Development and validation of nomograms predicting survival in operable breast cancer patients at reproductive age after breast conserving surgery and postoperative radiotherapy based on SEER database

Introduction

Breast cancer (BC) is the most commonly diagnosed malignancy and one of the major causes of cancer-related mortality globally, with a higher incidence in women (1,2). While the risk of BC increases with age, particularly in women over 50 years, it remains the most common malignancy among younger women of reproductive age (<45 years). In fact, over 40% of cancers diagnosed in women aged 45 and younger are BCs (3). Age is a critical determinant of long-term BC survival, with younger patients typically facing a worse prognosis than older age groups (4,5). Furthermore, several studies have shown that BC in young patients is often more aggressive, characterized by factors such as poorer tumor grade, more frequent breast cancer susceptibility gene 1/2 (BRCA1/2) mutations and lymphovascular invasion, all of which are associated with a poorer prognosis (6). The recent report indicates that the BC incidence rates among young women are 0.1, 5.7, and 46.6 per 100,000 for those aged 15–19, 20–29, and 30–39 years, respectively (7). Moreover, approximately 43% and 7% of young women present with regional or distant-stage disease, while BC-specific mortality reaches 22% in those aged 15–45 years (8).

Operable breast cancer (OBC) encompassing stages I, II, and III, represents more than 90% of all BC diagnoses (9). Despite the availability of curative treatment options for OBC—including surgery, radiation, chemotherapy, and adjuvant endocrine therapy—more than 30% of patients will experience recurrence, often with distant metastases, which remains incurable (10,11). Advancements in surgical techniques, improved collaboration with pathology, and the integration of radiotherapy and systemic therapies have led to increasing recognition of breast-conserving surgery (BCS) for its safety, cosmetic outcomes, reduced complications, and enhanced quality of life, as endorsed by both patients and surgeons (12). BCS combined with postoperative radiotherapy has become the standard surgical approach for the majority of early-stage BC cases (13). However, some studies have identified that patients with high-risk clinical features—such as larger tumor size, more advanced stage, extensive axillary lymph node involvement, and poorer histologic grade—are at an elevated risk of recurrence (14,15). Furthermore, some patients with early-stage and locally advanced BC may benefit from neoadjuvant chemotherapy, as tumor response during and after treatment can be assessed and guide subsequent treatment decisions (16).Thus, identifying patients at high risk of recurrence can help optimize treatment strategies (17,18).

To our knowledge, no systematic study has yet identified survival risk factors in OBC patients of reproductive age. Thus, this study aims to identify independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in this cohort, developing a visual predictive model to enable high-risk patient identification for personalized management. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1059/rc).

Methods

Data collection and patient selection

Patients with BC were identified from the Surveillance, Epidemiology, and End Results (SEER) Research Plus Data 22 registry (2000–2019). To ensure staging consistency using the 7th American Joint Committee on Cancer (AJCC) system, we restricted analysis to cases diagnosed between January 2010 and December 2016. Inclusion criteria comprised: (I) age 20–45 years; (II) completion of BCS with postoperative radiotherapy; (III) tumor, node, metastasis (TNM) stage T1–3N0–1M0. This cohort enabled analysis of survival rates and prognostic factors.

Each patient’s comprehensive information encompassed a range of age, race, laterality, TNM stage, grade, location, histological type, lymph node metastasis, BC subtype, tumor size, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, cause-specific death, tumor sequence, response to neoadjuvant therapy and survival months (more than 0 days of survival). Specifically, patients confirmed as achieving pathologic complete response (CR) upon postoperative specimen analysis are defined as having a CR. Patients stated as having no response or partial response are defined as having a non-complete response (NCR).

Patients were randomly allocated to training and validation cohorts (7:3 ratio) to ensure analytical robustness. Exclusion criteria comprised: (I) incomplete survival/follow-up data; (II) zero survival time or missing survival data. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Statistical analysis

Through the utilization of Cox regression models, we performed calculations to determine a 95% confidence interval (CI) and hazard ratio (HR). In order to identify potential prognostic factors, those showing significant differences in the univariate Cox regression analysis were further examined through multivariate analysis.

Using R software, we developed multivariate analysis-based nomograms to predict 3-, 5-, and 7-year OS and CSS. Model discrimination was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and area under the curve (AUC). Calibration plots compared predicted versus observed survival. Clinical utility was assessed via decision curve analysis (DCA), quantifying net benefits across threshold probabilities. The development cohort was segmented into risk groups based on total points, and survival differences were analyzed using the Kaplan-Meier method with log-rank tests. Propensity score matching (PSM) was applied to evaluate clinical intervention effects on outcomes. Statistical analyses used R (v3.6.1) and Statistical Package for the Social Sciences​(SPSS v25.0; International Business Machines Corporation). The “rms”, “survival”, “magick”, “timeROC”, “ggplotify”, and “cowplot” R packages facilitated nomogram development and validation. Statistical significance was defined as P<0.05.

Results

Baseline clinical features

In summary, this study included 9,477 women patients, categorized into a training cohort of 6,633 and a test cohort of 2,844. The majority of patients are between the ages of 40 to 44 years, with a median survival time of 66 months. All patients underwent BCS with postoperative radiotherapy, with more than half (61.06%) receiving chemotherapy. The majority of tumors were classified as grade III/IV (poorly differentiated and undifferentiated), accounting for 41.19% of cases, while only 18.99% and 39.81% of patients exhibited grade I (well-differentiated) and grade II (moderately differentiated) tumors, respectively. Infiltrating ductal carcinoma was the predominant histologic subtype, observed in 85.92% of patients. Chemotherapy was administered to over half of the patients (61.06%). The most common BC subtype was hormone receptor-positive (HR+)/HER2-negative (HER2−) (68.19%). ER positivity was present in 80.16% of cases, and PR positivity in 74.52%, while HER2 negativity was found in the majority (82.69%). In terms of tumor staging, stage I was the most frequent (53.76%), with T1 being the most common T stage (61.89%) and N0 being the most prevalent N stage (74.29%). Basic characteristics and variance analyses are detailed in Table 1. No significant differences in feature distributions were found between the training and test sets using Mann-Whitney U and Chi-squared (χ²) tests.

Variable feature importance of survival prediction

To identify prognostic factors for OS, we conducted univariate and multivariate Cox regression analyses in the training set (Table 2). Both analyses indicated several risk factors for OS outcomes: grade, PR status, HER2 status, tumor sequence, marital status, T stage, and N stage. Specifically, moderately differentiated (HR =2.79; 95% CI: 1.48–5.26), poorly differentiated and undifferentiated (HR =4.46; 95% CI: 2.35–8.45), unmarried (HR =4.46; 95% CI: 2.35–8.45), T2 stage (HR =1.90; 95% CI: 1.03–3.51), T3 stage (HR =2.11; 95% CI: 1.63–2.74) and N1 stage (HR =1.96; 95% CI: 2.35–8.45). In contrast, PR-positive status (HR =0.59; 95% CI: 0.45–0.76), HER2-positive status (HR =0.54; 95% CI: 0.39–0.76) and only one primary tumor (HR =0.43; 95% CI: 0.28–0.68) were linked to improved OS.

For CSS, independent prognostic factors included grade, PR status, HER2 status, tumor sequence, T stage and N stage (Table 3). Notably, moderately differentiated (HR =3.43; 95% CI: 1.47–8.00), poorly differentiated and undifferentiated (HR =6.01; 95% CI: 2.57–14.04), T2 stage (HR =2.54; 95% CI: 1.87–3.43), T3 stage (HR =3.44; 95% CI: 1.93–6.11) and N1 stage (HR =2.25; 95% CI: 1.73–2.91) correlated with poorer CSS. Furthermore, PR-positive status (HR =0.50; 95% CI: 0.37–0.68), HER2-positive status (HR =0.36; 95% CI: 0.23–0.56) only one primary tumor (HR =0.48; 95% CI: 0.29–0.79) were associated with better CSS outcomes.

Nomogram construction

Based on multivariate analysis of the training cohort, we constructed a nomogram predicting OS and CSS (Figures 1,2). Each prognostic factor was assigned a score between 0 and 100, reflecting its impact on the model’s predictive accuracy. By aggregating these scores for each patient, we derived a total point value that facilitated the estimation of 3-, 5- and 7-year OS and CSS probabilities. Importantly, higher total scores were associated with a worse prognosis for patients.

Figure 1 Nomogram for OS prediction in the patients. HER2, human epidermal growth factor receptor 2; N, lymph node; OS, overall survival; PR, progesterone receptor; T, tumor.

Figure 2 Nomogram for CSS prediction in the patients. CSS, cancer-specific survival; HER2, human epidermal growth factor receptor 2; N, lymph node; PR, progesterone receptor; T, tumor.

Model validation

In the training cohort, the OS nomogram achieved a significantly higher C-index than the AJCC 7th staging system [0.77 (95% CI: 0.75–0.80) vs. 0.69 (95% CI: 0.66–0.71)]. Time-dependent ROC analysis yielded AUCs of 0.81 (3-year), 0.78 (5-year), and 0.76 (7-year) (Figure 3). Validation confirmed these findings: C-index =0.75 (0.73–0.77) with corresponding 3-/5-/7-year AUCs of 0.81, 0.76 and 0.76 (Figure 3). Calibration plots demonstrated excellent agreement between predicted and observed outcomes in both cohorts (training: Figure 4A-4C; validation: Figure 4D-4F). DCA revealed superior clinical utility of the nomogram over AJCC 7th staging in training (Figure 5A) and validation sets (Figure 5B).

Figure 3 ROC curves of the nomogram for 3-year (A), 5-year (B) and 7-year (C) OS in the training set and validation set. AUC, area under the curve; CI, confidence interval; OS, overall survival; ROC, receiver operating characteristic.

Figure 4 Calibration plots of the training set (A-C) and the validation set (D-F) for 3-year (A,D), 5-year (B,E) and 7-year (C,F) OS. OS, overall survival.

Figure 5 Decision curve analysis of nomogram and AJCC 7th staging system for the OS prediction in the training cohort (A) and validation cohort (B). None: none of the patients have a bad outcome; All: bad outcomes occur in all patients. AJCC, American Joint Committee on Cancer; OS, overall survival.

The CSS nomogram demonstrated robust validation. In the training cohort, its C-index reached 0.82 (95% CI: 0.80–0.84), exceeding the AJCC system’s 0.62 (95% CI: 0.60–0.66). Time-dependent ROC analysis yielded AUCs of 0.86 (3-year), 0.82 (5-year), and 0.81 (7-year) for CSS (Figure S1). Validation cohort results confirmed this superiority: C-index =0.78 (95% CI: 0.74–0.81) with corresponding 1-/3-/5-year AUCs of 0.739, 0.730, and 0.730 (Figure S1). Calibration plots showed high concordance between predicted and observed outcomes in both cohorts (training: Figure S2A-S2C; validation: Figure S2D-S2F). DCA of the training set confirmed the model’s superior clinical utility (Figure S3A), and DCA of the validation cohort demonstrated superior clinical applicability versus the AJCC system (Figure S3B).

Therapeutic efficacy across risk-stratified subgroups

Patients were categorized into three risk groups based on scores derived from the X-tile prediction model: low-risk (OS <159, CSS <202), middle-risk (159< OS <228, 202< CSS <250), and high-risk (OS ≥228, CSS ≥250) (Figure S4). Kaplan-Meier analysis revealed significant survival differences among these groups, with higher-risk patients showing poorer OS and CSS, while the low-risk group exhibited improved outcomes (Figure 6). After PSM, we assessed differential treatment effects on OS and CSS in subgroup analyses (Tables S1-S12). Chemotherapy did not provide significant survival benefits in either the low- or middle-risk groups (Figure 7), and in fact, worsened outcomes in the low-risk group. In the middle-risk group, comparison of chemotherapy modalities revealed that adjuvant chemotherapy did not significantly improve OS or CSS (Figure 8). In contrast, neoadjuvant chemotherapy showed a notable benefit for OS in patients with CR, but not in those with NCR (Figure 8). Interestingly, in the high-risk group, NCR patients had better OS and CSS with adjuvant chemotherapy compared to neoadjuvant treatment, while CR patients fared better with neoadjuvant chemotherapy (Figure 9).

Figure 6 Kaplan-Meier survival curves with different risk group stratified by the nomogram. Kaplan-Meier OS (A) and CSS (B) curves with three risk groups stratified by the nomogram. CSS, cancer-specific survival; OS, overall survival.

Figure 7 The adjuvant chemotherapy effects in the low-risk and middle-risk groups. The comparison between patients who received adjuvant chemotherapy and those who did not receive adjuvant chemotherapy in the low-risk group for OS (A) and CSS (B). The comparison between patients who received adjuvant chemotherapy and those who did not receive adjuvant chemotherapy in the middle-risk group for OS (C) and CSS (D). CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; OS, overall survival.

Figure 8 Kaplan-Meier analysis for different chemotherapy strategies in middle-risk patients group. Comparative analysis of chemotherapy recipients versus non-recipients in the middle-risk group: OS (A) and CSS (D). The comparison between patients who received neoadjuvant chemotherapy and those who did not receive neoadjuvant chemotherapy in the middle-risk patients group of NCR for OS (B) and CSS (E). The comparison between patients who received adjuvant chemotherapy and those who receive neoadjuvant chemotherapy in the middle-risk patients group of CR for OS (C) and CSS (F). CI, confidence interval; CR, complete response; CSS, cancer-specific survival; HR, hazard ratio; NCR, non-complete response; OS, overall survival.

Figure 9 Kaplan-Meier analysis for different chemotherapy strategies in the high-risk patients group. Adjuvant vs. neoadjuvant chemotherapy in high-risk CR patients: OS (A) and CSS (B). The comparison between patients who received adjuvant chemotherapy and those who receive neoadjuvant chemotherapy in the high-risk patients group of NCR for OS (C) and CSS (D). CI, confidence interval; CR, complete response; CSS, cancer-specific survival; HR, hazard ratio; NCR, non-complete response; OS, overall survival.

Discussion

The incidence of BC in young women, especially those of reproductive age, has been increasing steadily (19). These patients often present with higher-grade tumors and more aggressive biological characteristics than older individuals (20). While BCS followed by postoperative radiotherapy has gained increasing acceptance as a standard approach for early-stage and locally advanced disease in younger women, this cohort remains at heightened risk of recurrence, driven by adverse clinicopathological features such as hormone receptor negativity, lymphovascular invasion, and multifocality (21,22). Although adjuvant systemic therapies have improved outcomes, their benefits are often constrained by inherent tumor resistance and treatment-related toxicities (23). Recent data suggest that neoadjuvant chemotherapy may enhance surgical outcomes and provide prognostic insights through pathologic response assessment; however, its role in early-stage operable disease remains contentious, particularly among patients without overt high-risk features (24,25). These challenges underscore the critical need for refined risk stratification tools to identify subgroups of young patients who may derive maximal benefit from intensified or alternative treatment strategies.

Our study aimed to investigate the prognostic factors for OS and CSS in this population and to develop predictive tools to guide clinical decision-making. We identified several factors, including tumor grade, T stage, N stage, hormone receptor status and tumor sequence as independent prognostic indicators. Tumor grade and lymph node were identified as key predictors of both OS and CSS, consistent with previous studies linking poorly differentiated histology and lymph node metastasis to more aggressive disease in younger patients (26). The observed protective effect of HER2 positivity contrasts with earlier reports but may reflect the significant impact of HER2-targeted therapies in current treatment regimens (27). Likewise, PR positivity correlated with better outcomes, likely due to increased sensitivity to endocrine therapies (28). Furthermore, our analysis firstly identified tumor sequence—specifically, the presence of multiple primary tumors—as an independent predictor of adverse survival outcomes.

While the role of chemotherapy in improving survival outcomes in early-stage BC has been established, the optimal use of chemotherapy, especially in high-risk younger patients, remains a subject of ongoing debate (29). In our study, we observed that chemotherapy provided limited survival benefits for both low- and middle-risk groups, and in some cases, was associated with worsened outcomes in the low-risk group. This observation aligns with the findings of recent clinical trials, which suggest that chemotherapy may have a less pronounced benefit in patients with early-stage disease and low-risk features (30). It is increasingly evident that overtreatment with chemotherapy in low-risk patients may lead to unnecessary toxicities without substantial improvements in survival. Additionally, we observed for the first time that neoadjuvant chemotherapy significantly improved OS in patients with CR, but not in those with NCR within the middle-risk group. The possible reason for this discrepancy may lie in the differential biological behavior of tumors in CR versus NCR patients, with the former likely achieving a more profound therapeutic response (31).

In contrast, neoadjuvant chemotherapy showed significant benefits for patients with CR, particularly in the middle-risk group, and offered improved OS for this subgroup. These results support the emerging consensus that neoadjuvant chemotherapy, by allowing for real-time assessment of tumor response, may guide more personalized treatment strategies. However, our findings also suggest that neoadjuvant chemotherapy does not benefit patients with NCR, who may fare better with adjuvant chemotherapy. The differential effectiveness of neoadjuvant versus adjuvant chemotherapy in high-risk subgroups raises important questions about tumor biology and the optimal timing for therapeutic intervention. Recent evidence suggests that neoadjuvant therapy not only reduces tumor size but also reveals underlying resistance mechanisms, allowing for more adaptive treatment strategies. CR patients saw the greatest benefit from neoadjuvant regimens, while those with NCR appeared to respond better to adjuvant chemotherapy. This may be due to the elimination of residual micrometastatic disease through postoperative systemic therapy (32). This differentiation highlights the importance of tailoring chemotherapy regimens based on individual tumor biology and response to treatment, a concept that has gained attention in the context of precision medicine (33).

Our study also highlights the utility of nomograms as a predictive tool for guiding clinical decisions in BC patients of reproductive age. The nomograms developed in this study demonstrated superior predictive accuracy compared to traditional staging AJCC 7th edition. This is particularly relevant in younger women with BC, who may present with diverse clinical features not fully captured by conventional staging methods. By integrating multiple clinical variables, the nomogram provides a personalized risk assessment, enabling clinicians to more effectively identify high-risk patients and make informed decisions regarding adjuvant therapy. Limitations of this study include its retrospective design and reliance on registry data. Critically, the absence of molecular profiling (such as genomic classifiers, Ki-67 indices, and immune markers) precludes biologically driven risk stratification. Furthermore, while internal validation demonstrated robust discrimination, external validation in independent, multi-institutional cohorts is needed to confirm generalizability before clinical translation. Future research integrating clinicopathological variables with tumor molecular subtyping may refine prediction accuracy and guide therapy de-escalation.

In conclusion, while current treatment strategies like BCS and postoperative radiotherapy have improved outcomes for early-stage BC, high-risk patients still face significant challenges in terms of recurrence and metastasis. Chemotherapy remains an important component of treatment, but its benefit must be carefully evaluated in individual patients, with particular attention to tumor biology and response. The development of predictive nomograms offers a promising approach for optimizing treatment strategies and improving survival outcomes in young BC patients. Future studies, including prospective clinical trials, are needed to further validate these models and refine our understanding of the role of chemotherapy in this patient population.

Conclusions

Our study highlights the importance of personalized treatment strategies for reproductive age women with OBC. The nomograms constructed in this study, based on prognostic factors, offer improved predictive accuracy over traditional staging systems, aiding in the identification of high-risk patients. While chemotherapy remains essential, its benefit must be tailored to individual tumor biology and response, with neoadjuvant therapy showing significant advantages for complete responders. Future prospective studies are needed to validate these models and further refine treatment strategies.

Acknowledgments

We would like to thank all the researchers for the SEER program.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1059/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1059/prf

Funding: This research was funded in by the National Natural Science Foundation of China (Grant No. 82372078 and No. 82203688).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1059/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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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/.

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