Breast cancer, the primary factor of tumor-related morbidity in women worldwide, has grown rapidly in recent years, presenting a great threat to human health (1). According to the published data, breast cancer has become the leading cancer after surpassing lung cancer in the number of new cases per year, and is also a significant cause of tumor-related deaths (2). It is well known that axillary lymph node (ALN) status is a crucial factor in evaluating the staging, prognosis, and treatment of breast cancer. Sentinel lymph node biopsy (SLNB) can be used for accurately assessing the axillary lymph node status of patients with early-stage breast cancer and has been widely used in clinical practice (3). Patients with positive sentinel lymph node (SLN) unavoidably receive axillary lymph node dissection (ALND), despite postoperative complications and poorer quality of life (4). Unfortunately, several studies have reported that more than half of breast cancer patients with positive SLN who receive ALND do not have non-SLN (NSLN) metastasis, implying that ALND is unnecessary for all these patients (5–7), which have led to a debate on the need for routine ALND in early-stage breast cancer patients with 1 or 2 positive SLNs. The National Comprehensive Cancer Network guidelines advise that early-stage breast cancer patients with 1 or 2 positive SLNs who receive breast-conserving surgery and radiotherapy are exempt from ALND. Meanwhile, the nomograms were developed by Kimberly Van Zee (8), Tenon (9), and Helsinki University (10) for the prediction of the probability of NSLN metastasis in patients with early-stage breast cancer who had SLN metastasis, which are all conducted in developed countries. However, few nomogram models have been conducted in developing countries represented by China. The rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy (11). It is great important to explore the possibility of omitting ALND in early-stage breast cancer patients with 1 or 2 positive SLNs in China.

The stiffness of malignant lesion is greater compared with the benign lesion, because tumor stiffness is associated with the degree of tumor malignancy (12). Ultrasound elastography has been reported to be able to quantitatively and qualitatively measure tumor stiffness, which is important for distinguishing benign or malignant lesion (13, 14). Recent studies showed the use of ultrasound elastography in the examination of many organs, such as lymph nodes, liver, and thyroid (15, 16). Shear wave elastography (SWE), a novel elastography technique, provides quantitative information on measuring tissue stiffness (17, 18). Recently, SWE was reported to be associated with the axillary lymph node metastasis in breast cancer (19). However, no nomogram based on SWE was developed for the prediction of the axillary lymph node metastasis in breast cancer.

This study aimed to develop a nomogram based on elastography for the prediction of the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs.

A total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. No significant differences were observed in baseline characteristics, including clinicopathologic and ultrasound characteristics, between the training and validation cohorts (all P>0.05) ( Table 1 ).

The routine ALND therapy for breast cancer patients with 1 or 2 SLN metastases is overtreatment because there is no further NSLN metastasis in approximately 60% of such patients, while positive lymph nodes other than SLN, if not removed, can cause residual tumor, leading to axillary recurrence (5–7). Thus, the development of a model that accurately predicts NSLN metastasis could improve the management of patients with positive SLNs. Medical nomograms graphically present statistical prediction models using biological and clinical variables to give the risk of a clinical event, such as death or lymph node metastasis, for a given individual (20). Preoperative nomograms can be used to assess the probability of lymph node metastases, helping clinicians find patients who may benefit more from more extensive surgery (21, 22). Our retrospective study aimed to develop a nomogram based on elastography for the prediction of the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. A prediction model was conducted after we discovered the independent risk factors of NSLN metastasis. This satisfactory model was then verified using data from the training and validation cohorts, respectively.

As the tumor grows, it tends to become stiffer due to an increase in the fibrous component and a decrease in the necrotic component, which is well understood (23). SWE is a promising elastic imaging technology, allowing quantitative measurement of tissue stiffness (24). The lymph node metastasis of breast cancer has been reported to be associated with elastic modulus of breast masses (25). Moreover, the increased tumor stiffness is thought to be an easier infiltration of tumor cells into the tissue interstices, which is an independent predictor for the prediction of tumor recurrence (26–28). In this study, we found that the higher Emean was associated with a higher risk of NSLN metastasis, which may be interpreted by the histopathologic composition of the tumor. In addition, we visualized the effect of Emean on NSLN metastasis through the nomogram model.

The present study showed that tumor size and HER2 status were the independent risk factors of NSLN metastasis and that larger tumor size and positive HER2 expression leaded to a higher risk of NSLN metastasis. Other studies have also reported that these two factors are associated with NSLN metastasis (10, 29). In addition, we also found that Ki67 was the independent risk factor of NSLN metastasis, which was consistent with the conclusion that the high expression of Ki67 significantly improves the probability of lymph node metastasis (30).

The main strength of this study is the development of the nomogram with good performance for the prediction of the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs in China. The risk of NSLN metastasis could be calculated repeatedly depending on the variable clinical status, and the increased risk of NSLN metastasis is dynamically evaluated, which would be very helpful for clinicians to adopt individualized treatment strategies. The C-index of the nomogram conducted by Liu et al. was 0.740 and 0.689 in the training and validation cohorts, respectively (31). Compared to previous results, our nomogram including elastography with greater C-index showed a better performance.

Two limitations exist in this study. First, due to the retrospective design of this study, clinical data for some patients were partially missing. Second, the patients enrolled in this study were from only 2 hospitals, which leaded to the nomogram conducted in this study was not confirmed in other populations. The research with a larger sample size will be performed to verify this nomogram model.

We conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

The studies involving human participants were reviewed and approved by Hulunbuir People’s Hospital (No. 2021SYY-021) and Wuxi Huishan District People’s Hospital (No. HYLL20220510001). The patients/participants provided their written informed consent to participate in this study.

Study design: HD, XZ, WW. Data collection and analysis: HD, JZ, GZ, XZ, WW. Supervision: HD, XZ, WW. Statistics: HD, JZ, GZ. Manuscript writing: HD. Manuscript revision: XZ, WW. Approval of the manuscript: all authors.