findings of fine-needle aspiration. Our mathematical model of prediction risk of lymph node metastasis included only pre-surgical data from logistic regression analysis: lymph node cortical thickness (p = 0.005), pre-surgical tumor size (p = 0.030), menopausal status (p = 0.017), histologic type (p = 0.034) and tumor location (p = 0.011). The area under the receiver operating characteristic curve of the model was 0.848, reflecting an excellent discrimination of the model. This nomogram may assist in the choice of the optimal axillary approach.

摘要

本研究的目的是建立一个数学模型,以根据腋窝淋巴结的超声特征和肿瘤特征预测腋窝淋巴结转移的概率。我们纳入了74例浸润性乳腺癌患者(75个腋窝),这些患者接受了肿瘤同侧的腋窝超声检查以及对一个选定淋巴结的细针穿刺活检。前哨淋巴结活检或手术切除的淋巴结病理结果与淋巴结超声数据以及细针穿刺活检的细胞学结果相关。我们的淋巴结转移预测风险数学模型仅包括逻辑回归分析的术前数据:淋巴结皮质厚度(p = 0.005)、术前肿瘤大小(p = 0.030)、绝经状态(p = 0.017)、组织学类型(p = 0.034)和肿瘤位置(p = 0.011)。该模型的受试者工作特征曲线下面积为0.848,表明该模型具有出色的辨别能力。这一列线图可能有助于选择最佳的腋窝处理方法。

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