ArticleScientific reports2025
Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Machine learning-based prediction of survival in breast cancer patients with lung metastasis: a SEER-based study.Translational cancer research · 2026Article
- Three-dimensional spheroid models in breast cancer: tumor microenvironment complexity, cancer stem cell-driven resistance, and translational model integration.Journal of translational medicine · 2026Review
- A time‑dependent risk prediction model for distant metastasis in early‑stage breast cancer based on explainable ensemble learning: a retrospective cohort study.Gland surgery · 2026Article
- Breast cancer survival prediction using machine learning and multimodal data for personalized care plan.BMC cancer · 2026Article
- Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery.Translational cancer research · 2026Article
- A distributed fusion framework for breast cancer recurrence prediction using MapReduce.Scientific reports · 2026Article
- Leveraging AI solutions for sustainable practice in pediatric radiology: a practical guide and an educational tool.Pediatric radiology · 2026Review
- Identifying Prognostic Factors in Brain Metastasis Patients Using MRI Morphological Features: A Machine Learning and Survival Analysis Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Novel explainable deep learning based drug sensitivity prediction for early treatment of breast cancer.Scientific reports · 2026Article
- A unified multi modal transformer framework for breast cancer recurrence prediction and survival analysis.Scientific reports · 2026Article
- Explainable AI for Predicting Mortality Risk in Metastatic Cancer: Retrospective Cohort Study Using the Memorial Sloan Kettering-Metastatic Dataset.JMIR cancer · 2026Article
- Exploring the recurrence and metastasis of breast invasive ductal carcinoma based on machine learning and survival analysis.Frontiers in oncology · 2026Article
- A pan-cancer multi-omicFrontiers in artificial intelligence · 2026Article
- Construction and validation of the prediction model for kinesiophobia in older adults with chronic low back pain.BMC geriatrics · 2025Article
- Deep Learning-Based Recurrence Prediction in HER2-Low Breast Cancer: Comparison of MRI-Alone, Clinicopathologic-Alone, and Combined Models.Diagnostics (Basel, Switzerland) · 2025Article
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns. Our methodology utilized three predictive strategies: assessing recurrence risk, differentiating local from distant recurrences, and identifying potential metastatic sites. Key prognostic factors were identified through survival analysis. LightGBM, XGBoost, and Random Forest models were employed and validated against data from the Baheya Foundation. The models demonstrated strong performance; the survival analysis achieved a C-index of 0.837. The LightGBM model reached an AUC of 92% in predicting recurrences, while XGBoost and Random Forest models distinguished recurrence types with up to 86% accuracy, and they effectively differentiated between bone metastasis and all other locations combined (brain, liver, and lungs). This study highlights the significant potential of machine learning in advancing breast cancer management and sets a new benchmark for predictive analytics. Future research will integrate genetic data to further enhance these models.
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