Volume 22 Issue 4
Apr.  2024
Turn off MathJax
Article Contents
Article Navigation >  Chinese Journal of General Practice  > 2024  >  22(4): 646-650
YANG Junyu, SHEN Ji, SHEN Siping, LI Dan, WU Wanbo. Construction and analysis of preoperative lymph node metastasis load model of breast cancer patients based on dual-mode ultrasound[J]. Chinese Journal of General Practice, 2024, 22(4): 646-650. doi: 10.16766/j.cnki.issn.1674-4152.003471
Citation: YANG Junyu, SHEN Ji, SHEN Siping, LI Dan, WU Wanbo. Construction and analysis of preoperative lymph node metastasis load model of breast cancer patients based on dual-mode ultrasound[J]. Chinese Journal of General Practice, 2024, 22(4): 646-650. doi: 10.16766/j.cnki.issn.1674-4152.003471
shu

Construction and analysis of preoperative lymph node metastasis load model of breast cancer patients based on dual-mode ultrasound

doi: 10.16766/j.cnki.issn.1674-4152.003471
  • 1.

    Department of Ultrasound, Huzhou Central Hospital, Huzhou, Zhejiang 313000, China

Funds:

 2020ZH041

  • Received Date: 2023-10-14
    Available Online: 2024-05-29
    Abstract
  •   Objective  To construct a risk model using dual-mode ultrasound to identify breast cancer patients with a high lymph node metastasis load. This model provides references for the formulation of surgical protocols.  Methods  The study included 168 breast cancer patients who underwent surgical treatment at Huzhou Central Hospital between May 2021 and May 2023. Preoperative dual-mode ultrasonography was performed and the patients were divided into two groups based on the status of lymph node metastasis: high metastatic load group (62 cases, ≥3 metastatic lymph nodes) and low metastatic load group (106 cases, ≤2 metastatic lymph nodes). LASSO-logistic regression was used to screen variables and construct a prediction model for preoperative high metastatic load of lymph nodes in breast cancer patients based on dual-mode ultrasound. The effectiveness of the model was also verified.  Results  Four representative characteristics, namely vascular invasion, catheter dilation, Adler blood flow grading and elastic strain rate were selected through LASSO regression analysis with 10-fold cross-validation. Multivariate logistic analysis revealed that vascular invasion (OR=2.250, 95% CI 1.012-5.002), Adler blood flow grade Ⅱ/Ⅲ (OR=2.929, 95% CI: 1.256-6.827), catheter dilation (OR=2.548, 95% CI: 1.066-6.093) and high elastic strain rate (OR=4.167, 95% CI: 2.486-6.982) were identified as risk factors for preoperative high burden of lymph node metastasis in breast cancer patients (P < 0.05). The C-index for factors predicting preoperative high metastatic load of lymph nodes in breast cancer patients was 0.834 (95% CI: 0.771-0.898), and the calibration curve demonstrated good agreement between measured and predicted values.  Conclusion  The model constructed using conventional two-dimensional ultrasound and shear-wave elastography ultrasound images combines the characteristics of multiple modes to make their information complementary. This is helpful in improving the accuracy of qualitative and localization analysis of lymph node metastasis load before breast cancer.

     

    • FullText(HTML)
  • loading
    • References(17)
  • [1]
    KATSURA C, OGUNMWONYI I, KANKM H K, et al. Breast cancer: presentation, investigation and management[J]. Br J Hosp Med(Lond), 2022, 83(2): 1-7.
    [2]
    MARINO M A, AVENDNO D, ZAPATA P, et al. Lymph node imaging in patients with primary breast cancer: concurrent diagnostic tools[J]. Oncologist, 2020, 25(2): e231-e242. doi: 10.1634/theoncologist.2019-0427
    [3]
    GU J, TONG T, XU D, et al. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: a multicenter study[J]. Cancer, 2023, 129(3): 356-366. doi: 10.1002/cncr.34540
    [4]
    SONG B I. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer[J]. Breast Cancer, 2021, 28(3): 664-671. doi: 10.1007/s12282-020-01202-z
    [5]
    GRADISHAR W J, MORAN M S, ABRAHAM J, et al. Breast cancer, version 3.2022, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2022, 20(6): 691-722. doi: 10.6004/jnccn.2022.0030
    [6]
    中国抗癌协会乳腺癌专业委员会. 中国抗癌协会乳腺癌诊治指南与规范(2019年版)[J]. 中国癌症杂志, 2019, 29(8): 609-679. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGAZ202312004.htm
    [7]
    American Institute of Ultrasound in Medicine, American Society of Breast Surgeons. AIUM practice guideline for the performance of a breast ultrasound examination[J]. J Ultrasound Med, 2009, 28(1): 105-109. doi: 10.7863/jum.2009.28.1.105
    [8]
    LAOHAWIRIYAKAMOL S, MHTTNOBON S, PUTTAWIBUL P. Intraoperative molecular analysis of total tumor load in sentinel lymph node: a predictor of axillary status in early breast cancer[J]. Asian Pac J Cancer Prev, 2022, 23(1): 349-354. doi: 10.31557/APJCP.2022.23.1.349
    [9]
    SHI J Y, WANG X, DING G Y, et al. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning[J]. Gut, 2021, 70(5): 951-961. doi: 10.1136/gutjnl-2020-320930
    [10]
    ZHU W, XIE L, HAN J, et al. The application of deep learning in cancer prognosis prediction[J]. Cancers (Basel), 2020, 12(3): 603. doi: 10.3390/cancers12030603
    [11]
    王莹, 崔颖颖, 王鑫慧, 等. 基于磁共振超短回波时间序列的影像组学模型预测非小细胞肺癌淋巴结转移[J]. 磁共振成像, 2023, 14(3): 17-20, 41. https://www.cnki.com.cn/Article/CJFDTOTAL-CGZC202303004.htm

    WANG Y, CUI Y Y, WANG X H, et al. 3D-ultrashort echo time MRI-based radiomics model facilitates the assessment of lymph node metastasis in non-small cell lung cancer[J]. Chinese Journal of Magnetic Resonance Imaging, 2023, 14(3): 17-20, 41. https://www.cnki.com.cn/Article/CJFDTOTAL-CGZC202303004.htm
    [12]
    胡文娟, 宋彬, 谢晓利, 等. 多参数磁共振组学预测甲状腺乳头状癌淋巴结转移的价值[J]. 放射学实践, 2023, 38(7): 863-867. https://www.cnki.com.cn/Article/CJFDTOTAL-FSXS202307009.htm

    HU W J, SING B, XIE X L, et al. Radiomics based on multiparametric MRI for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma[J]. Radiologic Practice, 2023, 38(7): 863-867. https://www.cnki.com.cn/Article/CJFDTOTAL-FSXS202307009.htm
    [13]
    姚晓倩, 洪敏萍, 蔡宏杰, 等. 基于超声影像组学术前预测浸润性乳腺癌患者腋窝淋巴结状态[J]. 现代实用医学, 2023, 35(1): 116-119. https://www.cnki.com.cn/Article/CJFDTOTAL-NBYX202301037.htm

    YAO X Q, HONG M P, CAI H J, et al. Prediction of axillary lymph node status in patients with invasive breast cancer based on pre-academic ultrasound imaging group[J]. Modern Practical Medicine, 2023, 35(1): 116-119. https://www.cnki.com.cn/Article/CJFDTOTAL-NBYX202301037.htm
    [14]
    刘晗, 徐楠, 吴杰, 等. 基于灰阶超声联合剪切波弹性成像的影像组学模型诊断乳腺癌腋窝淋巴结转移的临床价值[J]. 临床超声医学杂志, 2023, 25(4): 277-283. https://www.cnki.com.cn/Article/CJFDTOTAL-LCCY202304007.htm

    LIU H, XU N, WU J, et al. Clinical value of bimodal radiomics model based on gray-scale ultrasound combined with shear wave elastography in the diagnosis of axillary lymph node metastasis of breast cancer[J]. Journal of Clinical Ultrasound in Medicine, 2023, 25(4): 277-283. https://www.cnki.com.cn/Article/CJFDTOTAL-LCCY202304007.htm
    [15]
    ZHU Y, ZHUO W, JIA X H, et al. Preoperative axillary ultrasound in the selection of patients with a heavy axillary tumor burden in early-stage breast cancer: what leads to false-positive results?[J]. J Ultrasound Med, 2018, 37(6): 1357-1365. doi: 10.1002/jum.14545
    [16]
    PATEL B K, SMREEN N, ZHUO Y, et al. MR elastography of the breast: evolution of technique, case examples, and future directions[J]. Clin Breast Cancer, 2021, 21(1): e102-e111. doi: 10.1016/j.clbc.2020.08.005
    [17]
    陈佳佳, 孟利伟, 李星云. 乳腺癌超声特征和ER、PR、CerbB-2、Ki-67阳性表达的相关性研究[J]. 中华全科医学, 2021, 19(10): 1721-1724. doi: 10.16766/j.cnki.issn.1674-4152.002151

    CHEN J J, MENG L W, LI X Y. Relationship between ultrasound features and expression of ER, PR, CerbB-2 and Ki-67 in breast cancer[J]. Chinese Journal of General Practice, 2021, 19(10): 1721-1724. doi: 10.16766/j.cnki.issn.1674-4152.002151
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(3)  / Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (52) PDF downloads(0) Cited by()
    Proportional views
    Related

    Address：No. 287, Changhuai Road, Bengbu, Anhui Province, 233004, P.R.ChinaphoneNo：0552-3066635； 0552-3051890Email：zhqkyx@163.com

    Copyright © Chinese Journal of General Practice皖ICP备2020018345号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return