Establishment of a model to predict the recurrence time of hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer

Introduction

Breast cancer (BC) takes a large part of malignant tumor in China (1). As reported in a paper published in Chinese Journal of Cancer Research, the age-standardized incidence rate of BC is 36.1/100,000 in 2018 (1). Approximately 30% of early BC would relapse (2,3). The recurrence risk and recurrence time can be predicted by such clinicopathological factors as hormone receptor (HR) status and the number of positive lymph nodes (4). Estrogen receptor (ER), progesterone receptor (PR) or both are found in about 70% of all BCs, which are considered HR-positive (HR⁺) BC (5-7). As for HR-negative (HR⁻) BC, the recurrence rate reaches the highest level during the first 2 years after initial diagnosis, and drops rapidly to a low level afterwards. Most of the recurrence occurs during the 5 years after initial diagnosis, which is defined as early recurrence. While, as for HR⁺ BC, about 50% of recurrence occurs more than 5 years after initial treatment, which is defined as late recurrence (8-11), and the recurrence risk tends to be stable at a low level after at least 20 years (3,12). Although it has been demonstrated that adjuvant chemotherapy could significantly reduce the risk of early recurrence in patients with early BC (13,14), there remains a high risk of late recurrence in HR⁺/HER2⁻ BC patients (15,16). Five-year adjuvant endocrine therapy has long been regarded as standard treatment for patients with early HR⁺/HER2⁻ BC (17). According to previous studies, 5-year endocrine therapy could reduce a third of late recurrence (15-year recurrence) (18), and extending the duration of adjuvant endocrine therapy to 10 years could further reduce the risk of late recurrence in the second decade after initial diagnosis (19-23).

For the reason that HR⁺ BC is a biologically heterogeneous disease, there may be various responses to endocrine therapy for patients with different subtypes. Currently, it remains unclear about the efficacy of extended treatment duration among these patients. There is a possibility that patients with a high risk of late recurrence might benefit from extended endocrine therapy. Thus, identifying the patients with a high risk of late recurrence is of significance in improving treatment outcomes. Some researchers have explored prognostic factors of late recurrence in patients with HR⁺/HER2⁻ BC. As per a study of Yamashita et al., larger tumor size and more positive lymph nodes are associated with late recurrence in premenopausal patients. In postmenopausal patients, apart from those above-mentioned factors, low ER expression and short duration of endocrine therapy are associated with late recurrence as well (20). Ito et al. reported that larger tumor size, more lymph node involvement and higher tumor grade were related to late recurrence in ER⁺/HER2⁻ BC (24). Carvalho et al. reported PR was an independent prognostic factor for late BC recurrence (25). However, these studies couldn’t evaluate the risk of late recurrence quantitatively. Meanwhile, BC has heterogeneity, besides geographical factors, socioeconomic factors like lifestyle, environment and healthcare system may also contribute to BC outcomes. These research results abroad may not be applicable to Chinese. Therefore, a prediction model is established in this study to identify Chinese HR⁺/HER2⁻ BC patients with a high risk of late recurrence who might benefit from extended endocrine therapy. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1529/rc).

Methods

Patient selection

The clinical data of patients with histologically confirmed BC who were treated in the Department of Breast Oncology in Peking University Cancer Hospital between April 2007 and October 2019 were reviewed. The inclusion criteria were: (I) patients with positive HR and negative HER2 immunohistochemistry (IHC) results; (II) patients undergoing surgery; (III) patients with metastasis or recurrence after treatment. The exclusion criteria were as follows: (I) patients at stage IV at initial diagnosis; (II) patients with any other malignant tumor; (III) patients with incomplete information of ER, PR or HER2 status. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Peking University Cancer Hospital (No. 2017KT40). All patients provided written informed consent for using their medical information in the hospital database for the purpose of research.

IHC evaluation

ER, PR and Ki-67 was evaluated by IHC. HR was considered positive if there was ≥1% positive nuclear staining for either ER and/or PR. HER2 was considered positive if IHC score was 3+, or 2+ with fluorescent in situ hybridization showing gene amplification. HER2-positive patients were excluded from this study. According to the expression level of ER and PR, HR was categorized into four groups. Group 1 was defined as 1+ or <25%. Group 2 was defined as 2+ or 25–49%. Group 3 was defined as 3+ or 50–74%. Group 4 was defined as ≥75%. Similarly, Ki-67 was categorized into four groups according to the expression level as well.

Follow-up and survival outcomes

The follow-up was conducted through telephone, medical records of outpatient or inpatient. The last follow-up was performed at the end of January 2020. Besides, the data on clinicopathological factors, adjuvant therapies and metastatic characteristics were collected from medical records. The primary outcome was disease-free survival (DFS), which was defined as the duration from initial diagnosis to first metastasis or recurrence or death from any cause. Patients were divided into two groups according to DFS, namely the early recurrence group (DFS ≤5 years) and the late recurrence group (DFS >5 years).

Statistical analysis

SPSS 23.0, RevMan 5.3 and R 4.0.0 software were employed to conduct statistical analysis. DFS was evaluated by the Kaplan-Meier log-rank test. Chi-squared test was adopted to compare clinicopathological factors between the early and late recurrence groups. Multivariate analysis was performed through a logistic regression model to identify independent factors influencing the recurrence time. On the basis of potentially associated prognostic factors, a nomogram was constructed. The discrimination of the nomogram was evaluated by the receiver operating characteristic (ROC) curve and Concordance-index (C-index). Calibration was performed by the bootstrap with 1,000 resamples. P value <0.05 was considered as statistically significant.

Results

Patient characteristics

A total of 639 female patients with metastatic recurrence of BC were included in the study. The majority of patients (91.1%) were diagnosed after January 1, 2000. Demographic characteristics of these patients are listed in Table 1. Median age of these patients at initial diagnosis of primary tumors was 47 years old, including 263 patients (41.2%) ≥50 years old. A majority of patients (61.0%) were in premenopausal status at initial diagnosis. Most patients (528/639, 83.0%) received adjuvant therapy after surgery, while 17.0% (108/639) of them received neoadjuvant therapy before surgery. All patients received surgery. Mastectomy took a large proportion (93.6%), including radical and simple mastectomy. Forty patients (6.4%) received breast-conserving surgery. Invasive ductal carcinoma was the most common type of primary tumor (513/539, 84.1%). Most patients were treated with adjuvant/neoadjuvant chemotherapy (591/639, 92.5%) and endocrine therapy (505/639, 79.0%), while 273 patients (273/639, 42.7%) were treated with adjuvant radiotherapy. Three hundred and eighty-two patients (59.8%) were presented with early recurrence, and 257 patients (40.2%) were presented with late recurrence. The most common site of the first metastasis was bone (297/639, 46.5%), followed by lymph node (253/639, 39.6%). Lung metastasis, liver metastasis and brain metastasis took 24.7% (158/639), 16.1% (103/639) and 1.9% (12/639), respectively (Figure S1). Median DFS of these patients was 50.0 months (range, 3.0–324.0 months). The proportion of recurrence over time is shown in Figure S2. Approximately half of these patients (335/639, 52.4%) had a recurrence in the fourth year. During the follow-up of the first 5-year, the number of patients with recurrence increased rapidly. At the fifth year of the follow-up, 59.8% (382/639) of patients had a recurrence. Most (557/639, 87.2%) metastasis occur within 10 years during the follow-up.

Univariate analysis

Through univariate survival analysis for DFS, the following parameters were identified as significant factors (Figure 1), including age at diagnosis, hormonal status at diagnosis, treatment mode, histology of primary tumors, Scarff-Bloom-Richardson (SBR) grades, Ki-67 and the number of positive lymph nodes. Older age (≥50 years), postmenopausal status, higher SBR grade, higher level of Ki-67 and more positive lymph nodes were associated with shorter DFS. In addition, the patients treated with neoadjuvant therapy had an earlier recurrence than those without being treated before surgery (P=0.003). The level of hormonal receptor and tumor size had no significant impact on the recurrence time. Neoadjuvant/adjuvant chemo-/radiotherapy have no impact on recurrence time. Patients receiving chemotherapy or radiotherapy had more positive lymph nodes (P<0.001 for both) than those not receiving the treatment. Metastasis did not appear later in the neoadjuvant/adjuvant chemotherapy group and the adjuvant radiotherapy group even if they received treatment to delay recurrence.

Figure 1 Kaplan-Meier curves for recurrence-free survival. Univariate survival analysis for DFS showed the following parameters as significant factors: age at diagnosis, hormonal status at diagnosis, treatment mode, histology of primary tumor, SBR grade, Ki-67, number of positive lymph nodes. DFS, disease-free survival; SBR, Scarff-Bloom-Richardson.

Comparison results in the early recurrence and late recurrence groups are shown in Table 2 and Figure 2. Patients with early recurrence comprised 59.8% (382/639) of all patients, which was larger than the proportion of those with late recurrence (257/639). There were more patients who were in postmenopausal status at the initial diagnosis (42.6% vs. 33.6%), received neoadjuvant therapy (20.6% vs. 11.7%), had a higher level of Ki-67, had more positive lymph nodes, and did not receive radiotherapy (46.6% vs. 37.0%) in the early recurrence group compared with those in the late recurrence group. There was no significant difference in the proportion of patients receiving chemotherapy between both groups (P=0.48, due to the possible fact that there were more positive lymph nodes in patients treated with chemotherapy. In terms of the metastatic sites (Table 3), bone metastasis was most common in both groups. There were more patients with liver metastasis (19.4% vs. 11.3%, P=0.006) or chest/skin/soft tissue metastasis (28.8% vs. 21.4%, P=0.03) in the early recurrence group than the late recurrence group. While, there were more patients with pleura metastasis (5.0% vs. 12.8%, P<0.001) in the late recurrence group than the early recurrence group. There was no significant difference in bone metastasis, lymph node metastasis, lung metastasis and brain metastasis between both groups. Moreover, there was no significant difference in the number of the first metastatic site between both groups.

Figure 2 Forest plot in univariate analysis. Premenopausal, adjuvant therapy, less positive lymph nodes, lower level of Ki-67, not receiving radiotherapy were more common in late recurrence group. CI, confidence interval; M-H, Mantel-Haenszel.

Multivariate analysis

Logistic regression analysis was performed based on the results of univariate analysis and clinical experience. Age at initial diagnosis, hormonal status at initial diagnosis, treatment mode, type of surgery, expression level of Ki-67, N stage, neo-/adjuvant chemotherapy, neo-/adjuvant endocrine therapy and adjuvant radiotherapy were incorporated in logistic model, with the results listed in Table 4. Postmenopausal status, higher expression of Ki-67 and more positive lymph nodes were more common in patients with early recurrence than those with late recurrence (P=0.046, 0.003 and 0.02, respectively). The age of menopause in Chinese females is usually around 50 years old. Interaction analysis was performed in an attempt to explore the interaction between age and hormonal status. The result indicated that there was no synergistic effect of age and hormonal status on the recurrence time (P=0.66).

Construction and validation of the model

A logistic regression-based nomogram was constructed on the basis of potentially associated factors. Only those patients with complete data about associated factors could be incorporated to construct the nomogram. Due to the fact that there were many patients without the data on Ki-67 (Table 1), the above-mentioned factors except for the expression level of Ki-67 were adopted to construct Nomogram I at first (Figure 3). There were 563 patients in the construction setting of Nomogram I. C-index of Nomogram I was 0.637, and AUC was 0.635 (95% CI: 0.589–0.681). The internal validation was performed with the bootstrap resampling method. The calibration curve is shown in Figure S3. To optimize the model, considering the important role of Ki-67 in recurrence, Nomogram II was constructed by including the expression level of Ki-67 at last (Figure 4). There were 357 patients in the construction setting of Nomogram II. C-index of Nomogram II was 0.697, and AUC was 0.703 (95% CI: 0.643–0.763). The calibration curve is shown in Figure 5. The results in the calibration curve indicated that the absolute error and squared error were smaller in Nomogram II than in Nomogram Ⅰ. Besides, the calibration curve in Nomogram II showed the prediction (solid line) was closer to the 45-degree line than that in Nomogram I. Moreover, the ROC curve showed that Nomogram II exhibited better discrimination to predict the probability of late recurrence than Nomogram I. From the nomogram, the probability of late recurrence in every patient could be easily obtained through calculating the total scores of clinicopathological factors.

Figure 3 Nomogram I. Age at initial diagnosis, hormonal status at initial diagnosis, treatment mode, type of surgery, positive lymph nodes, neo-/adjuvant chemotherapy, neo-/adjuvant endocrine therapy and adjuvant radiotherapy were used to construct logistic regression-based Nomogram I. Through counting the scores of each factors, a total point would be attained. The total point would correspond to an exact risk of late-recurrence.

Figure 4 Nomogram II. Age at initial diagnosis, hormonal status at initial diagnosis, treatment mode, type of surgery, positive lymph nodes, level of Ki-67, neo-/adjuvant chemotherapy, neo-/adjuvant endocrine therapy and adjuvant radiotherapy were used to construct logistic regression-based Nomogram II. Through counting the scores of each factors, a total point would be attained. The total point would correspond to an exact risk of late-recurrence.

Figure 5 Calibration curve of Nomogram II. B stands for bootstrap. Internal validation was performed for Nomogram II using bootstrap resampling method, and calibration curve was depicted as above. In internal validation, 1,000 repetitions of resampling were carried out. Based on the results of internal validation, calibration curve was depicted. The closer the curve is to the diagonal line, the more reliable the nomogram is. Absolute error and squared error were used to describe difference between predictive value and actual value. The smaller the error is, the more reliable the nomogram is.

Discussion

Late recurrence is not only observed in HR⁺ BC, but also frequently found in many other solid tumors, such as thyroid cancer and melanoma. At present, it remains unclear about the mechanism of late recurrence. As is reported by some researchers, late recurrence is partly induced by changes of the immune microenvironment in primary tumors and tumor dormancy (26-32). Although late recurrence remains a tough problem for patients with HR⁺ BC, it has been proven by accumulating evidence that extending the duration of endocrine therapy could reduce the risk of late recurrence (12). Therefore, there is a demand to identify patients at high risk of late recurrence and formulate a more appropriate treatment plan for these patients. In this study, some possible factors influencing the recurrence time of patients with HR⁺/HER2⁻ BC were revealed, and a model has been established to predict the risk of late recurrence.

There are still some controversies over the impact of menstrual status on recurrence risk and recurrence time. As reported by Ditsatham et al., patients in premenopausal status have a higher risk of recurrence than those in postmenopausal status (33). In another study, there is no significant difference in menstrual status between patients with early and late recurrence (34). In this study, menstrual status is an important factor affecting the recurrence time of BC patients in both univariate and multivariate analyses. Patients in premenopausal status have later relapse than those in postmenopausal status. This phenomenon is probably caused by comorbid condition, tumor burden and immune microenvironment (28,35). Older postmenopausal patients are more likely to suffer from serious comorbid conditions than younger premenopausal patients. In addition, there might be a significant difference in the immune microenvironment between the premenopausal and postmenopausal patients. As a result, although premenopausal patients have a higher level of estrogen, they might have a later recurrence than postmenopausal patients.

According to some previous studies, lymph node status could provide predictive information for late recurrence of BC (36-38). The risk of late recurrence, especially distant recurrence, would increase when there are more positive lymph nodes (39). Takeshi et al. found that the risk of late recurrence would increase significantly when there are more than three positive lymph nodes. The same results were reported by another study as well (17,40). In this study, the number of positive lymph nodes more than 3 is more frequently observed in patients with early recurrence than those with late recurrence. As shown in Table 2, in the group which patients has 4–9 positive lymph nodes and the group ≥10 positive lymph nodes, there are more early recurrence than late recurrence (22.1% vs. 19.7% and 22.6% vs. 12.7%, respectively). The results of this study on the influence of positive lymph nodes on recurrence time is consistent with the findings of previous studies.

It is still under debate whether Ki-67 is an independent prognostic factor. The role of Ki-67 in predicting the prognosis of BC has been reported in some studies (41-44), while this predictive effect is not reported in another study. As is revealed in a study on the late recurrence of BC, a high level of Ki-67 is associated with short DFS in univariate analysis, but it’s not an independent prognostic factor in multivariate analysis (17). In this study, there is a significant difference in the level of Ki-67 between the early and late recurrence groups, and a high level of Ki-67 is more frequently observed in patients with early recurrence. After excluding the potential influence of other factors in multivariate analysis, Ki-67 can be determined as an independent factor affecting the recurrence time of BC. In our opinion, as Ki-67 is a marker of cell proliferation, it’s easy to understand its role in predicting recurrence time.

The impact of treatment modes on the recurrence time is rarely discussed in previous studies. As per a study of Akrami et al., chemotherapy before surgery is not related to the recurrence time (45). In this study, neoadjuvant therapy and adjuvant therapy are defined as the two treatment modes according to the time when patients receive treatment, either before surgery or after surgery. It has been demonstrated in this study that neoadjuvant therapy is more frequently observed in patients with early recurrence in univariate analysis, but there is no significant difference in multivariate analysis. The reason might be that patients with heavier tumor burden are more likely to undergo neoadjuvant therapy, and despite the patients have received treatment before surgery, the recurrence may not be delayed. This phenomenon reflects the fact that characteristics of primary tumors would significantly affect the recurrence time despite of aggressive treatment.

As revealed from a study of Akrami et al., adjuvant radiotherapy is a predictive factor of late recurrence (45). While, in another study, recurrence time is not associated with adjuvant radiotherapy (46). In univariate analysis of our study, adjuvant radiotherapy is more frequently observed in the late recurrence group than in the early recurrence group. However, in multivariate analysis, there was no significant difference in radiotherapy between both groups. For the reason that adjuvant radiotherapy is related to some clinical factors, such as breast-conserving surgery and the number of positive lymph nodes, an exploration is conducted to further explore the relationship between type of surgery, lymph node metastasis and adjuvant radiotherapy. The results suggest that adjuvant radiotherapy is not associated with type of surgery, but is associated with the number of positive lymph nodes. There are more positive lymph nodes in patients treated with adjuvant radiotherapy. Thus, the role of adjuvant radiotherapy in recurrence might have some relationship with more positive lymph nodes in whom received adjuvant radiotherapy. Recurrence is distinctly influenced by positive lymph nodes, and thus recurrence is not delayed even if they receive radiotherapy.

According to the results of univariate and multivariate analyses, a nomogram is constructed with R software. This model is optimized for obtaining better predictive value with a higher AUC value of 0.703 and a better calibration curve. Age, hormonal status, positive lymph nodes, Ki-67, treatment mode, type of surgery, radiotherapy, chemotherapy and endocrine therapy are incorporated in this model. It can be used to guide further research into the use of extended treatment for patients identified with a high risk of late recurrence. This nomogram could serve as a tool for risk stratification in prospective clinical trials. Such trials could investigate whether patients identified as high-risk by the nomogram derive greater benefit from extended adjuvant endocrine therapy. Furthermore, future studies should focus on the external validation of this model in independent, multi-center Chinese cohorts to verify its generalizability before widespread clinical application.

There are some advantages in this study. First, the sample size is relatively large, which ensures the reliability of the results. Second, patients diagnosed in recent 20 years are included in this study, which guarantees a relatively long-term follow-up. Besides, a comprehensive review is conducted on the medical records, which contributes to clarifying clinicopathological factors influencing the recurrence time. Meanwhile, a visual model is established in this study, which conduces to thoroughly understanding and accurately calculating the risk of late recurrence. The focus of most previous studies is placed on exploring the factors affecting recurrence or recurrence time (20,36-38). There are few studies establishing a model to predict the risk of recurrence time. Therefore, this model might be more helpful for the decision-making of medical management. Although the predictive efficacy of existing gene index for early or late recurrence has been verified in some studies (47-49), not all patients could afford gene test. Predicting the risk based on clinical factors is of great significance for patients in general. There are also some limitations in this study. First, this is a retrospective study with selection bias and some incomplete data. Second, histological grade, an important prognostic factor, is not available in half of these patients, so the results on histological grade should be interpreted more carefully. Third, our model is based solely on clinicopathological factors. A key limitation is the lack of incorporation of gene expression data (e.g., from assays like Oncotype DX or MammaPrint) or other emerging biomarkers, which have been shown to provide significant independent prognostic information for late recurrence. Future work should aim to integrate these molecular features with clinical parameters to build a more comprehensive and potentially more accurate prediction tool. Furthermore, the nomogram is constructed and validated based on the data from the same center. Another population from a different center is required to externally validate the effectiveness of the nomogram.

Currently, there are an increasing number of studies focusing on the role of genes and biomarkers in predicting the risk of late recurrence apart from clinicopathological factors, such as Oncotype DX, MammaPrint, BC Index, circulating tumor cell and circulating tumor DNA, which have the potential to optimize the candidates for extended endocrine therapy (36,47,48,50-59). The combination of clinicopathological factors, genes and biomarkers may supplement significant prognostic information for late recurrence of BC, and provide more useful information for future work.

Conclusions

It can be concluded from this study that HR⁺/HER2⁻ BC patients with premenopausal status at initial diagnosis, less positive lymph nodes and a lower level of Ki-67 are more likely to have a late recurrence. A well-calibrated nomogram is constructed to predict late recurrence of patients with HR⁺/HER2⁻ BC. This nomogram could be employed to predict the risk of late recurrence and can be used to guide further research into the use of extended endocrine therapy for patients with high risk of late recurrence. This model shall be further validated through prospectively designed studies. Combination of clinical factors and other markers may provide more prognostic information.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1529/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1529/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1529/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1529/coif). The authors have no conflicts of interest to declare.

Ethical Statement: 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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Peking University Cancer Hospital (No. 2017KT40). All patients provided written informed consent for using their medical information in the hospital database for the purpose of research.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Sun D, Cao M, Li H, et al. Cancer burden and trends in China: A review and comparison with Japan and South Korea. Chin J Cancer Res 2020;32:129-39. [Crossref] [PubMed]
2. Saphner T, Tormey DC, Gray R. Annual hazard rates of recurrence for breast cancer after primary therapy. J Clin Oncol 1996;14:2738-46. [Crossref] [PubMed]
3. Colleoni M, Sun Z, Price KN, et al. Annual Hazard Rates of Recurrence for Breast Cancer During 24 Years of Follow-Up: Results From the International Breast Cancer Study Group Trials I to V. J Clin Oncol 2016;34:927-35. [Crossref] [PubMed]
4. Gonzalez-Angulo AM, Morales-Vasquez F, Hortobagyi GN. Overview of resistance to systemic therapy in patients with breast cancer. Adv Exp Med Biol 2007;608:1-22. [Crossref] [PubMed]
5. Murphy CG, Dickler MN. Endocrine resistance in hormone-responsive breast cancer: mechanisms and therapeutic strategies. Endocr Relat Cancer 2016;23:R337-52. [Crossref] [PubMed]
6. Brufsky AM, Dickler MN. Estrogen Receptor-Positive Breast Cancer: Exploiting Signaling Pathways Implicated in Endocrine Resistance. Oncologist 2018;23:528-39. [Crossref] [PubMed]
7. Lobo-Martins S, Arecco L, Cabral TP, et al. Extended adjuvant endocrine therapy in early breast cancer: finding the individual balance. ESMO Open 2025;10:105057. [Crossref] [PubMed]
8. Tsai TC, Miller J, Andolfi C, et al. Surgical evaluation of lymph nodes in esophageal adenocarcinoma: Standardized approach or personalized medicine? Eur J Surg Oncol 2018;44:1177-80.
9. Sparano JA, Zhao F, Martino S, et al. Long-Term Follow-Up of the E1199 Phase III Trial Evaluating the Role of Taxane and Schedule in Operable Breast Cancer. J Clin Oncol 2015;33:2353-60. [Crossref] [PubMed]
10. Snoj N, Paridaens R, Cufer T. Current controversies in extended adjuvant endocrine therapy for early breast cancer. Curr Opin Oncol 2008;20:627-33. [Crossref] [PubMed]
11. Adachi M, Ishiba T, Maruya S, et al. Clinical Information and Prognosis of High-risk Luminal Breast Cancer Subjects Eligible for the MonarhE Study. JMA J 2025;8:486-97. [Crossref] [PubMed]
12. Chen X, Fan Y, Xu B. Distinct Characteristics and Metastatic Behaviors of Late Recurrence in Patients With Hormone Receptor-positive/Human Epidermal Growth Factor Receptor 2-negative Breast Cancer: A Single Institute Experience of More Than 10 Years. Clin Breast Cancer 2018;18:e1353-60. [Crossref] [PubMed]
13. Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. Lancet 2015;386:1341-52. [Crossref] [PubMed]
14. Palmieri C, Jones A. The 2011 EBCTCG polychemotherapy overview. Lancet 2012;379:390-2. [Crossref] [PubMed]
15. Jatoi I, Anderson WF, Jeong JH, et al. Breast cancer adjuvant therapy: time to consider its time-dependent effects. J Clin Oncol 2011;29:2301-4. [Crossref] [PubMed]
16. Richman J, Dowsett M. Beyond 5 years: enduring risk of recurrence in oestrogen receptor-positive breast cancer. Nat Rev Clin Oncol 2019;16:296-311. [Crossref] [PubMed]
17. Song F, Zhang J, Li S, et al. ER-positive breast cancer patients with more than three positive nodes or grade 3 tumors are at high risk of late recurrence after 5-year adjuvant endocrine therapy. Onco Targets Ther 2017;10:4859-67. [Crossref] [PubMed]
18. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005;365:1687-717. [Crossref] [PubMed]
19. Goss PE, Ingle JN, Pater JL, et al. Late extended adjuvant treatment with letrozole improves outcome in women with early-stage breast cancer who complete 5 years of tamoxifen. J Clin Oncol 2008;26:1948-55. [Crossref] [PubMed]
20. Yamashita H, Ogiya A, Shien T, et al. Clinicopathological factors predicting early and late distant recurrence in estrogen receptor-positive, HER2-negative breast cancer. Breast Cancer 2016;23:830-43. [Crossref] [PubMed]
21. Davies C, Pan H, Godwin J, et al. Long-term effects of continuing adjuvant tamoxifen to 10 years versus stopping at 5 years after diagnosis of oestrogen receptor-positive breast cancer: ATLAS, a randomised trial. Lancet 2013;381:805-16. [Crossref] [PubMed]
22. Jakesz R, Greil R, Gnant M, et al. Extended adjuvant therapy with anastrozole among postmenopausal breast cancer patients: results from the randomized Austrian Breast and Colorectal Cancer Study Group Trial 6a. J Natl Cancer Inst 2007;99:1845-53. [Crossref] [PubMed]
23. Goss PE, Ingle JN, Pritchard KI, et al. Extending Aromatase-Inhibitor Adjuvant Therapy to 10 Years. N Engl J Med 2016;375:209-19. [Crossref] [PubMed]
24. Ito M, Amari M, Sato A, et al. Risk factors for late recurrence and postrelapse survival in estrogen receptor (ER)-positive, human epidermal growth factor receptor (HER) 2-negative breast cancer after 5 years of endocrine therapy. Breast 2024;73:103604. [Crossref] [PubMed]
25. Carvalho GS, Gomes DM, Bretas GO, et al. Late Breast Cancer Recurrence Prediction: The Role of CTS5 and Progesterone Receptor Status. Breast Cancer (Dove Med Press) 2025;17:683-91. [Crossref] [PubMed]
26. Uhr JW, Pantel K. Controversies in clinical cancer dormancy. Proc Natl Acad Sci U S A 2011;108:12396-400. [Crossref] [PubMed]
27. Gelao L, Criscitiello C, Fumagalli L, et al. Tumour dormancy and clinical implications in breast cancer. Ecancermedicalscience 2013;7:320. [Crossref] [PubMed]
28. Takeshita T, Yan L, Asaoka M, et al. Late recurrence of breast cancer is associated with pro-cancerous immune microenvironment in the primary tumor. Sci Rep 2019;9:16942. [Crossref] [PubMed]
29. Heindl A, Sestak I, Naidoo K, et al. Relevance of Spatial Heterogeneity of Immune Infiltration for Predicting Risk of Recurrence After Endocrine Therapy of ER+ Breast Cancer. J Natl Cancer Inst 2018; [Crossref]
30. Louis M, Tapia R, Grabill N, et al. Dormancy Leading to Late Recurrence in Breast Cancer: A Case of Hormone Receptor-Positive Supraclavicular Metastasis 10 Years After the Initial Treatment. Cureus 2024;16:e71586. [Crossref] [PubMed]
31. Dowling RJO, Sparano JA, Goodwin PJ, et al. Toronto Workshop on Late Recurrence in Estrogen Receptor-Positive Breast Cancer: Part 2: Approaches to Predict and Identify Late Recurrence, Research Directions. JNCI Cancer Spectr 2019;3:pkz049.
32. Goss P, Allan AL, Rodenhiser DI, et al. New clinical and experimental approaches for studying tumor dormancy: does tumor dormancy offer a therapeutic target? APMIS 2008;116:552-68. [Crossref] [PubMed]
33. Ditsatham C, Somwangprasert A, Watcharachan K, et al. Factors affecting local recurrence and distant metastases of invasive breast cancer after breast-conserving surgery in Chiang Mai University Hospital. Breast Cancer (Dove Med Press) 2016;8:47-52. [Crossref] [PubMed]
34. Chen XL, Fan Y, Xu BH. Clinicopathological features and prognosis of HER2-negative luminal-type breast cancer patients with early and late recurrence. Zhonghua Zhong Liu Za Zhi 2016;38:448-53. [Crossref] [PubMed]
35. Tu KJ, Guerrero-Romero D, Eason K, et al. The tumor microenvironment of 14,837 breast cancers is associated with clinical outcome independently of genomic subtypes. Cell Rep Med 2025;6:102450. [Crossref] [PubMed]
36. Sestak I, Dowsett M, Zabaglo L, et al. Factors predicting late recurrence for estrogen receptor-positive breast cancer. J Natl Cancer Inst 2013;105:1504-11. [Crossref] [PubMed]
37. Nishimura R, Osako T, Nishiyama Y, et al. Evaluation of factors related to late recurrence--later than 10 years after the initial treatment--in primary breast cancer. Oncology 2013;85:100-10. [Crossref] [PubMed]
38. Payandeh M, Sadeghi M, Sadeghi E. Differences in Prognostic Factors between Early and Late Recurrence Breast Cancers. Asian Pac J Cancer Prev 2015;16:6575-9. [Crossref] [PubMed]
39. Lee ES, Han W, Kim MK, et al. Factors associated with late recurrence after completion of 5-year adjuvant tamoxifen in estrogen receptor positive breast cancer. BMC Cancer 2016;16:430. [Crossref] [PubMed]
40. Murata T, Jinno H, Takahashi M, et al. Clinicopathologic features of hormone-receptor-positive breast cancer patients with late recurrence. Breast J 2019;25:9-15. [Crossref] [PubMed]
41. Trihia H, Murray S, Price K, et al. Ki-67 expression in breast carcinoma: its association with grading systems, clinical parameters, and other prognostic factors--a surrogate marker? Cancer 2003;97:1321-31. [Crossref] [PubMed]
42. Conforti F, Pala L, Pagan E, et al. Endocrine-responsive lobular carcinoma of the breast: features associated with risk of late distant recurrence. Breast Cancer Res 2019;21:153. [Crossref] [PubMed]
43. Pinto AE, André S, Pereira T, et al. Prognostic comparative study of S-phase fraction and Ki-67 index in breast carcinoma. J Clin Pathol 2001;54:543-9. [Crossref] [PubMed]
44. Williams DJ, Cohen C, Darrow M, et al. Proliferation (Ki-67 and phosphohistone H3) and oncotype DX recurrence score in estrogen receptor-positive breast cancer. Appl Immunohistochem Mol Morphol 2011;19:431-6. [Crossref] [PubMed]
45. Akrami M, Arasteh P, Eghbali T, et al. Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different? World J Surg Oncol 2018;16:185. [Crossref] [PubMed]
46. Grieb BC, Agarwal R. HER2-Directed Therapy in Advanced Gastric and Gastroesophageal Adenocarcinoma: Triumphs and Troubles. Curr Treat Options Oncol 2021;22:88. [Crossref] [PubMed]
47. Sgroi DC, Sestak I, Cuzick J, et al. Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: a prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population. Lancet Oncol 2013;14:1067-76. [Crossref] [PubMed]
48. Zhang Y, Schnabel CA, Schroeder BE, et al. Breast cancer index identifies early-stage estrogen receptor-positive breast cancer patients at risk for early- and late-distant recurrence. Clin Cancer Res 2013;19:4196-205. [Crossref] [PubMed]
49. Mojarad S, Venturini B, Fulgenzi P, et al. Prediction of nodal metastasis and prognosis of breast cancer by ANN-based assessment of tumour size and p53, Ki-67 and steroid receptor expression. Anticancer Res 2013;33:3925-33.
50. Sparano JA, Paik S. Development of the 21-gene assay and its application in clinical practice and clinical trials. J Clin Oncol 2008;26:721-8. [Crossref] [PubMed]
51. Wolmark N, Mamounas EP, Baehner FL, et al. Prognostic Impact of the Combination of Recurrence Score and Quantitative Estrogen Receptor Expression (ESR1) on Predicting Late Distant Recurrence Risk in Estrogen Receptor-Positive Breast Cancer After 5 Years of Tamoxifen: Results From NRG Oncology/National Surgical Adjuvant Breast and Bowel Project B-28 and B-14. J Clin Oncol 2016;34:2350-8. [Crossref] [PubMed]
52. Sestak I, Cuzick J, Dowsett M, et al. Prediction of late distant recurrence after 5 years of endocrine treatment: a combined analysis of patients from the Austrian breast and colorectal cancer study group 8 and arimidex, tamoxifen alone or in combination randomized trials using the PAM50 risk of recurrence score. J Clin Oncol 2015;33:916-22. [Crossref] [PubMed]
53. Sparano J, O'Neill A, Alpaugh K, et al. Association of Circulating Tumor Cells With Late Recurrence of Estrogen Receptor-Positive Breast Cancer: A Secondary Analysis of a Randomized Clinical Trial. JAMA Oncol 2018;4:1700-6. [Crossref] [PubMed]
54. Garcia-Murillas I, Schiavon G, Weigelt B, et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci Transl Med 2015;7:302ra133. [Crossref] [PubMed]
55. Rohanizadegan M. Analysis of circulating tumor DNA in breast cancer as a diagnostic and prognostic biomarker. Cancer Genet 2018;228-229:159-68. [Crossref] [PubMed]
56. Sestak I, Cuzick J. Markers for the identification of late breast cancer recurrence. Breast Cancer Res 2015;17:10. [Crossref] [PubMed]
57. Ajjawi I, Rozenblit M, Rios-Hoyo A, et al. Prognostic Value of Circulating Tumor DNA in HR+/HER2- Stage I-III Breast Cancer: A Systematic Review. Cancers (Basel) 2025;17:2831. [Crossref] [PubMed]
58. van 't Veer LJ, Meershoek-Klein Kranenbarg E, Duijm-de Carpentier M, et al. Selection of Patients With Early-Stage Breast Cancer for Extended Endocrine Therapy: A Secondary Analysis of the IDEAL Randomized Clinical Trial. JAMA Netw Open 2024;7:e2447530. [Crossref] [PubMed]
59. Foldi J, O'Meara T, Marczyk M, et al. Defining Risk of Late Recurrence in Early-Stage Estrogen Receptor-Positive Breast Cancer: Clinical Versus Molecular Tools. J Clin Oncol 2019;37:1365-9. [Crossref] [PubMed]