ArticleBMC geriatrics2022
The impact of chemotherapy and survival prediction by machine learning in early Elderly Triple Negative Breast Cancer (eTNBC): a population based study from the SEER database.
Article in BMC geriatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 4 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- The translational chasm in machine learning for triple-negative breast cancer: a quantitative landscape assessment.Frontiers in oncology · 2026Pooled it
- Assessing the clinical, humanistic, and economic impact of early cancer diagnosis: a systematic literature review.Frontiers in oncology · 2025Pooled it
- Frequency of use and characterization of frailty assessments in observational studies on older women with breast cancer: a systematic review.BMC geriatrics · 2024Pooled it
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled 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
- A LASSO-based nomogram for predicting breast cancer-specific survival in elderly patients after surgery: a large retrospective cohort study.Translational cancer research · 2026Article
- Development and Validation of Machine Learning Models for Predicting 7-Day Mortality in Critically Ill Patients with Traumatic Spinal Cord Injury: A Multicenter Retrospective Study.Neurocritical care · 2026Article
- Impact of surgical approach and survival prediction of malignant phyllode tumor by machine learning.Updates in surgery · 2026Article
- Survival prediction in triple-negative breast cancer: a Cox model with fairness assessment using ISO/IEC TR 24027:2021 in a MENA cohort.Exploration of targeted anti-tumor therapy · 2026Article
- Development of predictive models for the prognosis of triple-negative breast cancer using multiple transcriptomic analyses.PloS one · 2026Article
- Long term efficacy of adjuvant chemotherapy in elderly patients with early stage breast cancer assessed through SEER database analysis.Scientific reports · 2025Article
- Article
- The times they are AI-changing: AI-powered advances in the application of extracellular vesicles to liquid biopsy in breast cancer.Extracellular vesicles and circulating nucleic acids · 2025Review
- Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy.Functional & integrative genomics · 2024Review
- Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.The Journal of international medical research · 2023Article
- Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study.Cancer science · 2023Article
- Survival benefit of adjuvant chemotherapy in elderly patients with stage I triple-negative breast cancer: a cohort study based on the SEER database.Translational cancer research · 2023Article
- Prognostic factors and predictive nomogram models for early death in elderly patients with hepatocellular carcinoma: a population-based study.Frontiers in molecular biosciences · 2023Article
- Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe aimed to analysis the impact of chemotherapy and establish prediction models of prognosis in early elderly triple negative breast cancer (eTNBC) by using machine learning.
methodsWe enrolled 4,696 patients in SEER Database who were 70 years or older, diagnosed with primary early TNBC(larger than 5 mm), from 2010 to 2016. The propensity-score matched method was utilized to reduce covariable imbalance. Univariable and multivariable analyses were used to compare breast cancer-specific survival(BCSS) and overall survival(OS). Nine models were developed by machine learning to predict the 5-year OS and BCSS for patients received chemotherapy.
resultsCompared to matched patients in no-chemotherapy group, multivariate analysis showed a better survival in chemotherapy group. Stratified analyses by stage demonstrated that patients with stage II and stage III other than stage I could benefit from chemotherapy. Further investigation in stage II found that chemotherapy was a better prognostic indicator for patients with T2N0M0 and stage IIb, but not in T1N1M0. Patients with grade III could achieve a better survival by receiving chemotherapy, but those with grade I and II couldn't. With 0.75 in 5-year BCSS and 0.81 in 5-year OS for AUC, the LightGBM outperformed other algorithms.
conclusionFor early eTNBC patients with stage I, T1N1M0 and grade I-II, chemotherapy couldn't improve survival. Therefore, de-escalation therapy might be appropriate for selected patients. The LightGBM is a trustful model to predict the survival and provide precious systemic treatment for patients received chemotherapy.
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