Nontherapeutic Risk Factors of Different Grouped Stage IIIC Breast Cancer Patients’ Mortality: A Study of the US Surveillance, Epidemiology, and End Results Database

Qiu, Yue; Chen, Hongye; Dai, Yongjing; Bao, Baoshi; Tian, Lin; Chen, Yuhui

doi:https://doi.org/10.1155/2022/6705052

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Research Article | Open Access

Volume 2022 | Article ID 6705052 | https://doi.org/10.1155/2022/6705052

Nontherapeutic Risk Factors of Different Grouped Stage IIIC Breast Cancer Patients’ Mortality: A Study of the US Surveillance, Epidemiology, and End Results Database

Yue Qiu,^1,2Hongye Chen,²Yongjing Dai,²Baoshi Bao,²Lin Tian,²and Yuhui Chen²

Academic Editor: Taobo Hu

Received07 May 2022

Revised05 Jul 2022

Accepted22 Jul 2022

Published30 Aug 2022

Abstract

Objectives. Stage IIIC breast cancer, as a local advanced breast cancer, has a poor prognosis compared with that of early breast cancer. We further investigated the risk factors of mortality in stage IIIC primary breast cancer patients and their predictive value. Methods. We extracted data from the US Surveillance, Epidemiology, and End Results (SEER) database of female patients with stage IIIC primary breast cancer (n = 1673) from January 2011 to December 2015. Results. Hormone receptor negativity ( and , respectively), aggressive molecular typing ( and , respectively), high T stage ( and , respectively), a high number of positive lymph nodes (≥14) ( and , respectively), and lymph node ratio (≥0.8148) ( and , respectively) were associated with poor disease-specific survival. The indicators of disease-specific survival included estrogen receptor status, progesterone receptor status, molecular typing, T stage, number of positive lymph nodes, and lymph node ratio (,,,, , and , respectively). Conclusion. Hormone receptor negativity, aggressive molecular typing, high T stage, high number of positive lymph nodes, and lymph node ratio are poor prognostic factors patients with stage IIIC primary breast cancer. The efficient indicators of disease-specific survival include estrogen receptor status, progesterone receptor status, molecular typing, T stage, number of positive lymph nodes, and lymph node ratio.

1. Introduction

In 2020, the number of new cases of breast cancer worldwide reached 2.26 million, surpassing lung cancer to rank the first in the world [1]. The prognosis of breast cancer is relatively good, and the 5-year survival rate of some breast cancer can reach as high as 99% [2]. However, studies on the staging system showed that the survival rate of breast cancer decreased with the increasing stage [3]. The 7th version of the American Joint Committee on Cancer (AJCC) TNM staging system [4] defines stage IIIC (any TN3M0) breast cancer as N3 stage breast cancer without any distant metastasis. Patients who meet one of the following conditions are diagnosed as pN3: 10 or more axillary lymph nodes metastases; subclavian lymph nodes metastases; clinically found internal mammary lymph nodes metastases with one or more axillary lymph nodes metastases; more than 3 axillary lymph nodes metastases, accompanied by clinically undetected internal mammary lymph nodes metastases confirmed by sentinel lymph node biopsy; and ipsilateral supraclavicular lymph nodes metastases. According to the aforementioned criteria, patients can be further divided into N3a, N3b, and N3c.

The 5-year overall survival (OS) and 5-year disease-specific survival (DSS) of stage IIIC breast cancer are about 61.7% and 66.8%, respectively, and its prognosis is relatively worse than that of early breast cancer [5]. Some studies have discussed the prognostic indicators of stage IIIC breast cancer, such as molecular typing and lymph node ratio (LNR). However, most of the patients were from a single institution and the factors involved were not comprehensive; so, they could not be further grouped [6–8]. Based on the US Surveillance, Epidemiology, and End Results (SEER) database, we studied the risk factors of breast cancer-specific mortality (BCSM) in patients with stage IIIC breast cancer and explored their predictive value at different levels.

2. Materials and Methods

2.1. Patients

The data supporting this study were obtained from the SEER registry. Since 1975, the SEER program has continuously collected data on cancer incidence and mortality in the United States, which is based on 9 population-based registries. We acquired the required data from the SEER database through SEERStat software of version 8.3.9.2.

In the client-server mode of SEERStat, we used the International Classification of Diseases for Oncology, the 3rd edition of histology, and behavior codes (ICD-O-3 morphology codes 8500–8599) to identify patients. We selected 2211 female patients diagnosed with stage IIIC primary breast cancer from January 2011 to December 2015, excluding those with unclear pathological classification (n = 146), missing immunohistochemical markers (n = 69), unknown involved lymph node number (n = 215), inaccessible lymph node staging (n = 77), and incomplete follow-up (n = 31). Finally, a total of 1673 patients were left (Figure 1).

2.2. Study Variables

We evaluated the clinicopathological features of each patient, including age at diagnosis, laterality, primary site, histological type, histological grade, molecular typing, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER-2) status, T stage, N3 stage, number of positive lymph nodes (LNP), LNR, and survival time. The age at diagnosis of patients was classified as 40 years. The LNR was defined as the ratio of the number of LNP to the total number of lymph nodes. The cutoff values of LNP and LNR, calculated by the receiver operating characteristic curve (ROC), were 14 and 0.8148, respectively.

2.3. Statistical Methods

All statistical analyses of this study were completed using Stata Statistical Software version 15.1 (College Station, TX: StataCorp LLC). The measurement data were described by median (interquartile range). Frequency was used to show the counting data. The Kaplan–Meier method was used in survival analysis, and the log-rank test was used to compare different survival curves. Univariate and multivariate analyses were performed using the Cox model. Finally, the ROC analysis was conducted to illustrate the discriminating ability of risk factors in terms of BCSM and report the area under the curve (AUC). Statistical significance was set as two-sided , and all confidence intervals (CI) were expressed at the 95% confidence level.

3. Results

3.1. Clinicopathological Features

Most patients with stage IIIC breast cancer were over 40 years of age (91.15% vs 8.85%). As for the histological type, patients with invasive ductal carcinoma predominated (64.02% vs 35.98%). In terms of molecular typing, luminal A patients ranked first, followed by triple negative patients (68.02% and 12.91%, respectively). The number of ER positive patients, PR positive patients, and HER-2 negative patients were far higher than their counterparts (78.00%, 65.03%, and 80.93%, respectively). The proportions of T2 stage patients and N3a stage patients were the highest (45.13% and 85.24%, respectively). There were more patients with an LNP higher than 14 and patients with an LNR higher than 0.8148 (56.37% and 55.29%) (Table 1). The median follow-up time was 54 (38∼72) months. At the end of the follow-up, 578 patients died, of whom 447 died of breast cancer. The 5-year OS and DSS were 51.72% (95% CI: 0.4838∼0.5495) and 60.74% (95% CI: 0.5735∼0.6395), respectively.

3.2. Univariate Analysis Related to DSS

Univariate analysis showed that poor molecular typing, ER negativity, PR negativity, HER-2 negativity, high T stage, and high N3 stage were associated with poor DSS (, , , , , and , respectively). A high number of LNP (≥14) and LNR (≥0.8148) were associated with poor DSS ( and , respectively) (Table 2).

3.3. Multivariate Analysis by the Cox Model Assessing Factors Associated with DSS

In multivariate analysis, ER negativity, PR negativity, aggressive molecular typing, and high T stage were still associated with poor DSS (,, , and , respectively). A higher number of LNP (≥14) and LNR (≥0.8148) were also associated with poor DSS ( and , respectively). There was a borderline level of statistical significance between the N3c stage and DSS (Hazard ratio: 1.419, 95% CI: 0.982∼2.051,) (Table 3).

3.4. Survival Analysis

The 5-year OS and DSS were 51.72% (95% CI: 0.4838∼0.5495) and 60.74% (95% CI: 0.5735∼0.6395), respectively. The DSS curve is displayed in Figure 2(a). The survival rate of patients with hormone receptor (HR) positivity was significantly higher ( and , respectively; Figures 2(b) and 2(c)). The prognosis of patients with an aggressive molecular typing was poor (, Figure 2(d)). Moreover, the higher the T stage and N3 stage were, the lower the survival rate of patients was ( and , respectively; Figures 2(e) and 2(f)). We also found that patients with LNP <14 and LNR <0.8148 had a better prognosis ( and , respectively; Figures 2(g) and 2(h)).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Figure 2

Kaplan–Meier analysis of DSS. (a). Kaplan–Meier estimates of DSS for patients with stage IIIC breast cancer. (b). Kaplan–Meier analysis comparing DSS in patients with different ER statuses. (c). Kaplan–Meier analysis comparing DSS in patients with different PR statuses. (d). Kaplan–Meier analysis comparing DSS in patients with different molecular typing. (e). Kaplan–Meier analysis comparing DSS in patients with different T stages. (f). Kaplan–Meier analysis comparing DSS in patients with different N3 stages. (g). Kaplan–Meier analysis comparing DSS in patients with different LNP. (h). Kaplan–Meier analysis comparing DSS in patients with different LNR. DSS: disease-specific survival. ER: estrogen receptor. LNP: positive lymph nodes. LNR: lymph node ratio. PR: progesterone receptor.

3.5. Predictive Value of Risk Factors

The ROC curve and AUC showed the discrimination ability of the ER status, PR status, T stage, molecular typing, LNP number, and LNR in stage IIIC breast cancer (Figure 3). The indicators for predicting DSS in patients with stage IIIC breast cancer included ER status, PR status, molecular typing, T stage, LNP number, and LNR (, , , , , and , respectively; Table 4). At the same time, we reported the ROC curve and AUC of the abovementioned predictors in patients with different N3 stages (Table 5 and Figure 4). The results showed that the predictors of DSS in patients with N3a stage were ER status, PR status, molecular typing, T stage, LNP number, and LNR (, , , , , and , respectively). The indicators with a good predictive value for DSS in patients with N3b stage only included the ER status, PR status, molecular typing, and T stage (, , , and , respectively); however, these indicators had a poor predictive ability for DSS in N3c patients. Meanwhile, there was no significant difference in the DSS prediction ability of each index in patients with different N3 stages ().

(a)

(b)

(c)

(d)

(e)

(f)

Figure 4

The ROC curve and AUC of risk factors of DSS in patients with different N3 stages. (a). Comparison of the ROC curve and AUC of the ER status in patients with different N3 stages. (b). Comparison of the ROC curve and AUC of molecular typing in patients with different N3 stages. (c). Comparison of the ROC curve and AUC of LNP in patients with different N3 stages. (d). Comparison of the ROC curve and AUC of the PR status in patients with different N3 stages. (e). Comparison of the ROC curve and AUC of LNR in patients with different N3 stages. (f). Comparison of the ROC curve and AUC of the T stage in patients with different N3 stages. AUC: area under the curve. DSS: disease-specific survival. ER: estrogen receptor. LNP: positive lymph nodes. LNR: lymph node ratio. PR: progesterone receptor. ROC: receiver operating characteristic curve.

4. Discussion

The 5-year OS of stage IIIC breast cancer was about 61%∼73%, and it was reported that the prognosis was not good [9, 10]. The median follow-up time of patients in this study was 54 (38∼72) months, the restricted mean DSS was 76.027 (95% CI: 74.5258∼77.5287) months, and the 5-year OS and 5-year DSS were 51.72% (95% CI: 0.4838∼0.5495) and 60.74% (95% CI: 0.5735∼6395), respectively. However, not all patients with stage IIIC breast cancer have the same clinicopathological characteristics and prognosis. For example, some researchers believed that the risk of BCSM in HR-negative stage IIIC breast cancer patients in 20 years was significantly higher than that in HR-positive patients (68.7% vs 63.1%) [11].

At the beginning, we discussed the clinicopathological features of patients with stage IIIC breast cancer and found that stage IIIC breast cancer patients had old age, low HER-2 positivity rate, and a high HR positivity rate. In terms of molecular typing, Luminal A patients ranked first, followed by triple negative patients (68.02% and 12.91%, respectively). The HR and HER-2 status are recognized prognostic markers in breast cancer. Compared with other subtypes, the overall prognosis of triple negative breast cancer was poor [12, 13], while the overall prognosis of Luminal B breast cancer was the best [14, 15]. Our Kaplan–Meier analysis also confirmed this point. The survival rates of HR-positive patients were higher than those of HR-negative patients (). As for the histological type, patients with invasive ductal carcinoma predominated (64.02% vs 35.98%). After matching, invasive ductal carcinoma had similar OS to invasive lobular carcinoma, but invasive lobular carcinoma patients with risk factors had worse outcomes [16]. In addition, a high proportion of LNP <14 and LNR <0.8148 were also the characteristics of these patients. Notably, after breast cancer patients with supraclavicular nodal metastases downstaged from AJCC stage IV to IIIC, N3c patients who received the standard therapy demonstrated better OS than stage IV disease [17, 18]. Hence, we sought to illustrate whether there was a difference between stage IIIC patients.

In this study, univariate analysis showed that poor molecular typing, ER negativity, PR negativity, HER-2 negativity, high T stage, and high N3 stage were associated with poor DSS (, , , , and , respectively). In addition, the prognosis of patients with stage IIIC breast cancer varied with different N3 stages. Real-world studies found that patients with the N3c stage had the worst prognosis, followed by N3a patients, while N3b patients had the best prognosis. Both univariate and multivariate analyses showed that HR negativity ( and , respectively), aggressive molecular typing ( and , respectively), high T stage ( and , respectively), a high number of LNP (≥14) ( and , respectively), and LNR (≥0.8148) (, and , respectively) were associated with poor DSS. When the 7th version of the AJCC TNM staging system took N staging and the number of LNP as lymph node-related prognostic indicators, some researchers had questioned whether it was the most accurate way to represent the state of metastatic cancer in lymph nodes and proposed that LNR should be considered to be more valuable [19, 20]. In patients with stage II to III breast cancer, LNR may be a more useful tool for predicting survival than pathological lymph node staging [21–23].

We drew the ROC curve of the abovementioned prediction indicators and reported the AUC. The predictors of DSS for patients with stage IIIC breast cancer included ER status, PR status, molecular typing, T stage, LNP number, and LNR (, , , , , and , respectively). In some studies, large tumor size, lymph node metastasis, and HR negativity were often considered to be adverse prognostic factors [24–26]. In multivariate analysis, older age at diagnosis, higher N stage, and ER negativity were found to be independent predictors of BCSM [27]. We wanted to know whether the predictive value of these indicators was different from patients with different N3 stages. Therefore, patients were further grouped according to the N3 stage and we found that the indexes with predictive value for DSS of N3a patients were ER status, PR status, molecular typing, T stage, LNP number, and LNR (, , , , and , respectively). The indicators with a good predictive value for DSS in patients with the N3b stage only included ER status, PR status molecular typing, and T stage (, , , and , respectively). However, these indicators had a poor predictive ability in N3c patients. In previous studies, grade III disease, perineural invasion, LNP ≥20, and LNR ≥0.9 were found to be associated with the OS and disease-free survival of N3a stage breast cancer [28, 29]. For N3b stage breast cancer, pathological prognostic staging was an independent predictor related to OS and DSS [30]. In addition, there was no significant difference in the discrimination ability of DSS among patients with different N3 stages in our study ().

Inevitably, our research also has some limitations. For example, due to the lack of detailed follow-up data, we have not discussed the effect of different treatment options on the survival of patients with stage IIIC breast cancer under modern treatment modalities. In addition, we failed to establish an effective prognosis prediction model. We still lack specific exploration and discussion on the predictive value of LNR.

5. Conclusion

The survival rate of HR-positive patients with stage IIIC breast cancer is significantly higher. The higher the T stage and N3 stage are, the lower the survival rate is. The prognosis of patients with aggressive molecular typing, LNP ≥14, and LNR ≥0.8148 is poor. HR negativity, poor molecular typing, high T stage and N3 stage, and high LNP and LNR are the adverse prognostic factors of stage IIIC breast cancer. The ideal predictors of BCSM in patients with stage IIIC breast cancer include ER status, PR status, molecular typing, T stage, LNP number, and LNR. Although the discrimination ability of the abovementioned indicators in patients with different N3 stages is dissimilar, there is no statistical difference.

Data Availability

All data generated or analyzed during this study are included in this article.

Ethical Approval

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The research study was carried out in accordance with the Declaration of Helsinki (as revised in 2013).

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding the publication of this article.

Authors’ Contributions

Yue Qiu and Yuhui Chen are the guarantors of the integrity of the study and study concepts and design. Literature research and clinical studies were conducted by all authors. All authors participated in the experimental studies, data analysis, and statistical analysis. All authors contributed to manuscript preparation and manuscript editing. All authors gave final approval of the version to be submitted.

References

GLOBOCAN 2020, “Graph production: Global cancer observatory,” 2021, http://gco.iarc.fr/today.
View at: Google Scholar
K. Maajani, A. Jalali, S. Alipour, M. Khodadost, H. R. Tohidinik, and K. Yazdani, “The Global and regional survival rate of women with breast cancer: a systematic review and meta-analysis,” Clinical Breast Cancer, vol. 19, no. 3, pp. 165–177, 2019.
View at: Google Scholar
S. B. Lee, G. Sohn, J. Kim et al., “A retrospective prognostic evaluation analysis using the 8th edition of the American Joint Committee on Cancer staging system for breast cancer,” Breast Cancer Research and Treatment, vol. 169, no. 2, pp. 257–266, 2018.
View at: Publisher Site | Google Scholar
S. B. Edge, D. R. Byrd, C. C. Compton, G. A. Fritz, L. F. Greene, and A. Trotti, AJCC Cancer Staging Manual, Springer, New York, NY, USA, 2010.
X. Ai, X. Liao, J. Li, P. Tang, and J. Jiang, “Clinical outcomes of N3 breast cancer: a real-world study of a single institution and the us surveillance, epidemiology, and end results (SEER) database,” Cancer Management and Research, vol. 12, pp. 5331–5343, 2020.
View at: Publisher Site | Google Scholar
Ö Yersal, M. A. Kaplan, A. Işikdoğan et al., “Molecular subtypes are prognostic for N3 breast cancer patients in the modern therapeutic era,” Molecular and Clinical Oncology, vol. 10, no. 1, pp. 180–184, 2019.
View at: Publisher Site | Google Scholar
M. H. Hur and S. Ko, “Metastatic axillary node ratio predicts recurrence and poor long-term prognosis in patients with advanced stage IIIC (pN3) breast cancer,” Annals of Surgical Treatment and Research, vol. 92, no. 5, pp. 340–347, 2017.
View at: Publisher Site | Google Scholar
Y. Y. Kim, H. K. Park, K. H. Lee, K. I. Kim, and Y. S. Chun, “Prognostically distinctive subgroup in pathologic N3 breast cancer,” Journal of Breast Cancer, vol. 19, no. 2, pp. 163–168, 2016.
View at: Publisher Site | Google Scholar
J. Wang, L. Chen, Y. Nie, W. Wu, and Y. Yao, “Nomogram for predicting the overall survival of patients with breast cancer with pathologic nodal status N3,” Clinical Breast Cancer, vol. 20, no. 6, pp. e778–e785, 2020.
View at: Publisher Site | Google Scholar
A. Grassadonia, P. Vici, T. Gamucci et al., “Long-term outcome of breast cancer patients with pathologic N3a lymph node stage,” Breast, vol. 32, pp. 79–86, 2017.
View at: Publisher Site | Google Scholar
J. P. Leone, B. A. Leone, N. Tayob et al., “Twenty-year risks of breast cancer-specific mortality for stage III breast cancer in the surveillance, epidemiology, and end results registry,” Breast Cancer Research and Treatment, vol. 187, no. 3, pp. 843–852, 2021.
View at: Publisher Site | Google Scholar
N. L. Ferguson, J. Bell, R. Heidel et al., “Prognostic value of breast cancer subtypes, Ki-67 proliferation index, age, and pathologic tumor characteristics on breast cancer survival in Caucasian women,” Breast Journal, vol. 19, no. 1, pp. 22–30, 2013.
View at: Publisher Site | Google Scholar
N. Howlader, K. A. Cronin, A. W. Kurian, and R. Andridge, “Differences in breast cancer survival by molecular subtypes in the United States,” Cancer Epidemiology, Biomarkers & Prevention, vol. 27, no. 6, pp. 619–626, 2018.
View at: Publisher Site | Google Scholar
J. P. Leone, J. Leone, A. O. Zwenger, C. T. Vallejo, and B. A. Leone, “Prognostic significance of tumor subtypes in women with breast cancer according to stage: a population-based study,” American Journal of Clinical Oncology, vol. 42, no. 7, pp. 588–595, 2019.
View at: Publisher Site | Google Scholar
S. D. Simon, J. Bines, G. Werutsky et al., “Characteristics and prognosis of stage I-III breast cancer subtypes in Brazil: the AMAZONA retrospective cohort study,” Breast, vol. 44, pp. 113–119, 2019.
View at: Publisher Site | Google Scholar
C. Yang, C. Lei, Y. Zhang et al., “Comparison of overall survival between invasive lobular breast carcinoma and invasive ductal breast carcinoma: a propensity score matching study based on SEER database,” Frontiers in Oncology, vol. 10, Article ID 590643, 2020.
View at: Publisher Site | Google Scholar
N. P. Tamirisa, Y. Ren, B. M. Campbell et al., “Treatment patterns and outcomes of women with breast cancer and supraclavicular nodal metastases,” Annals of Surgical Oncology, vol. 28, no. 4, pp. 2146–2154, 2021.
View at: Publisher Site | Google Scholar
A. Nilupai, J. Y. Wang, Q. Li et al., “[Analysis of the effect of ipsilateral supraclavicular lymph node metastasis on the prognosis of N3 breast cancer],” Zhonghua Zhongliu Zazhi, vol. 43, no. 10, pp. 1069–1075, 2021.
View at: Google Scholar
Y. Li, E. Holmes, K. Shah, K. Albuquerque, A. Szpaderska, and C. Ersahin, “The prognostic value of lymph node cross-sectional cancer area in node-positive breast cancer: a comparison with N stage and lymph node ratio,” Pathology Research International, vol. 2012, Article ID 161964, 8 pages, 2012.
View at: Publisher Site | Google Scholar
L. J. Chen, K. P. Chung, Y. J. Chang, and Y. J. Chang, “Ratio and log odds of positive lymph nodes in breast cancer patients with mastectomy,” Surgical Oncology, vol. 24, no. 3, pp. 239–247, 2015.
View at: Publisher Site | Google Scholar
A. Sakin and M. N. Aldemir, “Lymph node ratio predicts long-term survival in lymph node-positive breast cancer,” European Journal of Breast Health, vol. 16, no. 4, pp. 270–275, 2020.
View at: Publisher Site | Google Scholar
I. A. Cetin, S. U. Akay, H. B. Caglar Ozkok, and M. Sengoz, “Lymph node ratio as an independent prognostic factor for breast cancer-related mortality in patients with node-positive breast cancer,” Journal of Cancer Research and Therapeutics, vol. 16, no. 6, pp. 1387–1392, 2020.
View at: Publisher Site | Google Scholar
G. A. De la Cruz-Ku, D. Chambergo-Michilot, B. Valcarcel et al., “Lymph node ratio as best prognostic factor in triple-negative breast cancer patients with residual disease after neoadjuvant chemotherapy,” Breast Journal, vol. 26, no. 9, pp. 1659–1666, 2020.
View at: Publisher Site | Google Scholar
H. Yamashita, A. Ogiya, T. Shien et al., “Clinicopathological factors predicting early and late distant recurrence in estrogen receptor-positive, HER2-negative breast cancer,” Breast Cancer, vol. 23, no. 6, pp. 830–843, 2016.
View at: Publisher Site | Google Scholar
I. Turker, U. Y. Arslan, O. Yazici et al., “Prognostic factors in operated stage IIIC, pathological N3a breast cancer patients,” Breast Care, vol. 9, no. 6, pp. 421–427, 2014.
View at: Publisher Site | Google Scholar
D. Nguyen, J. Yu, W. C. Reinhold, and S. X. Yang, “Association of independent prognostic factors and treatment modality with survival and recurrence outcomes in breast cancer,” JAMA Network Open, vol. 3, no. 7, Article ID e207213, 2020.
View at: Publisher Site | Google Scholar
H. M. Johnson, W. Irish, N. A. Vohra, and J. H. Wong, “The effect of local therapy on breast cancer-specific mortality of women with occult breast cancer and advanced nodal disease (N2/N3): a population analysis,” Breast Cancer Research and Treatment, vol. 177, no. 1, pp. 155–164, 2019.
View at: Publisher Site | Google Scholar
E. Koca, T. Y. Kuzan, O. Dizdar et al., “Outcomes of locally advanced breast cancer patients with ≥10 positive axillary lymph nodes,” Medical Oncology, vol. 30, no. 3, p. 615, 2013.
View at: Publisher Site | Google Scholar
J. S. Lee, S. I. Kim, S. Y. Choi et al., “Factors influencing the outcome of breast cancer patients with 10 or more metastasized axillary lymph nodes,” International Journal of Clinical Oncology, vol. 16, no. 5, pp. 473–481, 2011.
View at: Publisher Site | Google Scholar
C. L. Lian, H. Y. Zhang, J. Wang et al., “Staging for breast cancer with internal mammary lymph nodes metastasis: utility of incorporating biologic factors,” Front in Oncology, vol. 10, 2021.
View at: Google Scholar

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Copyright © 2022 Yue Qiu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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