A Predictive Model for Nonsentinel Node Status after Sentinel Lymph Node Biopsy in Sentinel Lymph Node-Positive Chinese Women with Early Breast Cancer

Lifang He; Peide Liang; Huancheng Zeng; Guangsheng Huang; Jundong Wu; Yiwen Zhang; Yukun Cui; Wenhe Huang

doi:10.1155/2022/7704686

A Predictive Model for Nonsentinel Node Status after Sentinel Lymph Node Biopsy in Sentinel Lymph Node-Positive Chinese Women with Early Breast Cancer

J Oncol. 2022 Feb 24:2022:7704686. doi: 10.1155/2022/7704686. eCollection 2022.

Authors

Lifang He^{1

2}, Peide Liang^{1

3}, Huancheng Zeng¹, Guangsheng Huang¹, Jundong Wu^{1

2}, Yiwen Zhang¹, Yukun Cui², Wenhe Huang^{4

5}

Affiliations

¹ Breast Center, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China.
² Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China.
³ Department of Thyroid and Breast Surgery, Dongguan Houjie Hospital, Dongguan 523000, Guangdong Province, China.
⁴ Department of Breast and Thyroid Surgery, Xiang'an Hospital of Xiamen University, No. 2000, Xiang'an East Road, Xiamen 361101, Fujian Province, China.
⁵ Key Laboratory for Endocrine-Related Cancer Precision Medicine of Xiamen, Xiamen 361101, Fujian Province, China.

Abstract

Background: Axial lymph node dissection (ALND) is needed in patients with positive sentinel lymph node (SLN). ALND is easy to cause upper limb edema. Therefore, accurate prediction of nonsentinel lymph nodes (non-SLN) which may not need ALND can avoid excessive dissection and reduce complications. We constructed a new prognostic model to predict the non-SLN metastasis of Chinese breast cancer patients.

Methods: We enrolled 736 patients who underwent sentinel lymph node biopsy (SLNB); 228 (30.98%) were diagnosed with SLNB metastasis which was determined by intraoperative pathological detection and further accepted ALND. We constructed a prediction model by univariate analysis, multivariate analysis, "R" language, and binary logistic regression in the abovementioned 228 patients and verified this prediction model in 60 patients.

Results: Based on univariate analysis using α = 0.05 as the significance level for type I error, we found that age (P=0.045), tumor size (P=0.006), multifocality (P=0.011), lymphovascular invasion (P=0.003), positive SLN number (P=0.009), and negative SLN number (P=0.034) were statistically significant. Age was excluded in multivariate analysis, and we constructed a predictive equation to assess the risk of non-SLN metastasis: Logit(P)=Ln(P/1 - P)=0.267∗a+1.443∗b+1.078∗c+0.471∗d - 0.618∗e - 2.541 (where "a" represents tumor size, "b" represents multifocality, "c" represents lymphovascular invasion, "d" represents the number of metastasis of SLN, and "e" represents the number of SLNs without metastasis). AUCs for the training group and validation group were 0.715 and 0.744, respectively. When setting the risk value below 22.3%, as per the prediction equation's low-risk interval, our model predicted that about 4% of patients could avoid ALND.

Conclusions: This study established a model which demonstrated good prognostic performance in assessing the risk of non-SLN metastasis in Chinese patients with positive SLNs.