李婷婷, 薛继平, 苏莉莉. 乳腺癌剪切波弹性成像与常规超声特征预测腋窝淋巴结转移及模型建立[J]. 实用临床医药杂志, 2023, 27(5): 11-15. DOI: 10.7619/jcmp.20221769
引用本文: 李婷婷, 薛继平, 苏莉莉. 乳腺癌剪切波弹性成像与常规超声特征预测腋窝淋巴结转移及模型建立[J]. 实用临床医药杂志, 2023, 27(5): 11-15. DOI: 10.7619/jcmp.20221769
LI Tingting, XUE Jiping, SU Lili. Prediction of axillary lymph node metastasis by shear wave elastography and conventional ultrasound features of breast cancer and model establishment[J]. Journal of Clinical Medicine in Practice, 2023, 27(5): 11-15. DOI: 10.7619/jcmp.20221769
Citation: LI Tingting, XUE Jiping, SU Lili. Prediction of axillary lymph node metastasis by shear wave elastography and conventional ultrasound features of breast cancer and model establishment[J]. Journal of Clinical Medicine in Practice, 2023, 27(5): 11-15. DOI: 10.7619/jcmp.20221769

乳腺癌剪切波弹性成像与常规超声特征预测腋窝淋巴结转移及模型建立

Prediction of axillary lymph node metastasis by shear wave elastography and conventional ultrasound features of breast cancer and model establishment

  • 摘要:
    目的 探讨乳腺癌原发灶剪切波弹性成像(SWE)定量参数和常规超声特征对腋窝淋巴结转移(ALNM)的预测价值,并与腋窝超声相结合构建术前列线图模型。
    方法 选取术前行SWE和常规超声检查的乳腺癌患者295例。回顾性分析原发灶及腋窝淋巴结超声特征,筛选与ALNM相关的独立危险因素,在此基础上构建列线图模型并评估预测价值。
    结果 乳腺癌原发灶边缘不光整、Emax及超声诊断ALNM阳性是预测ALNM的独立危险因素。在此基础上构建的列线图模型的曲线下面积(AUC)为0.842(95%CI: 0.786~0.888), 预测效能均优于单一指标,差异有统计学意义(P < 0.05)。
    结论 乳腺癌原发灶SWE定量参数和常规超声特征可用于预测ALNM, 与腋窝超声相结合构建的列线图模型在预测ALNM方面具有良好的价值。

     

    Abstract:
    Objective To explore the predictive value of shear wave elastography (SWE) quantitative parameters and conventional ultrasound features of primary lesions for axillary lymph node metastasis (ALNM) in breast cancer, and to construct a preoperative nomogram model combined with axillary ultrasound.
    Methods A total of 295 breast cancer patients who underwent preoperative SWE and conventional ultrasound were selected. Ultrasonic features of primary lesions and axillary lymph nodes were retrospectively analyzed, and independent risk factors associated with ALNM were screened, based on which a nomogram model was constructed and the predictive value was assessed.
    Results Breast tumor uneven margin, Emax and positive for ALNM by ultrasound diagnosis were independent risk factors for predicting ALNM. On this basis, the area under the curve (AUC) of the nomogram model was 0.842 (95%CI, 0.786 to 0.888), and the prediction efficiency was significantly better than that of a single index (P < 0.05).
    Conclusion The quantitative parameters of SWE and conventional ultrasound characteristics of primary lesions of breast cancer can be used to predict ALNM, and the nomogram model constructed in combination with axillary ultrasound has a good value in predicting ALNM.

     

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