{ "abstract": "Background\n\nBrain metastasis (BM) is a devastating complication of breast cancer (BC) with a poor prognosis. Early identification of high-risk patients is essential but currently lacks accurate predictive tools. This study aimed to develop a stable machine learning model for predicting BM in a large, diverse patient population.Methods\n\nWe conducted a retrospective cohort study of 186,351 primary BC patients from the SEER program, the National Cancer Database, and a multi-center Chinese cohort. Patients were split into training and validation sets. Candidate predictors included demographic, clinicopathological, biomarker, and treatment-related variables, including surgery, chemotherapy, endocrine therapy, neoadjuvant therapy, and radiotherapy. LASSO regression was used for feature selection. Nine machine learning models were developed and compared using area under the curve (AUC) and decision curve analysis. The optimal model was deployed as an online tool.Results\n\nAmong 186,351 patients, 2,982 (1.6%) developed BM. In baseline comparisons, patients who developed BM more frequently exhibited advanced disease characteristics and different treatment patterns, likely reflecting more aggressive disease presentation. Key predictors retained in the final parsimonious model included AJCC N/M/T stage, age, and surgery status. The LASSO-regularized Logistic Regression model achieved superior discrimination in the validation set, with an AUC of 0.907 (95% CI: 0.857-0.958). The model demonstrated good calibration, clinical net benefit, and interpretability, with SHAP analysis confirming the contribution of each retained predictor to individual risk estimates. Survival analysis confirmed the poor prognosis associated with BM (p < 0.001). An interactive online tool was developed for real-time risk estimation.Conclusion\n\nWe developed and internally validated a accurate, clinically translatable model for predicting BM in BC using over 186,000 patients from North American and Asian cohorts. This web-based tool can assist clinicians in identifying high-risk individuals, enabling personalized management and potentially improving patient outcomes.", "abstract_shown": true, "affiliations": [ "Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, China. yquan646908@gmail.com.", "Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, China.", "Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, 350025, China.", "The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, 361000, China.", "Department of Breast Surgery, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, 362000, China.", "Nanyang Central Hospital, Nanyang, Henan, 473000, China.", "Department of Breast Surgery, Sanming First Hospital Affiliated to Fujian Medical University, Sanming, Fujian, 365001, China.", "Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515000, China.", "Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA.", "The First Hospital of Putian City, Putian, Fujian, 351100, China.", "Fujian Key Laboratory of Aptamers Technology, Affiliated Dongfang Hospital of School of Medicine (the 900th Hospital), Xiamen University, Fuzhou, Fujian, 350025, China.", "Department of Medical Oncology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, 362000, China.", "Department of Infection Disease, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, 362000, China.", "Department of Breast and Thyroid Surgery, Shaoxing People's Hospital, Shaoxing, China.", "Department of Breast Surgery, Nanyang Second People's Hospital, Nanyang, Henan, 473000, China.", "Department of Breast Surgery, Peking University Shougang Hospital, Beijing, China.", "Daqing Oilfield General Hospital, Daqing, Heilongjiang, China.", "The First Hospital of Putian City, Putian, Fujian, 351100, China. 18559339379@163.com.", "Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515000, China. medyzy@163.com.", "Department of Breast Surgery, Sanming First Hospital Affiliated to Fujian Medical University, Sanming, Fujian, 365001, China. fjsmcwx@fjmu.edu.cn.", "Nanyang Central Hospital, Nanyang, Henan, 473000, China. 20090957@qq.com.", "Department of General Surgery, The 900th Hospital of Joint Logistic Support Force, Fuzhou, Fujian, 350025, China. xiao84chun@163.com.", "The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, 361000, China. yhsu@xmu.edu.cn.", "Jilin Cancer Hospital, Changchun, Jilin Province, China. znq300@163.com.", "Department of Breast Surgery, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, 362000, China. debochensr@163.com.", "Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, China. niuming2024@126.com.", "Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, China. han_jg112@163.com." ], "article_type": "research article", "arxiv": null, "author_total": 43, "authors": [ { "affs": [ 0 ], "name": "Quan Yuan", "orcid": "0009-0003-8060-2158" }, { "affs": [ 1 ], "name": "Yupeng Sha" }, { "affs": [ 2 ], "name": "Rui Yu" }, { "affs": [ 3 ], "name": "Hao Yu" }, { "affs": [ 4 ], "name": "Rongjie Ye" }, { "affs": [ 5 ], "name": "Yi Du" }, { "affs": [ 6 ], "name": "Hanqing Deng" }, { "affs": [ 7 ], "name": "Liqun Wang" }, { "affs": [ 8 ], "name": "Zhihao Lei" }, { "affs": [ 9 ], "name": "Kaizhou Chen" }, { "affs": [ 9 ], "name": "Jian Cai" }, { "affs": [ 1 ], "name": "Xiaoming Li" }, { "affs": [ 1 ], "name": "Yixin Liu" }, { "affs": [ 1 ], "name": "Yige Lu" }, { "affs": [ 1 ], "name": "Hui Pang" }, { "affs": [ 10 ], "name": "Shuiliang Wang" }, { "affs": [ 2 ], "name": "Taozhu Ye" }, { "affs": [ 2 ], "name": "Youzhuang Wu" }, { "affs": [ 11 ], "name": "Xiaofeng Li" }, { "affs": [ 4 ], "name": "Zhengda Chen" }, { "affs": [ 4 ], "name": "Junjing Li" }, { "affs": [ 12 ], "name": "Shuqi Yang" }, { "affs": [ 7 ], "name": "Kangni Chen" }, { "affs": [ 7 ], "name": "Qinpei Ke" }, { "affs": [ 7 ], "name": "Yongxuan Yuan" }, { "affs": [ 7 ], "name": "Lierui Chen" }, { "affs": [ 7 ], "name": "Fan Wang" }, { "affs": [ 7 ], "name": "Yuxin Cheng" }, { "affs": [ 1 ], "name": "Boqian Yu" }, { "affs": [ 13 ], "name": "Yuancong Jiang" }, { "affs": [ 14 ], "name": "Pengfei He" }, { "affs": [ 15 ], "name": "Kejie Zhang" }, { "affs": [ 16 ], "name": "Fei Gao" }, { "affs": [ 17 ], "name": "Zhichuan He" }, { "affs": [ 18 ], "name": "Zhiyang Li" }, { "affs": [ 19 ], "name": "Wenxin Chen" }, { "affs": [ 20 ], "name": "Zheng Wang" }, { "affs": [ 21 ], "name": "Chunhong Xiao" }, { "affs": [ 22 ], "name": "Yonghui Su" }, { "affs": [ 23 ], "name": "Naiqian Zhang" }, { "affs": [ 24 ], "name": "Debo Chen" }, { "affs": [ 25 ], "name": "Ming Niu" }, { "affs": [ 26 ], "name": "Jiguang Han" } ], "bibtex": "https://research.draconitic.com/paper/2mrhcx5fh4yqz.bib", "cited_by": 1, "doi": "10.1186/s12967-026-08675-0", "fulltext_url": "https://europepmc.org/articles/PMC13685199", "id": "2mrhcx5fh4yqz", "issns": [ "1479-5876" ], "landing_url": "https://europepmc.org/article/MED/42902382", "language": "eng", "licence": "CC-BY-NC-ND", "licence_label": "CC BY-NC-ND", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/", "metadata_licence": null, "native_subjects": [ [ "lcc", "R" ], [ "openalex", "2740|Brain Metastases and Treatment" ] ], "openalex": "W7170295234", "pdf_url": "https://europepmc.org/articles/PMC13685199?pdf=render", "pmcid": "PMC13685199", "pmid": "42902382", "published": "2026-07-01", "publisher": "BMC", "ris": "https://research.draconitic.com/paper/2mrhcx5fh4yqz.ris", "sources": [ { "first_seen": "2026-10-30T12:19:41Z", "native_id": "MED:42902382", "source": "europepmc", "url": "https://europepmc.org/article/MED/42902382" }, { "first_seen": "2026-10-30T13:46:19Z", "native_id": "2bc71337d99a414fbc1cc5abd35e868a", "source": "doaj", "url": "https://doi.org/10.1186/s12967-026-08675-0" } ], "subjects": [ { "field": "medicine", "origin": "openalex:2740|Brain Metastases and Treatment", "subfield": "general" } ], "title": "Interpretable machine learning model for brain metastasis in breast cancer: a large-scale, multi-center study.", "title_text": "Interpretable machine learning model for brain metastasis in breast cancer: a large-scale, multi-center study.", "type": "peer-reviewed", "updated_at": "2026-10-30T13:46:19Z", "url": "https://research.draconitic.com/paper/2mrhcx5fh4yqz", "venue": "Journal of translational medicine", "versions": [] }