In this study, we developed a predictive nomogram to assess the probability of SLNM in breast cancer patients. Nine significant predictive factors with independent effects were ultimately identified. ROC curves, calibration curves, and clinical decision analysis of both the training and validation cohorts demonstrated that this nomogram has high sensitivity and specificity in predicting SLNM in patients with breast cancer and is a reliable clinical tool.

Age is one of the key objects explored in this study. On the basis of practical clinical experience, older patients tend to have a lower SLNM rate. However, our study data revealed that this pattern only exists in patients under 80 years of age. In patients over 80 years old, a greater age was associated with a higher SLNM rate, which is a novel finding. Although we cannot enumerate all previous prediction model studies on the relationship between age and LNM, the results of all single-center studies reviewed thus far support this clinical experience [31,32,33,34,35,36,37]. We believe that this is related mainly to the absolute shortage of patients aged ≥ 80 years; for this reason, few studies have categorized the population aged ≥ 80 years as a separate group. In fact, according to the findings of our study, previous studies' age-grouping methods essentially mixed the lowest SLNM rate group (60–79 years old) with the ≥ 80-year-old group. This naturally diluted the unique characteristics of the ≥ 80 age group, indicating that previous research underestimates the importance of the ≥ 80 age group. Therefore, it is necessary to conduct more systematic research on this population to fully explore the authenticity of the newly discovered pattern and its underlying reasons. This will become one of our key future research directions. On the basis of the patient baseline information statistically derived from this study, we can preliminarily analyze the possible reasons for this new finding: 1. Patients ≥ 80 years old have characteristics such as relatively larger tumors, a greater proportion of distant metastasis, and lower tumor differentiation. 2. This may also be related to the fact that elderly patients are less likely to detect the disease promptly, and by the time they seek medical attention, breast cancer may have significantly progressed. We similarly explored the scientific grouping of pathological types and ultimately categorized them into 7 types. The results revealed that mucinous adenocarcinoma was the least likely to metastasize via sentinel lymph nodes. The characteristics of mucinous adenocarcinoma, including lower histological grade, better differentiation, and lower LNM rates, have also been confirmed in multiple studies [36, 38, 39]. Compared with the most common pathological type “IDC,” Paget's disease [40] and IMPC [31, 41] are considered to have a high tendency for LNM, requiring greater clinical vigilance to avoid overlooking such lymphatic progression. Moreover, we found that the pathological type of IDC mixed with ILC had a significantly higher SLNM rate than did pure IDC and pure ILC. No previous research has focused on a detailed study of this type, which will also be a key focus of our future research.

Regarding race, as most previous studies categorized it into White, Black, and others, we referenced Iqbal J et al.'s earlier study [42] on the basis of the SEER database and additionally screened Chinese and Japanese populations, whose sample sizes were large enough and exhibited significantly different LNM rates than did other races. The results revealed that the SLNM rates for Chinese and Japanese patients were significantly lower than those for Black and White patients. SLNM risk positively correlates with tumor size, as reported in nearly all relevant studies by researchers such as Rivadeneira [43]. This conclusion, along with the conclusion that "M1 patients have a much higher LNM rate than M0 patients," applies in almost all scenarios. Our study also revealed that patients with primary tumors located in the axillary tail and nipple/central regions of the breast were more likely to experience SLNM, requiring attention to these relatively unique primary tumor locations. Research by Gou et al. [44] indicated that the axillary tail is an independent factor contributing to LNM. Another survey revealed that tumors in the central and nipple regions were associated with LNM [45]. With respect to breast cancer subtypes, Reyal et al. [46] reported that the TNBC had the lowest SLNM rate, and the HER2-enriched subtype had the highest SLNM rate. A study based on the SEER database [47] suggested that while the risk of LNM in the TNBC is significantly lower than that in the luminal A subtype, there is no significant difference between the other subtypes. In fact, our univariate logistic regression analysis revealed statistically significant differences in SLNM between subtypes. However, in our multivariate logistic regression analysis, subtype was not considered a significant predictive factor. The above results indicate that there may indeed be differences in SLNM between different breast cancer subtypes, while TNBC is generally considered the least likely to metastasize to lymph nodes. However, when factors with greater influence on SLNM are included in the predictive model, the overall differences between subtypes are further reduced, which is the primary reason for the discrepancy between the univariate and multivariate analysis results for breast cancer subtypes.

Compared with previous studies, our nomogram has several advantages. First, almost all previous prediction model studies for SLNM were small-sample, single-center studies. Owing to limitations in the geographic area and absolute number of people included in these studies, their predictive value is questionable. Although two articles [48, 49] used the National Cancer Database (NCDB) for large-sample prediction model studies, they both limited the enrolled population to ductal carcinoma in situ (DCIS) and did not establish an SLNM model for a broader population. Therefore, on the basis of the conclusions of Bilimoria et al. [25]., we screened the population considered to have undergone SLNB in the SEER database, leveraging the advantages of this database's large, multicenter sample size. This allowed us to build a prediction model for SLNM in a wider population of patients with breast cancer. Compared with previous similar studies, our prediction information is richer, the prediction model is larger, and it can be better applied in clinical practice. Moreover, before conducting clinicopathological feature analysis, we first explored the scientific grouping of each predictive factor. This is a novel approach compared with previous SEER database-based predictive model studies. Taking "pathological type" as an example, most existing SEER database-based studies simply divide it into three categories: IDC, ILC, and others. Reducing the subgroups of a prediction factor often leads to higher AUC values and better model discrimination, which is why few researchers choose grouping methods beyond the "three-category" approach. However, the "site record–rare tumors" field appears to have not been studied previously. On the basis of the clinical value of pathological type, sample size, and significant differences in SLNM rates, we successfully created a new classification method. Similarly, we conducted a scientific exploration of the "age" factor on the basis of the inherent objective relationship between age and SLNM, resulting in an age grouping that was more suitable for this study. We believe that evaluating a predictive model solely on the basis of AUC values is insufficient. Many studies prioritize increasing AUC values, often altering factor groupings to achieve this goal, potentially misclassifying groups with high discrimination into one category, and leading to erroneous conclusions.

This nomogram holds significant potential for clinical translation and optimizing axillary management in breast cancer. By enabling personalized risk stratification (e.g., identifying low-risk patients who may safely avoid SLNB), it aligns with global trends toward surgical de-escalation, potentially reducing complications like lymphedema by 15–20% [19, 22]. Its integration with multimodal data—such as imaging (MRI/PET-CT) and targeted therapies (e.g., HER2 blockade)—supports dynamic treatment planning [18], while cost-effectiveness analyses suggest substantial savings in resource-limited settings [21]. Moreover, the nomogram’s framework can be augmented with deep learning algorithms trained on radiopathomic data, potentially elevating AUC to > 0.85. [20]

Although the nomogram showed several advantages and significant potential, this study had some limitations. Our study utilized the conclusions of Bilimoria KY et al. to define breast cancer patients with 1–5 lymph nodes examined in the SEER database as patients who underwent SLNB. This approach is similar to a double-edged sword. While this allowed us to successfully establish a predictive model for SLNM in breast cancer patients on the basis of the SEER database, this definition inevitably contains bias. We could not perfectly match the actual SLNB population, which is a major limitation of our study. Moreover, several knowledge gaps remains. First, we could not obtain other important information from the SEER database, such as vascular invasion, despite its proven importance in axillary LNM in some single-center studies [50, 51]. Second, external validation in non-Western populations (e.g., Chinese cohorts) is pending, and the model’s generalizability requires further confirmation. Since our nomogram is based on a population of White and Black individuals, efforts are needed to minimize selection bias. Finally, the nomogram’s AUC (0.700–0.711) suggests moderate discriminative power. Incorporating predictive factors such as vascular invasion and molecular biomarkers (e.g., circulating tumor DNA or immune characteristics) may improve the accuracy of the model's prediction.

Overall, although SLNB has significantly fewer surgical complications than ALND, trials such as INSEMA (NCT02466737) [52] still demonstrate persistent complications, including pain, lymphedema, and functional impairment, evident within the first month after surgery. Therefore, in clinical practice, we should strive to improve axillary lymph node assessment while ensuring patient quality of life. We developed this nomogram to more accurately predict the individual probability of SLNM, with the goal of tentatively screening patients who were predicted to be SLN (-). This may provide valuable evidence for potential SLNB exemptions, achieving broader benefits. Over the next five years, we anticipate that predictive models will increasingly integrate multiomics data (genomic, radiomic, and clinicopathological) to achieve higher precision. Moreover, artificial intelligence-driven tools could dynamically update risk predictions based on real-world data, enabling adaptive clinical decision-making. Our study serves as a foundational step toward these goals, emphasizing the need for collaborative efforts to validate and refine such models in prospective trials.