ArticleFrontiers in public health2022
Machine learning predicts the prognosis of breast cancer patients with initial bone metastases.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis.BMC medical research methodology · 2025Pooled it
- Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy.European journal of medical research · 2023Trial
- Review
- Breast cancer survival prediction using machine learning and multimodal data for personalized care plan.BMC cancer · 2026Article
- Development and validation of explainable machine learning models for the prediction of survival in patients with M1 breast cancer.Gland surgery · 2026Article
- Development and validation of a prognostic model for stage IV breast cancer based on primary tumor resection with machine learning methods: retrospective cohort study.Frontiers in endocrinology · 2026Article
- Development and validation of an interpretable machine learning-based predictive model for breast cancer bone metastasis.Frontiers in oncology · 2026Article
- A comparative analysis of parametric survival models and machine learning methods in breast cancer prognosis.Scientific reports · 2025Article
- Options for postoperative radiation therapy in patients with de novo metastatic breast cancer.Breast (Edinburgh, Scotland) · 2025Article
- Prognostic factors analysis and nomogram construction of breast cancer patients lung metastases and bone metastases.Surgery open science · 2025Article
- Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.Journal of bone oncology · 2025Review
- Development and validation of a nomogram for predicting bone metastasis in breast cancer: a retrospective study.Frontiers in surgery · 2025Article
- A Machine-Learning Model for the Prediction of Triple-Negative Breast Cancer Based on Multiparameter MRI.Breast cancer (Dove Medical Press) · 2025Article
- Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer.Frontiers in endocrinology · 2025Article
- Random survival forest predicts survival in patients with metastatic laryngeal and hypopharyngeal cancer and the prognostic benefits of surgery and radiotherapy.Journal of Cancer · 2025Article
- Novel models by machine learning to predict the risk of cardiac disease-specific death in young patients with breast cancer.Discover oncology · 2024Article
- Machine learning-based analysis identifies a 13-gene prognostic signature to improve the clinical outcomes of colorectal cancer.Journal of gastrointestinal oncology · 2024Article
- Development of a predictive model for patients with bone metastases referred to palliative radiotherapy: Secondary analysis of a multicenter study (the PRAIS trial).Cancer medicine · 2024Observational
- Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.Current medical science · 2024Article
- Machine learning predicts the risk of osteoporosis in patients with breast cancer and healthy women.Journal of cancer research and clinical oncology · 2024Article
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Bone is the most common metastatic site of patients with advanced breast cancer and the survival time is their primary concern; however, we lack accurate predictive models in clinical practice. In addition to this, primary surgery for breast cancer patients with bone metastases is still controversial. Method: The data used for analysis in this study were obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of patients with bone metastatic breast cancer (BMBC). Through cross-validation, we constructed an XGBoost model to predicting survival in patients with BMBC. We also investigated the prognosis of patients treated with neoadjuvant chemotherapy plus surgical and chemotherapy alone using propensity score matching and K-M survival analysis. Results: Our validation results showed that the model has high sensitivity, specificity, and correctness, and it is the most accurate one to predict the survival of patients with BMBC (1-year AUC = 0.818, 3-year AUC = 0.798, and 5-year survival AUC = 0.791). The sensitivity of the 1-year model was higher (0.79), while the specificity of the 5-year model was higher (0.86). Interestingly, we found that if the time from diagnosis to therapy was ≥1 month, patients with BMBC had even better survival than those who started treatment immediately (HR = 0.920, 95%CI 0.869-0.974, Conclusion: We constructed an AI model to provide a quantitative method to predict the survival of patients with BMBC, and our validation results indicate that this model should be highly reproducible in a similar patient population. We also identified potential prognostic factors for patients with BMBC and suggested that primary surgery followed by neoadjuvant chemotherapy might increase survival in a selected subgroup of patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.