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Introduction
Breast angiosarcoma (BAS) is a rare and highly aggressive malignancy that accounts for less than 1% of all malignant breast tumors and less than 5% of soft tissue sarcomas [1]. Despite the low incidence of BAS, the prognosis remains poor. Studies indicate that the 5-year survival rate ranges between 28 and 50% [2]. Although BAS is not well understood, studies have shown a strong association with breast irradiation, chronic lymphoedema, and other vascular anomalies [3]. Recent studies have found that the incidence of BAS has been increasing in recent years, particularly among patients who have undergone radiotherapy following breast cancer surgery [4]. Genetic factors and immunosuppression may also play a role in the pathogenesis of BAS [5]. Surgical excision, often requiring extensive mastectomy, is currently the primary treatment for BAS. Although surgery is the preferred treatment for BAS, its aggressive nature and high rate of local recurrence often mean that surgery alone is not enough to achieve long-term control. The role of radiotherapy in the management of this disease remains controversial. Chemotherapies are effective in specific cases, but their overall effectiveness remains unclear. Although immunotherapy and targeted therapies offer new hope for patients with BSA, their clinical efficacy has not yet been fully validated in large-scale clinical trials [6, 7]. Most studies remain limited to small case reports or preliminary clinical trials. There is significant individual variability in response. As a result, predicting outcomes for patients with BSA remains challenging due to the poor prognosis and inconsistent treatment approaches.
Historically, tumor staging and histological characteristics have been used to predict patient prognosis. Tumor staging and histological characteristics are the mainstay of traditional prognostic evaluation. However, due to the highly invasive and heterogeneous nature of angiosarcoma, these conventional methods alone are often insufficient to reflect the prognosis of the patient fully. As a result, the prediction of patient survival and treatment outcomes remains highly uncertain for clinicians. Individualized predictive models based on clinical, pathological, and treatment data have become a focus of research with the advancement of personalized medicine. As a statistical tool that integrates multiple factors, the nomogram has been widely used to prognosticate various cancers, providing clinicians with personalized survival predictions. Most prognostic models for angiosarcomas focus on the general population of patients...