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A multivariable model of ultrasound and clinicopathological features for predicting axillary nodal burden of breast cancer: potential to prevent unnecessary axillary lymph node dissection.
Yang, Lei; Gu, Yifan; Wang, Bing; Sun, Ming; Zhang, Lei; Shi, Lei; Wang, Yanfei; Zhang, Zheng; Yin, Yifei.
Affiliation
  • Yang L; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China.
  • Gu Y; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China.
  • Wang B; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China.
  • Sun M; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China.
  • Zhang L; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China.
  • Shi L; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China.
  • Wang Y; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China.
  • Zhang Z; Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, 212000, People's Republic of China. ujszhangzheng@163.com.
  • Yin Y; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China. yinyifei4311@163.com.
BMC Cancer ; 23(1): 1264, 2023 Dec 21.
Article in En | MEDLINE | ID: mdl-38129804
ABSTRACT

BACKGROUND:

To develop a clinical model for predicting high axillary nodal burden in patients with early breast cancer by integrating ultrasound (US) and clinicopathological features. METHODS AND MATERIALS Patients with breast cancer who underwent preoperative US examination and breast surgery at the Affiliated Hospital of Nantong University (centre 1, n = 250) and at the Affiliated Hospital of Jiangsu University (centre 2, n = 97) between January 2012 and December 2016 and between January 2020 and March 2022, respectively, were deemed eligible for this study (n = 347). According to the number of lymph node (LN) metastasis based on pathology, patients were divided into two groups limited nodal burden (0-2 metastatic LNs) and heavy nodal burden (≥ 3 metastatic LNs). In addition, US features combined with clinicopathological variables were compared between these two groups. Univariate and multivariate logistic regression analysis were conducted to identify the most valuable variables for predicting ≥ 3 LNs in breast cancer. A nomogram was then developed based on these independent factors.

RESULTS:

Univariate logistic regression analysis revealed that the cortical thickness (p < 0.001), longitudinal to transverse ratio (p = 0.001), absence of hilum (p < 0.001), T stage (p = 0.002) and Ki-67 (p = 0.039) were significantly associated with heavy nodal burden. In the multivariate logistic regression analysis, cortical thickness (p = 0.001), absence of hilum (p = 0.042) and T stage (p = 0.012) were considered independent predictors of high-burden node. The area under curve (AUC) of the nomogram was 0.749.

CONCLUSION:

Our model based on US variables and clinicopathological characteristics demonstrates that can help select patients with ≥ 3 LNs, which can in turn be helpful to predict high axillary nodal burden in early breast cancer patients and prevent unnecessary axillary lymph node dissection.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast Neoplasms Limits: Female / Humans Language: En Journal: BMC Cancer Journal subject: NEOPLASIAS Year: 2023 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast Neoplasms Limits: Female / Humans Language: En Journal: BMC Cancer Journal subject: NEOPLASIAS Year: 2023 Type: Article