ArticleSurgery today2021
Machine learning to predict the cancer-specific mortality of patients with primary non-metastatic invasive breast cancer.
Article in Surgery today, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy and Utility of Preoperative Ultrasound-Guided Axillary Lymph Node Biopsy for Invasive Breast Cancer: A Systematic Review and Meta-Analysis.Computational intelligence and neuroscience · 2022Pooled it
- SEER-based machine learning prediction of bone metastasis in breast cancer: model development and validation.Gland surgery · 2025Article
- NELBI score: a new clinical calculator of thirty-day mortality following systemic anticancer therapy in breast cancer patients near the end of life.Breast cancer (Tokyo, Japan) · 2025Article
- Construction and interpretation of machine learning-based prognostic models for survival prediction among intestinal-type and diffuse-type gastric cancer patients.World journal of surgical oncology · 2024Article
- A personalized mRNA signature for predicting hypertrophic cardiomyopathy applying machine learning methods.Scientific reports · 2024Article
- Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models.Heliyon · 2024Article
- Article
- Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study.JMIR formative research · 2024Article
- Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia.BMC medical informatics and decision making · 2024Article
- Integrating Machine Learning and the SHapley Additive exPlanations (SHAP) Framework to Predict Lymph Node Metastasis in Gastric Cancer Patients Based on Inflammation Indices and Peripheral Lymphocyte Subpopulations.Journal of inflammation research · 2024Article
- Development and validation of interpretable machine learning models for postoperative pneumonia prediction.Frontiers in public health · 2024Article
- [Efficacy of machine learning modelsNan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model.BMC medical informatics and decision making · 2023Article
- Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage.Scientific reports · 2023Article
- Article
- Evaluation of Lee-Carter model to breast cancer mortality prediction in China and Pakistan.Frontiers in oncology · 2023Article
- Development and validation of a machine learning model for survival risk stratification after esophageal cancer surgery.Frontiers in oncology · 2022Article
- A Breast Cancer Prediction Model Based on a Panel from Circulating Exosomal miRNAs.BioMed research international · 2022Article
- Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery.Frontiers in medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe used five machine-learning algorithms to predict cancer-specific mortality after surgical resection of primary non-metastatic invasive breast cancer.
methodsThis study was a secondary analysis of data for 1661 women with primary non-metastatic invasive breast cancer. The overall patient population was divided into a training group and a test group at a ratio of 8:2 and python was used for machine learning to establish the prognosis model.
resultsThe machine-learning Gbdt algorithm for cancer-specific death caused by various factors showed the five most important factors, ranked from high to low as follows: the number of regional lymph node metastases, LDH, triglyceride, plasma fibrinogen, and cholesterol. Among the five algorithm models in the test group, the highest accuracy rate was by DecisionTree (0.841), followed by the gbm algorithm (0.838). Among the five algorithms, the AUC values from high to low were GradientBoosting (0.755), gbm (0.755), Logistic (0.733), Forest (0.715), and DecisionTree (0.677).
conclusionMachine learning can predict cancer-specific mortality after surgery for patients with primary non-metastatic invasive breast.
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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 OpenQuestion graph.