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基于体外超声特征的乳腺癌淋巴结转移淋巴结预测模型

Lymph Node Predictive Model with in Vitro Ultrasound Features for Breast Cancer Lymph Node Metastasis.

作者信息

Han Pu, Yang Houpu, Liu Miao, Cheng Lin, Wang Siyuan, Tong Fuzhong, Liu Peng, Zhou Bo, Cao Yingming, Liu Hongjun, Wang Chaobin, Peng Yuan, Shen Danhua, Wang Shu

机构信息

Breast Center, Peking University People's Hospital, Beijing, China.

Pathology Department, Peking University People's Hospital Beijing, China.

出版信息

Ultrasound Med Biol. 2020 Jun;46(6):1395-1402. doi: 10.1016/j.ultrasmedbio.2020.01.030. Epub 2020 Mar 5.

Abstract

Ultrasound diagnosis of axillary lymph nodes has the advantages of ease, convenience and low cost; however, most previous studies evaluated lymph node metastasis of the entire axilla rather than the association between the ultrasound features of a single lymph node and its pathology. This prospective study was performed to explore the ultrasound features of lymph nodes observed in bionic medium in vitro and to develop a lymph node-specific model for prediction of metastasis based on analysis of the association between the ultrasound features and pathology of each lymph node. From November 1, 2017 to December 19, 2017, 373 nodes (54 patients) were enrolled into the modeling group; from December 20, 2017 to January 12, 2018, 139 lymph nodes (22 patients) were enrolled into the validation group. Lymph nodes from sentinel lymph node biopsy or axillary lymph node dissection were enrolled. Individual lymph nodes were placed in bionic medium and observed separately using ultrasound. Traditional ultrasound features of metastatic nodes (long axis, short axis, cortical thickness and hilum loss) were recorded, and the longitudinal-to-transverse axis ratio (L/T) and cortical proportion were calculated. Pathologic results specific to each lymph node were recorded. On the basis of two-level binary logistic regression, independent predictors of lymph node metastasis in the modeling group were lymph node long axis (p = 0.004), short axis (p < 0.001), L/T (p = 0.006), cortical thickness (p = 0.001) and hilum loss (p < 0.001). When analysis was done at the node level, the areas under the curve of the modeling and validation groups were 0.97 and 0.75, respectively. When validation was done at the patient level, the areas under the curve of the modeling and validation groups were 0.96 and 0.93, respectively. The model for prediction of metastasis based on the ultrasound features and pathology of each lymph node is of good predictive value for lymph node metastasis.

摘要

腋窝淋巴结的超声诊断具有简便、便捷和低成本的优点;然而,以往大多数研究评估的是整个腋窝的淋巴结转移情况,而非单个淋巴结的超声特征与其病理之间的关联。本前瞻性研究旨在探讨体外仿生介质中观察到的淋巴结的超声特征,并基于对每个淋巴结的超声特征与病理之间关联的分析,建立一个用于预测转移的淋巴结特异性模型。2017年11月1日至2017年12月19日,373个淋巴结(54例患者)被纳入建模组;2017年12月20日至2018年1月12日,139个淋巴结(22例患者)被纳入验证组。纳入前哨淋巴结活检或腋窝淋巴结清扫获取的淋巴结。将单个淋巴结置于仿生介质中,分别用超声进行观察。记录转移淋巴结的传统超声特征(长轴、短轴、皮质厚度和门部消失),并计算纵横比(L/T)和皮质比例。记录每个淋巴结的病理结果。基于二级二元逻辑回归,建模组中淋巴结转移的独立预测因素为淋巴结长轴(p = 0.004)、短轴(p < 0.001)、L/T(p = 0.006)、皮质厚度(p = 0.001)和门部消失(p <  0.001)。在淋巴结水平进行分析时,建模组和验证组的曲线下面积分别为0.97和0.75。在患者水平进行验证时,建模组和验证组的曲线下面积分别为0.96和0.93。基于每个淋巴结的超声特征和病理建立的转移预测模型对淋巴结转移具有良好的预测价值。

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