Breast cancer remains a leading global malignancy in women, with persistently high incidence and mortality rates [1]. Although surgery is the mainstay of breast cancer treatment, postoperative recurrence remains a significant factor affecting patient prognosis, with approximately 20%–30% of patients experiencing local recurrence or distant metastasis within 5 years after surgery [2]. Current clinical practice mainly uses tumor size, nodal status, and histological grade to evaluate recurrence risk [3]. However, these conventional markers have important limitations. Their assessments are largely based on static morphological observations, which fail to dynamically reflect the biological behavior heterogeneity and micrometastatic potential of tumors. This can lead to overtreatment in some low-risk patients and undertreatment in high-risk patients [4, 5]. Therefore, the search for novel and more effective predictive markers and methods has become an important direction in current breast cancer research.

Ultrasound has become a commonly used modality for clinical evaluation of breast cancer, due to its advantages of being non-invasive, convenient, and repeatable. Two-dimensional grayscale ultrasound can clearly display the morphology, size, boundaries, and internal echogenicity of breast cancer, aiding in the identification of cystic or solid components within masses [6]. Color Doppler ultrasound, on the other hand, reflects tumor angiogenesis by detecting blood flow signals within and around the tumor [7]. However, in predicting postoperative recurrence of breast cancer, both modalities still have limitations in terms of specificity and sensitivity, making it difficult to accurately identify patients at higher risk of recurrence [8, 9]. Virtual touch tissue imaging quantification (VTIQ), as a novel ultrasound technique, is capable of non-invasively and real-time reflecting the stiffness characteristics of tumors by quantitatively measuring the shear wave velocity (SWV) [10]. It has currently become an important clinical tool for the evaluation of breast cancer [11]. Research shows the stiffness of tumor tissue is closely related to its invasiveness, proliferative capacity, and recurrence risk [10]. As the core parameter of VTIQ, SWV shows a significant positive correlation with histological grade and nuclear proliferationantigen-67 (Ki-67) index of breast cancer tissues, which are indicators of malignancy [12]. Moreover, its diagnostic performance is significantly superior to that of traditional ultrasound elastography scores [12]. Other studies have also shown that tumor stiffness is closely related to the extracellular matrix remodeling and stromal fibrosis, which are components of the pro-invasive microenvironment. Parameters of VTIQ (such as the maximum value of Young’s modulus) can serve as potential biomarkers for predicting lymph node metastasis and chemosensitivity [13, 14]. However, the existing studies have predominantly focused on the diagnostic value of VTIQ as a single modality, with a lack of research on the construction of multiparametric predictive models by integrating VTIQ with clinicopathological features.

This study aims to construct and validate a nomogram model combining VTIQ quantitative parameters (SWV) with clinicopathological features to predict postoperative recurrence of breast cancer. By integrating these parameters, we seek to provide a reference for accurately identifying patients at high risk of recurrence and metastasis, thereby optimizing treatment strategies and improving patient prognosis.

Breast cancer recurrence, characterized by its high incidence, unpredictability, and severe threat to quality of life, has emerged as a significant challenge in clinical oncology, imposing a substantial economic and psychological burden on patients and their families [17, 18]. This research effectively developed and validated a nomogram incorporating VTIQ parameters (SWV) and essential clinical-pathological characteristics to forecast the recurrence risk in breast cancer patients post-radical surgery. The study results confirmed that SWV values, as well as histological grade, calcification, and ER/PR status, are independent predictors of postoperative recurrence in breast cancer. The nomogram indicated robust predictive capability (AUC $>$0.72) and clinical applicability in both the training and validation sets, providing a new tool for individualized recurrence risk assessment.

In this study, we analyzed 294 breast cancer patients in the training set, among whom 53 experienced recurrence and 241 did not. In the validation set, there were 126 breast cancer patients, with 23 experiencing recurrence and 103 remaining recurrence-free. Between the training and validation sets, no statistically significant differences were observed between the recurrence and non-recurrence groups regarding age, BMI, menopausal history, obstetric history, smoking history, drinking history and family history (p $>$ 0.05). This indicates that the patients in the two groups within the training and validation sets were comparable in terms of SWV and clinicopathological features.

The influencing factors of postoperative recurrence in breast cancer are complex, with extratumoral factors including lifestyle behaviors, endocrine function, tumor grade, molecular subtypes, and histological grade [19, 20]. In this study, univariate analysis showed that patients in the recurrence group had a higher proportion of tumor maximum diameter $>$5 cm, clinical stage III, histological grade III, irregular mass shape, calcification, vascular invasion, ER/PR negativity, and HER2 positivity compared to those in the non-recurrence group (p 0.05). Logistic regression analysis identified histological grade (OR: 3.36, 95% CI: 1.23–9.19), calcification (OR: 3.16, 95% CI: 1.15–8.68), ER/PR (OR: 2.74, 95% CI: 1.03–7.31), and SWV (OR: 3.71, 95% CI: 1.75–7.84) as independent predictors of postoperative recurrence in breast cancer (p 0.05). Histological grade reflects the degree of cellular atypia and proliferative capacity of tumor cells [21]. This study suggested that histological grade III is an independent predictor of postoperative recurrence in breast cancer, aligning with the findings reported by Groen et al. [22]. The analysis suggests that tumor cells with high histological grade typically exhibit stronger invasiveness and proliferative capacity. These cells are more likely to breach the basement membrane, invade surrounding tissues and blood vessels, thereby increasing the risk of recurrence. The presence of calcifications in breast cancer may indicate tumor aggressiveness [23]. This study demonstrated that calcification is an independent predictor of postoperative recurrence in breast cancer, which is consistent with the findings reported by Wang et al. [24]. The analysis suggests that calcifications may be associated with tumor necrosis and fibrosis, which are likely related to rapid tumor growth and invasive behavior. Moreover, calcifications may also influence the tumor microenvironment, thereby promoting tumor recurrence. Prior research has suggested that ER/PR negativity is typically associated with more aggressive tumor biology [25]. In this study, ER/PR negativity was identified as an independent predictor of postoperative recurrence in breast cancer. This may be attributed to the fact that patients not only have a higher degree of tumor malignancy but also exhibit poor sensitivity to chemotherapy and endocrine therapy, thereby increasing the risk of postoperative recurrence. Investigations have suggested that SWV reflects tissue stiffness, and its higher values in the recurrence group may be associated with fibrous tissue proliferation in tumors [26]. This study demonstrated that SWV was an independent predictor of postoperative recurrence in breast cancer. This may be because increased tumor stiffness essentially reflects changes in the microenvironment. The pathological findings in the recurrence group showed significant fibrous tissue proliferation and collagen fiber deposition, which led to increased tissue stiffness. These changes may alter the tumor microenvironment, promoting the survival and proliferation of tumor cells, thereby increasing the risk of recurrence. In this study, tumor diameter $>$5 cm, clinical stage III, vascular invasion, and HER2 positivity were correlated with recurrence in univariate analysis but did not emerge as independent predictive factors. This may be because tumor size and clinical staging have already been integrated into histological grading, with grade III tumors more likely to grow rapidly to a larger size and exhibit lymph node metastasis and recurrence [27]. Although tumor size is an important clinical indicator, in models that incorporate other pathological factors, it may not be an independent predictor. Vascular invasion was collinear with histological analysis, as poorly differentiated tumors are more prone to vascular invasion. In recent years, improvements in adjuvant chemotherapy regimens for breast cancer have gradually mitigated the impact of HER2 positivity on chemosensitivity [28].

In this study, multivariate analysis was used to identify the most predictive independent factors (histological grade, calcification, ER/PR status, and SWV), based on which a nomogram prediction model was constructed and externally validated. The AUC values for the training and validation sets were 0.789 (95% CI: 0.729–0.850) and 0.728 (95% CI: 0.615–0.841), respectively. These results suggest that the model possesses strong discriminative power, effectively differentiating patients with a high risk of recurrence from those with a low risk. The calibration curves revealed a close match between the predicted probabilities and the actual recurrence probabilities, highlighting the model’s accuracy and reliability. DCA further demonstrated that the model has certain clinical utility. The strengths of this study lie in the integration of traditional clinicopathological features with VTIQ. As a non-invasive imaging technique, VTIQ provides quantitative information on tissue stiffness, thereby complementing the limitations of traditional pathological examinations. Moreover, the independent predictive factors identified through logistic regression analysis further enhanced the accuracy and reliability of the model. In clinical practice, patients identified as high-risk should be offered intensified surveillance and considered for more aggressive adjuvant therapy to maximize the chance of preventing recurrence.

There are also several limitations in this study that need to be considered. Firstly, the retrospective study design may be affected by selection bias and information bias. Secondly, the relatively short follow-up period of 3 years may not fully reflect the long-term recurrence pattern, and postoperative adjuvant therapy was not included in the analysis. Future studies should focus on conducting prospective research and consider the impact of postoperative treatment by conducting longer follow-up to further verify the predictive efficacy of this model. Moreover, although the overall sample size (n = 420) is moderate, the number of events (recurrences, n = 76) remains limited. This low event-to-variable ratio may compromise model stability and generalizability and increase the risk of over-fitting. To address this limitation, we performed both internal and external validation together with calibration analyses; the results confirm that the model maintains consistent performance and has practical clinical value.

This study successfully developed and preliminarily validated a nomogram model integrating VTIQ with clinicopathological features to assess the risk of postoperative recurrence in breast cancer patients. The model showed strong predictive accuracy, calibration, and clinical applicability, offering valuable prognostic stratification for postoperative recurrence risk.

Data supporting this study are available from the corresponding author upon reasonable request.

SD: Conceptualization, validation, data curation, writing—review and editing. CZ: Conceptualization, methodology, software, formal analysis, investigation, writing—original draft preparation. XC: Investigation, data curation, writing—review and editing. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

The study was approved by the ethics committee of Wuxi People’s Hospital Affiliated to Nanjing Medical University (Ethics Approval Number: KY23040), and written informed consent was obtained from all patients. All procedures complied with institutional ethics guidelines and the Declaration of Helsinki.

Not applicable.

This research received no external funding.

The authors declare no conflict of interest.