Prognostic Factors Analysis in Breast Cancer Patients with Brain Metastases: Identification of Key Determinants of Survival

Prognostic Factors Analysis in Breast Cancer Patients with Brain Metastases: Identification of Key Determinants of Survival | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Factors Analysis in Breast Cancer Patients with Brain Metastases: Identification of Key Determinants of Survival Man Li, Yuchen Ge, Tao Wei, Yifan Lei, Yunyan Yang, Mingrui Zhao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8009638/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : With advances in systemic therapy, survival has improved in patients with breast cancer brain metastases (BCBM). Radiotherapy (RT), as a local treatment, further contributes to this improvement. This study investigates the prognostic factors for survival in BCBM patients with a Karnofsky Performance Status (KPS) score ≥70 who underwent craniocerebral radiotherapy (CRT). Methods : A total of 505 patients with BCBM diagnosed in our hospital from January 2017 to December 2023 were retrospectively explored. Subsequently, Kaplan-Meier method (K-M method) was used to analyze overall survival (OS) and progression-free survival (PFS). Additionally, Clinical data were collected, radiomics and dosimetric factors were extracted for univariate and multivariate model analysis, and a survival prediction model was established. Results : The median overall survival (mOS) of the 505 BCBM patients was 20.8 months (95% CI 18.8 - 23.2 months). Multivariate analysis identified the number (≤4) of brain metastases (P<0.001) and CRT (P<0.001) as independent prognostic factors. Specifically, the median OS in the CRT group (n=293) was 27.0 months (95% CI 23.33 - 30.50 months), which was significantly longer than that of the non-radiotherapy group (n=201), which had a median OS of 15.10 months (95% CI 10.97 - 17.17 months). A significant survival difference was observed between the two groups (P < 0.001, HR 0.522, 95% CI 0.424-0.632). Furthermore, Multivariate analysis revealed that leptomeningeal metastasis (LM) (P=0.015), extracranial metastasis (P=0.015), and intracranial progression after RT (P=0.013) were independent adverse prognostic factors for OS in patients receiving CRT. Additionally, the median progression-free survival (PFS) in the radiotherapy group was 16.3 months (95% CI 14.6 - 19.17 months), with LM (P<0.001) identified as an independent adverse prognostic factor for PFS. In the subgroup analysis, patients with positive expression of human epidermal growth factor receptor 2 (HER2) had a better survival benefit when treated with targeted therapy (TT) (P=0.021). Finally, a predictive model incorporating radiomic and radiodosimetric features (n=149) was developed and showed excellent performance in predicting 1-, 2-, and 3-year survival, with area under curve (AUC) values of 0.965, 0.861, and 0.859, respectively. Conclusion : For BCBM patients with a KPS ≥70, CRT is associated with a significant survival benefit. However, the development of LM remains a major adverse factor leading to poorer overall prognosis. Besides, the integration of radiomic and radiodosimetric features into predictive models demonstrates strong potential for accurately estimating prognosis. These findings suggest that such models could enhance clinical decision-making, ultimately supporting improved patient outcomes. Breast cancer brain metastases Craniocerebral radiotherapy Survival outcomes Predictive modeling Radiomics and radiodosomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Breast cancer (BC) has the second highest incidence of brain metastasis (BM) after lung cancer, and is one of the leading causes of death in affected patients (1). Previous studies have shown that among patients with stage IV breast cancer, those who develop BM exhibit the poorest site-specific survival and overall survival across all distant metastatic sites (2). Therefore, identifying and intervening in prognostic factors associated with survival has become particularly critical for patients with BCBM. Currently, systemic therapy has improved the survival of patients with BCBM. The primary cause of death in most of these patients remains extracranial disease progression leading to organ failure (3-5). Intracranial disease control is achieved mainly through RT (6). However, some studies have indicated that the benefits of CRT may not translate into long-term survival improvement (7). One contributing factor is that a lower KPS score in irradiated patients serves as an independent prognostic factor for both intracranial PFS and OS (8). A prospective clinical study enrolling patients with a KPS score >70 who underwent stereotactic radiosurgery (SRS) demonstrated significant OS benefits (9). This is primarily because patients in poor general condition face an increased risk of radiotherapy-related toxicities, which may lead to treatment intolerance and potential risk-benefit imbalance, ultimately exacerbating intracranial reactions and clinical symptoms (10). Consequently, both American Society of Clinical Oncology (ASCO) and American Society for Therapeutic Radiology and Oncology (ASTRO) guidelines recommend SRS or whole-brain radiotherapy (WBRT) for BM patients with a KPS score >70 to achieve improved survival outcomes (11-13). With advancements in imaging technology, a growing number of patients are diagnosed and treated at an early stage when their KPS score remains relatively high (14). In this study, we primarily enrolled BCBM patients with a KPS score ≥70 for analysis. The aim was to compare whether RT could prolong survival in this generally well-performing patient population. Subsequently, univariate and multivariate analyses were performed to identify prognostic factors associated with PFS and OS among those undergoing RT. Furthermore, a cohort of 149 patients with complete RT planning images and dose distribution data was included to develop a predictive model for survival outcomes. The model's performance was evaluated at the 1-, 2-, and 3-year time points using multiple metrics, and a nomogram was created for visual interpretation of the results. Materials and Methods Data Collection Female patients with BCBM, confirmed radiologically or histologically at our hospital between January 2017 and December 2023, were retrospectively enrolled. The inclusion criteria were as follows: 1. Histologically or cytologically confirmed BC; 2. BM confirmed by cranial Computed Tomography (CT), Magnetic Resonance Imaging (MRI) or pathological report; 3. Age ≥18 years with complete clinical data. The patient screening process is illustrated in Fig.1. Furthermore, treatment response was evaluated using the Response Evaluation Criteria in Solid Tumors version 1.1. Regarding survival endpoints: Median overall survival (mOS) was defined as the time from the diagnosis of brain metastasis to death from any cause or the last follow-up. Radiation-specific overall survival (RT-OS) was defined as the time from the initiation of cranial radiotherapy to death from any cause or the last follow-up. Radiation-specific progression-free survival (RT-PFS) was defined as the time from the initiation of cranial radiotherapy to intracranial disease progression or death from any cause. Patient Follow-up Follow-up information was collected through medical record review and telephone interviews. The unified follow-up cutoff date was set as December 10, 2024. The former approach involved retrieving data from outpatient visits and hospital admissions. Additionally, all patients underwent a standardized telephone interview at the time of their last follow-up. Statistical Analysis Collected data were organized and categorized, and statistical analysis and visualization were performed using SPSS 26.0 and R 4.3.0 software. Variables in the baseline characteristics were treated as qualitative data, and differences between groups were assessed using the chi-square test (χ² test). In cases where significant imbalances in baseline characteristics were observed between the radiotherapy and non-radiotherapy groups, propensity score matching was applied to adjust for confounding factors before further survival analysis. The main analytical procedures included: 1. K-M method to plot survival curves for the overall population and subgroup populations; 2. Log-rank test to evaluate survival differences between groups; 3. Univariate and multivariate Cox proportional hazards regression models to identify factors associated with survival. Variables with a P < 0.1 in univariate analysis were included in the multivariate analysis (backward stepwise method; statistical significance set at P < 0.05) to determine independent prognostic factors. 4. Additionally, subgroup analyses based on estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) receptor status and treatment modalities were conducted using Cox regression models, and forest plots were generated to visualize the results. Predictive Modeling DICOM CT images and dose distribution maps acquired prior to treatment from 149 patients undergoing cranial radiotherapy were utilized for feature extraction. This process was performed using 3D Slicer software (version 5.6.2). A total of 851 original features were extracted from the CT images and dose maps collectively. Subsequently, feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) regression combined with multivariate Cox regression analysis. Features with a significance level of P < 0.05 were incorporated into the predictive model. To evaluate model performance, multiple metrics were applied, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and clinical decision curve analysis (DCA). These evaluations were performed at the 1‑, 2‑, and 3‑year survival timepoints. Finally, a nomogram was developed to visually represent the predictive model. Results Baseline characteristics. A total of 505 BCBM patients were included in this study, and their baseline characteristics are summarized in Table 1. Among them, 293 patients (58.02%) received CRT, while 212 patients (41.98%) did not. The median age was 47 years, and 42.77% of patients were younger than 45 years, suggesting a trend toward younger age at BM diagnosis. Additionally, a relatively high proportion of patients presented with locally advanced disease, and 23.96% had distant metastases at initial diagnosis, with 8.7% having BM at first presentation. According to molecular subtyping based on pathological results, 51.29% of patients were ER+, 36.44% were HER2+, and 14.46% had triple-negative breast cancer (TNBC). In terms of metastatic features, 80.2% of patients had a maximum lesion diameter smaller than 1 cm, and the majority had fewer than 4 metastatic lesions. Regarding treatment, only 8.32% of patients underwent surgical resection of BM, while the majority (62.97%) received chemotherapy. Based on ER and HER2 expression status, some patients also received endocrine therapy (ET) (22.77%) and/or targeted therapy (TT) (24.95%). Analysis of survival prognosis. The median follow-up time for the entire cohort was 40.5 months (95% CI: 37.27–46.27 months). As shown in the K-M curve in Fig.2A, the OS for the total population was 20.8 months (95% CI: 18.8–23.2 months). Univariate and multivariate analyses (Supplementary Table 1) indicated that the number of BM, LM, infratentorial metastasis, cranial surgery, CRT, and chemotherapy were associated with OS in BCBM patients. Notably, fewer than 4 brain metastases (P < 0.001) and CRT (P < 0.001) were identified as independent prognostic factors. Therefore, K-M survival analysis was performed comparing the radiotherapy and non-radiotherapy groups (Fig.2B). The mOS was 27.0 months (95% CI: 23.33–30.50 months) in the radiotherapy group (n = 293) and 15.10 months (95% CI: 10.97–17.17 months) in the non-radiotherapy group (n = 212), demonstrating a statistically significant difference between the two groups (P < 0.001; HR: 0.522; 95% CI: 0.424–0.632). These results suggest that radiotherapy significantly improves OS in BCBM with a KPS score ≥ 70, with particularly pronounced short-term survival benefits. Further analysis of survival outcomes in the CRT cohort revealed that, as shown in the K-M curve in Fig.2C, the RT-OS, defined as the time from RT initiation to death or last follow-up, was 22.9 months (95% CI: 20.5–27.0 months). According to univariate and multivariate analyses (Table 2), factors associated with reduced survival included Ki67 > 14%, LM, WBRT, ECM, and intracranial progression after RT. The LM (P=0.015), ECM (P=0.015), and intracranial progression after RT (P=0.013) were identified as independent adverse prognostic factors for OS in irradiated patients. Additionally, the RT-PFS was 16.3 months (95% CI: 14.6–19.17 months; Fig.2D). Notably, LM (P < 0.001) was an independent adverse prognostic factor for PFS in this population (Supplementary Table 3). In the overall cohort, patients with LM had a mOS of 13.43 months (95% CI: 10.37–19.17 months), while irradiated patients with this condition exhibited a median RT-OS of 12.37 months (95% CI: 7.37–NA months). These results indicate that RT may not significantly improve survival in patients with LM, however, this conclusion should be interpreted with caution due to the limited sample size, which may introduce bias. Furthermore, patients with stable intracranial disease after RT achieved longer survival, whereas those with ECM experienced poorer overall outcomes. Subgroup Analysis. As shown in the forest plot in Supplementary Fig.3A, ET did not significantly improve survival outcomes in ER+ patients after RT. This may be attributed to the fact that the benefits of ET are typically observed in long-term survival, whereas this patient population had relatively shorter OS, thereby limiting observable benefits. Overall, trends suggested that chemotherapy, TT, cranial surgery, or SRS were associated with better survival in these patients, although these differences were not statistically significant. In Supplementary Fig.3B, HER2+ patients receiving TT showed improved survival outcomes. This included not only HER2-directed targeted agents but also a small subset of patients who received anti-angiogenic targeted therapy. The results indicate that both types of TT contributed to prolonged survival in BM patients to some extent. Development of the Predictive Model. The predictive model was developed by integrating radiomic and dosimetric features with clinical factors from 149 patients. As illustrated in Supplementary Fig.3, all extracted features underwent min-max normalization. Through cross-validation, the parameter λ was adjusted to identify survival-associated features. Feature selection was performed using LASSO regression combined with multivariate Cox analysis, incorporating features with P < 0.05. The optimal λ value (Lambda.1se) was determined to be 0.04882492, resulting in the selection of 9 feature factors in Fig3. The coefficients of the feature factors were as follows: 0.201239759、-0.094642364、0.072927215、-0.035393399、-0.228740902、0.189803755、0.089741358、-0.312898484、0.170645305。Ultimately, the aforementioned nine features were incorporated to construct predictive models for 1-year, 2-year, and 3-year survival. The survival nomogram visually predicts the 1-, 2-, and 3-year OS rates for BCBM patients (Fig.3). In terms of performance across the constructed time-dependent predictive models, the 1-year survival model demonstrated the highest predictive accuracy. As shown in Fig.4ABC, time-dependent receiver operating characteristic (ROC) curve analysis revealed AUC values of 0.965, 0.861, and 0.859 for 1-, 2-, and 3-year survival predictions, respectively. Specifically, the 1-year survival prediction showed a sensitivity of 1.000, specificity of 0.829, and accuracy of 0.832; the 2-year prediction had a sensitivity of 1.000, specificity of 0.681, and accuracy of 0.711; and the 3-year prediction achieved a sensitivity of 0.973, specificity of 0.455, and accuracy of 0.584. Furthermore, the calibration curves for 1-, 2-, and 3-year survival predictions (Fig.4DEF) indicated minimal deviation between predicted and observed outcomes, suggesting good model reliability. In conclusion, the integration of clinical factors, radiotherapy radiomics, and radiodosimetric features holds significant clinical utility for predicting 1-, 2-, and 3-year survival in BCBM patients. This approach provides valuable guidance for improving outcomes in this population; however, the predictive performance gradually decreases as the survival time frame extends. Discussion This study, based on single-center data, analyzed the prognosis of BCBM patients. The results indicate that CRT is associated with a significant survival benefit in BCBM patients with a KPS score ≥70. The overall survival observed in our cohort is consistent with current published findings (15, 16). After the development of BM, survival in BM patients is primarily influenced by the characteristics of the BM and the treatment strategies employed (17, 18). RT, as one of the main treatment modalities for BM, improves local control rates (19, 20). Importantly, our study demonstrates that RT not only enhances local control but also significantly improves OS in irradiated patients. Furthermore, patients who achieved stable intracranial disease after RT showed pronounced benefits in long-term survival. Systemic therapy also plays a crucial role in the long-term survival of BCBM patients. Currently, the majority of patients receive chemotherapy for disease control, while only a minority undergo ET or TT (21). For HER2+ patients, existing studies have demonstrated that TT can prolong survival in those with BM (15). A real-world multicenter study (22) indicated that patients receiving CRT combined with pyrotinib achieved improved survival outcomes. Additionally, a phase 3 trial showed (23) that pyrotinib plus capecitabine significantly improved PFS compared to lapatinib plus capecitabine, providing an alternative treatment option for HER2+ metastatic BC patients after trastuzumab and chemotherapy. In the present study, although the use of TT after BM did not demonstrate a statistically significant improvement in OS or PFS among irradiated patients overall, subgroup analysis revealed a survival benefit in HER2+ patients who received TT alongside CRT. Based on these findings, HER2+ BCBM patients with good performance status are likely to achieve prolonged survival through a combination of CRT and TT. In this study, only 50 (9.9%) BC patients were diagnosed with LM via MRI. Among these LM patients, 30 (60%) received RT; however, they still exhibited poor survival outcomes despite this intervention. Previous studies have similarly reported significantly worse prognosis in patients with LM (24, 25). On one hand, current imaging techniques require further improvement to enable earlier detection of LM (26). On the other hand, effective treatment strategies for LM remain limited. The main approach involves a combination of systemic therapy and local treatment; however, the efficacy of systemic agents is often constrained by the BBB (27, 28). Although recent studies suggest that small-molecule targeted therapies may improve survival in HER2+ BC patients with LM (29)，no clinical trials have yet explored whether combining RT with TT provides additional benefits for this specific population. Further investigation is needed to address this question. Predictive models utilize existing data to estimate an individual's risk of developing a disease or experiencing a specific future outcome, and they already offer valuable guidance in clinical practice. For instance, a study by Josef A. Buchner et al. (30) employed radiomics to predict local control in BM patients following postoperative stereotactic radiotherapy. Similarly, another study based on a prospective trial predicted treatment response and overall survival in breast cancer brain metastasis patients treated with stereotactic radiosurgery (31). While these models demonstrated good predictive value, they incorporated a relatively limited set of variables. Currently, many studies integrate clinical and radiomic features to predict local control after RT for BM, and the combination of these factors has been shown to significantly enhance model performance (32, 33). The present study is the first to incorporate clinical variables, radiotherapy imaging features, and radiodosimetric parameters into a unified predictive model for survival prognosis in BCBM patients. The model validation results indicate strong predictive accuracy, suggesting its potential utility in guiding clinical management and improving survival outcomes for this patient population. This study has several inherent limitations. First, as a single-center retrospective analysis, the results are subject to potential selection bias, which may affect the accuracy and generalizability of the findings. Second, the study spans a relatively long period, during which changes in systemic treatment strategies and advancements in radiotherapy techniques may have influenced patient outcomes. These temporal variations could introduce confounding effects that impact the interpretation of the data and the validity of the conclusions. Finally, the sample size used to develop the predictive model was relatively small. Therefore, the conclusions require validation in larger, multicenter prospective cohort studies, as well as further verification using external datasets or prospective data. Conclusion In summary, BCBM patients with good performance status can achieve long-term survival benefits from CRT, particularly when local control of intracranial lesions is maintained, which is associated with significantly prolonged OS. However, the development of LM leads to a substantially worse overall prognosis. Therefore, early detection and timely intervention for LM are critically important. Our predictive model demonstrates strong performance in estimating patient prognosis. In the future, incorporating additional relevant factors could further enhance its accuracy, ultimately enabling more effective prediction of outcomes in BCBM patients, guiding clinical decision-making, and thereby contributing to improved survival and patient support. Abbreviations Full title Abbreviations radiotherapy RT breast cancer patients with brain metastases BCBM Karnofsky Performance Status KPS craniocerebral radiotherapy CRT Kaplan-Meier K-M median overall survival mOS leptomeningeal metastasis LM extracranial metastasis ECM area under curve AUC Breast cancer BC brain metastasis BM stereotactic radiosurgery SRS American Society of Clinical Oncology ASCO American Society for Therapeutic Radiology and Oncology ASTRO whole-brain radiotherapy WBRT Computed Tomography CT Magnetic Resonance Imaging MRI Radiation-specific overall survival RT-OS Radiation-specific progression-free survival RT-PFS Least Absolute Shrinkage and Selection Operator LASSO receiver operating characteristic ROC clinical decision curve analysis DCA triple-negative breast cancer TNBC endocrine therapy ET targeted therapy TT Human Epidermal Growth Factor Receptor 2 HER2 estrogen receptor ER Declarations Conflict of interest The authors declare that they have no conflicts of interest. Ethics approval and consent to participate This study was approved by the Ethics Committee of Yunnan Cancer Hospital (approval number: SLKYLX2025-219), and the experimental procedures were conducted in accordance with the relevant regulations and in strict compliance with the Declaration of Helsinki. Informed consent to participate was obtained from all participants also. Consent for publication Written informed consent for publication was obtained from all participants. CRediT authorship contribution statement Man Li (First Author): Conceptualization, Validation, Formal analysis, Investigation, Data Curation, Writing-Original Draft. Yuchen, Ge: Formal analysis, Investigation, Writing-Review & Editing. Tao Wei: Software, Data Curation, Methodology. Yifan Lei: Software, Data Curation. Yunyan Yang: Investigation, Data Curation. Mingrui Zhao: Data Curation, Visualization. Bo Li: Data Curation, Methodology. Wenhui Li (Corresponding author): Resources, Project administration, Conceptualisation, Writing - Review & Editing, Funding acquisition. Li Wang (Corresponding author): Resources, Conceptualisation, Project administration, Writing - Review & Editing, Funding acquisition. Funding Information and Acknowledgements This work was financially supported by the National Natural Science Foundation of China (82460576), Xingdian Talents Support Program for young Talents (XDYC-QNRC-2023-0189) and Yunnan Provincial Health Commission Medical Reserve Talent Training Program(H2024022). Data availability The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. References Giaquinto AN, Sung H, Newman LA, Freedman RA, Smith RA, Star J, et al. Breast cancer statistics 2024. CA: a cancer journal for clinicians. 2024;74(6):477-95. Wang R, Zhu Y, Liu X, Liao X, He J, Niu L. 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1","display":"","copyAsset":false,"role":"figure","size":53669,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient screening and enrollment. KPS: Karnofsky performance status. CT: Computed tomography.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8009638/v1/58a8dbcb1f4e8bbd10abf263.png"},{"id":96247123,"identity":"b0282013-d2d6-46e9-9929-5533c544e444","added_by":"auto","created_at":"2025-11-19 07:27:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116890,"visible":true,"origin":"","legend":"\u003cp\u003eOS and PFS of BCBM Patients Analyzed by Kaplan-Meier Method with Log-Rank Test. (A) OS of the entire cohort from the time of BM diagnosis. (B) Comparison of OS between patients who received CRT and those who did not. (C) RT-OS from the initiation of CRT in the treated subgroup. (D) RT-PFS from the initiation of CRT in the treated subgroup. RT: Radiation therapy.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8009638/v1/1aa6b956db7e52862c5f28ca.png"},{"id":96247624,"identity":"cdbd82aa-d990-4840-8102-b82fb76540af","added_by":"auto","created_at":"2025-11-19 07:27:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":198277,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for Predicting 1-, 2-, and 3-Year Survival Based on Clinical Factors, Radiomic Features, and Dosimetric Features.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8009638/v1/cffc8a48aa391c2981b53d1b.png"},{"id":96247991,"identity":"81247a51-4bd4-4f9d-afa3-8468f23ba79d","added_by":"auto","created_at":"2025-11-19 07:27:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109138,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance Evaluation of the Survival Prediction Model. (A) ROC curve for 1-year survival prediction. (B) ROC curve for 2-year survival prediction. (C) ROC curve for 3-year survival prediction. (D) Calibration curve for 1-year survival prediction. (E) Calibration curve for 2-year survival prediction. (F) Calibration curve for 3-year survival prediction.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8009638/v1/be0a9082a866c55d8b8b9ffc.png"},{"id":103496572,"identity":"0dafd526-b90a-48f6-8e0f-44e280f1c94c","added_by":"auto","created_at":"2026-02-26 11:13:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1044592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8009638/v1/96161f65-aaee-4f25-af56-b47a40938e0a.pdf"},{"id":96073765,"identity":"900df413-5838-47a8-9188-cbe61bf14b3d","added_by":"auto","created_at":"2025-11-17 10:21:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":593593,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8009638/v1/29c70745d5d573298f8729ea.docx"},{"id":96073762,"identity":"6e7aa37b-08e4-4f8a-ac12-85bd3c2a06f3","added_by":"auto","created_at":"2025-11-17 10:21:13","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":36827,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8009638/v1/d310557e52686f55d7c44051.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Factors Analysis in Breast Cancer Patients with Brain Metastases: Identification of Key Determinants of Survival","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BC) has the second highest incidence of brain metastasis (BM) after lung cancer, and is one of the leading causes of death in affected patients (1). Previous studies have shown that among patients with stage IV breast cancer, those who develop BM exhibit the poorest site-specific survival and overall survival across all distant metastatic sites (2). Therefore, identifying and intervening in prognostic factors associated with survival has become particularly critical for patients with BCBM.\u003c/p\u003e\n\u003cp\u003eCurrently, systemic therapy has improved the survival of patients with BCBM. The primary cause of death in most of these patients remains extracranial disease progression leading to organ failure (3-5). Intracranial disease control is achieved mainly through RT (6). However, some studies have indicated that the benefits of CRT may not translate into long-term survival improvement (7). One contributing factor is that a lower KPS score in irradiated patients serves as an independent prognostic factor for both intracranial PFS and OS (8). A prospective clinical study enrolling patients with a KPS score \u0026gt;70 who underwent stereotactic radiosurgery (SRS) demonstrated significant OS benefits (9). This is primarily because patients in poor general condition face an increased risk of radiotherapy-related toxicities, which may lead to treatment intolerance and potential risk-benefit imbalance, ultimately exacerbating intracranial reactions and clinical symptoms (10). Consequently, both American Society of Clinical Oncology (ASCO) and American Society for Therapeutic Radiology and Oncology (ASTRO) guidelines recommend SRS or whole-brain radiotherapy (WBRT) for BM patients with a KPS score \u0026gt;70 to achieve improved survival outcomes (11-13). With advancements in imaging technology, a growing number of patients are diagnosed and treated at an early stage when their KPS score remains relatively high (14).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we primarily enrolled BCBM patients with a KPS score \u0026ge;70 for analysis. The aim was to compare whether RT could prolong survival in this generally well-performing patient population. Subsequently, univariate and multivariate analyses were performed to identify prognostic factors associated with PFS and OS among those undergoing RT. Furthermore, a cohort of 149 patients with complete RT planning images and dose distribution data was included to develop a predictive model for survival outcomes. The model\u0026apos;s performance was evaluated at the 1-, 2-, and 3-year time points using multiple metrics, and a nomogram was created for visual interpretation of the results.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemale patients with BCBM, confirmed radiologically or histologically at our hospital between January 2017 and December 2023, were retrospectively enrolled. The inclusion criteria were as follows: 1. Histologically or cytologically confirmed BC; 2. BM confirmed by cranial Computed Tomography (CT), Magnetic Resonance Imaging (MRI) or pathological report; 3. Age \u0026ge;18 years with complete clinical data. The patient screening process is illustrated in Fig.1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, treatment response was evaluated using the Response Evaluation Criteria in Solid Tumors version 1.1. Regarding survival endpoints: Median overall survival (mOS) was defined as the time from the diagnosis of brain metastasis to death from any cause or the last follow-up. Radiation-specific overall survival (RT-OS) was defined as the time from the initiation of cranial radiotherapy to death from any cause or the last follow-up. Radiation-specific progression-free survival (RT-PFS) was defined as the time from the initiation of cranial radiotherapy to intracranial disease progression or death from any cause.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollow-up information was collected through medical record review and telephone interviews. The unified follow-up cutoff date was set as December 10, 2024. The former approach involved retrieving data from outpatient visits and hospital admissions. Additionally, all patients underwent a standardized telephone interview at the time of their last follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollected data were organized and categorized, and statistical analysis and visualization were performed using SPSS 26.0 and R 4.3.0 software. Variables in the baseline characteristics were treated as qualitative data, and differences between groups were assessed using the chi-square test (\u0026chi;\u0026sup2; test). In cases where significant imbalances in baseline characteristics were observed between the radiotherapy and non-radiotherapy groups, propensity score matching was applied to adjust for confounding factors before further survival analysis. The main analytical procedures included: 1. K-M method to plot survival curves for the overall population and subgroup populations; 2. Log-rank test to evaluate survival differences between groups; 3. Univariate and multivariate Cox proportional hazards regression models to identify factors associated with survival. Variables with a P \u0026lt; 0.1 in univariate analysis were included in the multivariate analysis (backward stepwise method; statistical significance set at P \u0026lt; 0.05) to determine independent prognostic factors. 4. Additionally, subgroup analyses based on estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) receptor status and treatment modalities were conducted using Cox regression models, and forest plots were generated to visualize the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDICOM CT images and dose distribution maps acquired prior to treatment from 149 patients undergoing cranial radiotherapy were utilized for feature extraction. This process was performed using 3D Slicer software (version 5.6.2). A total of 851 original features were extracted from the CT images and dose maps collectively. Subsequently, feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) regression combined with multivariate Cox regression analysis. Features with a significance level of P \u0026lt; 0.05 were incorporated into the predictive model. To evaluate model performance, multiple metrics were applied, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and clinical decision curve analysis (DCA). These evaluations were performed at the 1‑, 2‑, and 3‑year survival timepoints. Finally, a nomogram was developed to visually represent the predictive model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 505 BCBM patients were included in this study, and their baseline characteristics are summarized in Table 1. Among them, 293 patients (58.02%) received CRT, while 212 patients (41.98%) did not. The median age was 47 years, and 42.77% of patients were younger than 45 years, suggesting a trend toward younger age at BM diagnosis. Additionally, a relatively high proportion of patients presented with locally advanced disease, and 23.96% had distant metastases at initial diagnosis, with 8.7% having BM at first presentation. According to molecular subtyping based on pathological results, 51.29% of patients were ER+, 36.44% were HER2+, and 14.46% had triple-negative breast cancer (TNBC). In terms of metastatic features, 80.2% of patients had a maximum lesion diameter smaller than 1 cm, and the majority had fewer than 4 metastatic lesions. Regarding treatment, only 8.32% of patients underwent surgical resection of BM, while the majority (62.97%) received chemotherapy. Based on ER and HER2 expression status, some patients also received endocrine therapy (ET) (22.77%) and/or targeted therapy (TT) (24.95%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of survival prognosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median follow-up time for the entire cohort was 40.5 months (95% CI: 37.27\u0026ndash;46.27 months). As shown in the K-M curve in Fig.2A, the OS for the total population was 20.8 months (95% CI: 18.8\u0026ndash;23.2 months). Univariate and multivariate analyses (Supplementary Table 1) indicated that the number of BM, LM, infratentorial metastasis, cranial surgery, CRT, and chemotherapy were associated with OS in BCBM patients. Notably, fewer than 4 brain metastases (P \u0026lt; 0.001) and CRT (P \u0026lt; 0.001) were identified as independent prognostic factors. Therefore, K-M survival analysis was performed comparing the radiotherapy and non-radiotherapy groups (Fig.2B). The mOS was 27.0 months (95% CI: 23.33\u0026ndash;30.50 months) in the radiotherapy group (n = 293) and 15.10 months (95% CI: 10.97\u0026ndash;17.17 months) in the non-radiotherapy group (n = 212), demonstrating a statistically significant difference between the two groups (P \u0026lt; 0.001; HR: 0.522; 95% CI: 0.424\u0026ndash;0.632). These results suggest that radiotherapy significantly improves OS in BCBM with a KPS score \u0026ge; 70, with particularly pronounced short-term survival benefits.\u003c/p\u003e\n\u003cp\u003eFurther analysis of survival outcomes in the CRT cohort revealed that, as shown in the K-M curve in Fig.2C, the RT-OS, defined as the time from RT initiation to death or last follow-up, was 22.9 months (95% CI: 20.5\u0026ndash;27.0 months). According to univariate and multivariate analyses (Table 2), factors associated with reduced survival included Ki67 \u0026gt; 14%, LM, WBRT, ECM, and intracranial progression after RT. The LM (P=0.015), ECM (P=0.015), and intracranial progression after RT (P=0.013) were identified as independent adverse prognostic factors for OS in irradiated patients. Additionally, the RT-PFS was 16.3 months (95% CI: 14.6\u0026ndash;19.17 months; Fig.2D). Notably, LM (P \u0026lt; 0.001) was an independent adverse prognostic factor for PFS in this population (Supplementary Table 3). In the overall cohort, patients with LM had a mOS of 13.43 months (95% CI: 10.37\u0026ndash;19.17 months), while irradiated patients with this condition exhibited a median RT-OS of 12.37 months (95% CI: 7.37\u0026ndash;NA months). These results indicate that RT may not significantly improve survival in patients with LM, however, this conclusion should be interpreted with caution due to the limited sample size, which may introduce bias. Furthermore, patients with stable intracranial disease after RT achieved longer survival, whereas those with ECM experienced poorer overall outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup Analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in the forest plot in Supplementary Fig.3A, ET did not significantly improve survival outcomes in ER+ patients after RT. This may be attributed to the fact that the benefits of ET are typically observed in long-term survival, whereas this patient population had relatively shorter OS, thereby limiting observable benefits. Overall, trends suggested that chemotherapy, TT, cranial surgery, or SRS were associated with better survival in these patients, although these differences were not statistically significant. In Supplementary Fig.3B, HER2+ patients receiving TT showed improved survival outcomes. This included not only HER2-directed targeted agents but also a small subset of patients who received anti-angiogenic targeted therapy. The results indicate that both types of TT contributed to prolonged survival in BM patients to some extent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of the Predictive Model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictive model was developed by integrating radiomic and dosimetric features with clinical factors from 149 patients. As illustrated in Supplementary Fig.3, all extracted features underwent min-max normalization. Through cross-validation, the parameter \u0026lambda; was adjusted to identify survival-associated features. Feature selection was performed using LASSO regression combined with multivariate Cox analysis, incorporating features with P \u0026lt; 0.05. The optimal \u0026lambda; value (Lambda.1se) was determined to be 0.04882492, resulting in the selection of 9 feature factors in Fig3. The coefficients of the feature factors were as follows: 0.201239759、-0.094642364、0.072927215、-0.035393399、-0.228740902、0.189803755、0.089741358、-0.312898484、0.170645305。Ultimately, the aforementioned nine features were incorporated to construct predictive models for 1-year, 2-year, and 3-year survival.\u003c/p\u003e\n\u003cp\u003eThe survival nomogram visually predicts the 1-, 2-, and 3-year OS rates for BCBM patients (Fig.3). In terms of performance across the constructed time-dependent predictive models, the 1-year survival model demonstrated the highest predictive accuracy. As shown in Fig.4ABC, time-dependent receiver operating characteristic (ROC) curve analysis revealed AUC values of 0.965, 0.861, and 0.859 for 1-, 2-, and 3-year survival predictions, respectively. Specifically, the 1-year survival prediction showed a sensitivity of 1.000, specificity of 0.829, and accuracy of 0.832; the 2-year prediction had a sensitivity of 1.000, specificity of 0.681, and accuracy of 0.711; and the 3-year prediction achieved a sensitivity of 0.973, specificity of 0.455, and accuracy of 0.584. Furthermore, the calibration curves for 1-, 2-, and 3-year survival predictions (Fig.4DEF) indicated minimal deviation between predicted and observed outcomes, suggesting good model reliability. In conclusion, the integration of clinical factors, radiotherapy radiomics, and radiodosimetric features holds significant clinical utility for predicting 1-, 2-, and 3-year survival in BCBM patients. This approach provides valuable guidance for improving outcomes in this population; however, the predictive performance gradually decreases as the survival time frame extends.\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThis study, based on single-center data, analyzed the prognosis of BCBM patients. The results indicate that CRT is associated with a significant survival benefit in BCBM patients with a KPS score \u0026ge;70. The overall survival observed in our cohort is consistent with current published findings (15, 16). After the development of BM, survival in BM patients is primarily influenced by the characteristics of the BM and the treatment strategies employed (17, 18). RT, as one of the main treatment modalities for BM, improves local control rates (19, 20).\u0026nbsp;Importantly, our study demonstrates that RT not only enhances local control but also significantly improves OS in irradiated patients. Furthermore, patients who achieved stable intracranial disease after RT showed pronounced benefits in long-term survival.\u003c/p\u003e\n\u003cp\u003eSystemic therapy also plays a crucial role in the long-term survival of BCBM patients. Currently, the majority of patients receive chemotherapy for disease control, while only a minority undergo ET or TT (21). For HER2+ patients, existing studies have demonstrated that TT can prolong survival in those with BM (15). A real-world multicenter study (22) indicated that patients receiving CRT combined with pyrotinib achieved improved survival outcomes. Additionally, a phase 3 trial showed (23) that pyrotinib plus capecitabine significantly improved PFS compared to lapatinib plus capecitabine, providing an alternative treatment option for HER2+ metastatic BC patients after trastuzumab and chemotherapy. In the present study, although the use of TT after BM did not demonstrate a statistically significant improvement in OS or PFS among irradiated patients overall, subgroup analysis revealed a survival benefit in HER2+ patients who received TT alongside CRT. Based on these findings, HER2+ BCBM patients with good performance status are likely to achieve prolonged survival through a combination of CRT and TT.\u003c/p\u003e\n\u003cp\u003eIn this study, only 50 (9.9%) BC patients were diagnosed with LM via MRI. Among these LM patients, 30 (60%) received RT; however, they still exhibited poor survival outcomes despite this intervention. Previous studies have similarly reported significantly worse prognosis in patients with LM (24, 25). On one hand, current imaging techniques require further improvement to enable earlier detection of LM (26). On the other hand, effective treatment strategies for LM remain limited. The main approach involves a combination of systemic therapy and local treatment; however, the efficacy of systemic agents is often constrained by the BBB (27, 28). Although recent studies suggest that small-molecule targeted therapies may improve survival in HER2+ BC patients with LM (29)，no clinical trials have yet explored whether combining RT with TT provides additional benefits for this specific population. Further investigation is needed to address this question.\u003c/p\u003e\n\u003cp\u003ePredictive models utilize existing data to estimate an individual\u0026apos;s risk of developing a disease or experiencing a specific future outcome, and they already offer valuable guidance in clinical practice. For instance, a study by Josef A. Buchner et al. (30) employed radiomics to predict local control in BM patients following postoperative stereotactic radiotherapy. Similarly, another study based on a prospective trial predicted treatment response and overall survival in breast cancer brain metastasis patients treated with stereotactic radiosurgery (31). While these models demonstrated good predictive value, they incorporated a relatively limited set of variables. Currently, many studies integrate clinical and radiomic features to predict local control after RT for BM, and the combination of these factors has been shown to significantly enhance model performance (32, 33). The present study is the first to incorporate clinical variables, radiotherapy imaging features, and radiodosimetric parameters into a unified predictive model for survival prognosis in BCBM patients. The model validation results indicate strong predictive accuracy, suggesting its potential utility in guiding clinical management and improving survival outcomes for this patient population.\u003c/p\u003e\n\u003cp\u003eThis study has several inherent limitations. First, as a single-center retrospective analysis, the results are subject to potential selection bias, which may affect the accuracy and generalizability of the findings. Second, the study spans a relatively long period, during which changes in systemic treatment strategies and advancements in radiotherapy techniques may have influenced patient outcomes. These temporal variations could introduce confounding effects that impact the interpretation of the data and the validity of the conclusions. Finally, the sample size used to develop the predictive model was relatively small. Therefore, the conclusions require validation in larger, multicenter prospective cohort studies, as well as further verification using external datasets or prospective data.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, BCBM patients with good performance status can achieve long-term survival benefits from CRT, particularly when local control of intracranial lesions is maintained, which is associated with significantly prolonged OS. However, the development of LM leads to a substantially worse overall prognosis. Therefore, early detection and timely intervention for LM are critically important. Our predictive model demonstrates strong performance in estimating patient prognosis. In the future, incorporating additional relevant factors could further enhance its accuracy, ultimately enabling more effective prediction of outcomes in BCBM patients, guiding clinical decision-making, and thereby contributing to improved survival and patient support.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"475\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eFull title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eAbbreviations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eradiotherapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eRT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003ebreast cancer patients with brain metastases\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eBCBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eKarnofsky Performance Status\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eKPS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003ecraniocerebral radiotherapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCRT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eKaplan-Meier\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eK-M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003emedian overall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003emOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eleptomeningeal metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eLM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eextracranial metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eECM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003earea under curve\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003ebrain metastasis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003estereotactic radiosurgery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eAmerican Society of Clinical Oncology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eASCO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eAmerican Society for Therapeutic Radiology and Oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eASTRO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003ewhole-brain radiotherapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eWBRT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eComputed Tomography\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eRadiation-specific overall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eRT-OS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eRadiation-specific progression-free survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eRT-PFS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003ereceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eclinical decision curve analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003etriple-negative breast cancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eendocrine therapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eET\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003etargeted therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eHuman Epidermal Growth Factor Receptor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eHER2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 377px;\"\u003e\n \u003cp\u003eestrogen receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Yunnan Cancer Hospital (approval number: SLKYLX2025-219), and the experimental procedures were conducted in accordance with the relevant regulations and in strict compliance with the Declaration of Helsinki. Informed consent to participate was obtained from all participants also.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMan Li (First Author): Conceptualization, Validation, Formal analysis, Investigation, Data Curation, Writing-Original Draft. Yuchen, Ge: Formal analysis, Investigation, Writing-Review \u0026amp; Editing. Tao Wei: Software, Data Curation, Methodology. Yifan Lei: Software, Data Curation. Yunyan Yang: Investigation, Data Curation. Mingrui Zhao: Data Curation, Visualization. Bo Li: Data Curation, Methodology. Wenhui Li (Corresponding author): Resources, Project administration, Conceptualisation, Writing - Review \u0026amp; Editing, Funding acquisition. Li Wang (Corresponding author): Resources, Conceptualisation, Project administration, Writing - Review \u0026amp; Editing, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information and Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the National Natural Science Foundation of China (82460576), Xingdian Talents Support Program for young Talents (XDYC-QNRC-2023-0189) and Yunnan Provincial Health Commission Medical Reserve Talent Training Program(H2024022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGiaquinto AN, Sung H, Newman LA, Freedman RA, Smith RA, Star J, et al. Breast cancer statistics 2024. 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Survival outcomes among patients with breast cancer and leptomeningeal disease. Scientific reports. 2025;15(1):24170.\u003c/li\u003e\n\u003cli\u003eBuchner JA, Kofler F, Mayinger M, Christ SM, Brunner TB, Wittig A, et al. Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy. Neuro-oncology. 2024;26(9):1638-50.\u003c/li\u003e\n\u003cli\u003eKatsos K, Michalopoulos G, D\u0026apos;Ambrosio AL, Cobb WS, Grills IS, McInerney J, et al. Predictors of treatment response and overall survival in patients with breast cancer brain metastases treated with stereotactic radiosurgery: a prospective study using the NeuroPoint Alliance SRS Registry. Journal of neurosurgery. 2025:1-12.\u003c/li\u003e\n\u003cli\u003eKanakarajan H, De Baene W, Hanssens P, Sitskoorn M. Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features. Radiation oncology (London, England). 2024;19(1):182.\u003c/li\u003e\n\u003cli\u003eVolovăț CC, Buzea CG, Boboc DI, Ostafe MR, Agop M, Ochiuz L, et al. Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data. Diagnostics (Basel, Switzerland). 2025;15(10).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer brain metastases, Craniocerebral radiotherapy, Survival outcomes, Predictive modeling, Radiomics and radiodosomics","lastPublishedDoi":"10.21203/rs.3.rs-8009638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8009638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: With advances in systemic therapy, survival has improved in patients with breast cancer brain metastases (BCBM). Radiotherapy (RT), as a local treatment, further contributes to this improvement. This study investigates the prognostic factors for survival in BCBM patients with a Karnofsky Performance Status (KPS) score ≥70 who underwent craniocerebral radiotherapy (CRT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 505 patients with BCBM diagnosed in our hospital from January 2017 to December 2023 were retrospectively explored. Subsequently, Kaplan-Meier method (K-M method) was used to analyze overall survival (OS) and progression-free survival (PFS). Additionally, Clinical data were collected, radiomics and dosimetric factors were extracted for univariate and multivariate model analysis, and a survival prediction model was established.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The median overall survival (mOS) of the 505 BCBM patients was 20.8 months (95% CI 18.8 - 23.2 months). Multivariate analysis identified the number (≤4) of brain metastases (P\u0026lt;0.001) and CRT (P\u0026lt;0.001) as independent prognostic factors. Specifically, the median OS in the CRT group (n=293) was 27.0 months (95% CI 23.33 - 30.50 months), which was significantly longer than that of the non-radiotherapy group (n=201), which had a median OS of 15.10 months (95% CI 10.97 - 17.17 months). A significant survival difference was observed between the two groups (P \u0026lt; 0.001, HR 0.522, 95% CI 0.424-0.632). Furthermore, Multivariate analysis revealed that leptomeningeal metastasis (LM) (P=0.015), extracranial metastasis (P=0.015), and intracranial progression after RT (P=0.013) were independent adverse prognostic factors for OS in patients receiving CRT. Additionally, the median progression-free survival (PFS) in the radiotherapy group was 16.3 months (95% CI 14.6 - 19.17 months), with LM (P\u0026lt;0.001) identified as an independent adverse prognostic factor for PFS. In the subgroup analysis, patients with positive expression of human epidermal growth factor receptor 2 (HER2) had a better survival benefit when treated with targeted therapy (TT) (P=0.021). Finally, a predictive model incorporating radiomic and radiodosimetric features (n=149) was developed and showed excellent performance in predicting 1-, 2-, and 3-year survival, with area under curve (AUC) values of 0.965, 0.861, and 0.859, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: For BCBM patients with a KPS ≥70, CRT is associated with a significant survival benefit. However, the development of LM remains a major adverse factor leading to poorer overall prognosis. Besides, the integration of radiomic and radiodosimetric features into predictive models demonstrates strong potential for accurately estimating prognosis. These findings suggest that such models could enhance clinical decision-making, ultimately supporting improved patient outcomes.\u003c/p\u003e","manuscriptTitle":"Prognostic Factors Analysis in Breast Cancer Patients with Brain Metastases: Identification of Key Determinants of Survival","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 10:21:08","doi":"10.21203/rs.3.rs-8009638/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"811d1d00-9c53-461a-8aa9-22f9f094f829","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T11:12:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 10:21:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8009638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8009638","identity":"rs-8009638","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}