ArticleBMC medical informatics and decision making2023
Machine learning-based models for the prediction of breast cancer recurrence risk.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
91 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- ESR Essentials: artificial intelligence in breast imaging-practice recommendations by the European Society of Breast Imaging.European radiology · 2026Guideline
- The predictive value of multiple artificial intelligence models in axillary lymph node metastasis of breast cancer detected by ultrasound - a network meta-analysis.Frontiers in oncology · 2026Pooled it
- Development and validation of a machine learning-based prediction model for prolonged length of stay after laparoscopic gastrointestinal surgery: a secondary analysis of the FDP-PONV trial.BMC gastroenterology · 2025Trial
- 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
- An interpretable breast cancer risk stratification model via multi-omics integration: multi-method development and cross-cohort validation.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Risk Factors for Cardiovascular Disease: Epidemiology, Screening, Prevention, and Therapeutic Interventions.MedComm · 2026Review
- Recurrence of Basal Cell Carcinoma across Different Treatment Modalities: A Nationwide Study with 8 Years of Follow-up and Modelled Prediction.Acta dermato-venereologica · 2026Article
- Article
- Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.BMC cancer · 2026Article
- Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma.NPJ precision oncology · 2026Article
- Development of a risk prediction model for second primary thyroid cancer in female breast cancer patients based on machine learning algorithms.Scientific reports · 2026Article
- Machine learning prediction of in-hospital mortality risk among hospitalized patients with secondary bloodstream infection: a retrospective cohort study.BMC infectious diseases · 2026Article
- Machine learning prediction of postoperative recurrence in bladder cancer using clinical and laboratory indicators.Scientific reports · 2026Article
- Breast cancer detection via targeted enzymatic methyl sequencing of plasma cell-free DNA.Molecular genetics and genomics : MGG · 2026Article
- Exploring graph-based models for predicting active compounds against triple-negative breast cancer.Molecular diversity · 2026Article
- Validation of the postoperative prognostication tool PREDICT version 2.2 and 3.0 using data from the National cancer center hospital in Japan.Breast cancer (Tokyo, Japan) · 2026Article
- Serum peptidomics by MALDI-TOF MS coupled with machine learning approaches for diagnosis of primary liver cancer.Analytical and bioanalytical chemistry · 2026Article
- Predicting adverse prognostic outcomes in hospitalized breast cancer patients: development and validation of a risk model.BMC medical informatics and decision making · 2026Article
- Development and validation of an interpretable machine learning model for postoperative radiotherapy decision-making in ypN0 breast cancer after neoadjuvant chemotherapy: a real-world study.BMC medical informatics and decision making · 2026Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Breast cancer is the most common malignancy diagnosed in women worldwide. The prevalence and incidence of breast cancer is increasing every year; therefore, early diagnosis along with suitable relapse detection is an important strategy for prognosis improvement. This study aimed to compare different machine algorithms to select the best model for predicting breast cancer recurrence. The prediction model was developed by using eleven different machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), support vector classification (SVC), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), decision tree, multilayer perceptron (MLP), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), Gaussian naive Bayes (GaussianNB), and light gradient boosting machine (LightGBM), to predict breast cancer recurrence. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score were used to evaluate the performance of the prognostic model. Based on performance, the optimal ML was selected, and feature importance was ranked by Shapley Additive Explanation (SHAP) values. Compared to the other 10 algorithms, the results showed that the AdaBoost algorithm had the best prediction performance for successfully predicting breast cancer recurrence and was adopted in the establishment of the prediction model. Moreover, CA125, CEA, Fbg, and tumor diameter were found to be the most important features in our dataset to predict breast cancer recurrence. More importantly, our study is the first to use the SHAP method to improve the interpretability of clinicians to predict the recurrence model of breast cancer based on the AdaBoost algorithm. The AdaBoost algorithm offers a clinical decision support model and successfully identifies the recurrence of breast cancer.
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What Socratic holds
Registered trials
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.