Trial reportEuropean journal of medical research2023
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.
Trial report in European journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis 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
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Review
- Artificial Intelligence in Early Breast Cancer Detection: A Systematic Review of Innovations in Preventive Women's Healthcare.Healthcare (Basel, Switzerland) · 2026Review
- Prediction of myelosuppression in cervical cancer after concurrent chemoradiotherapy by CT radiomics-based model.BMC medical imaging · 2026Article
- Machine learning and SHAP-based risk assessment of PICC-related bloodstream infections in premature infants at the time of clinical suspicion.Pediatric research · 2026Article
- Machine learning-based early survival prediction in early-onset hepatocellular carcinoma: a SEER-based multi-model comparative study.Translational cancer research · 2026Article
- Multicenter study of the diagnostic value of erythrocyte morphology assessment by the EH-2090 for differentiation of glomerular and non-glomerular hematuria.BMC nephrology · 2026Article
- Article
- Machine learning prediction of cardiovascular disease risk progression from sulfur dioxide exposure in longitudinal population studies in China.BMC cardiovascular disorders · 2026Article
- Diagnostic potential of neutrophil extracellular traps in ulcerative colitis: a gene-based predictive model.Molecular and cellular biochemistry · 2026Article
- Molecular subtyping and immune microenvironment heterogeneity in pediatric influenza-associated prolonged multiple organ dysfunction syndrome.Translational pediatrics · 2026Article
- Multi-Omics Analysis Identified ADAM7 as a Biomarker and Therapeutic Target for End-Stage Renal Disease.Kidney & blood pressure research · 2026Article
- Integrated single-cell and transcriptomic profiling identifies machine-learning-based pyroptosis biomarkers in IBD.Frontiers in immunology · 2026Article
- Machine learning for chemotherapy decision-making in breast cancer using large language model.Frontiers in digital health · 2026Article
- An explainable machine learning model predicts pediatric varicella encephalitis.Frontiers in cellular and infection microbiology · 2026Article
- Identification and validation of circadian rhythm and astrocyte-associated diagnostic and therapeutic model for cirrhosis encephalopathy patients via integrative bioinformatic pipelines andFrontiers in neuroscience · 2026Article
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- m5C: Novel Diagnostic and Drug Repurposing Targets for Nonalcoholic Steatohepatitis.International journal of genomics · 2026Article
- A transcriptomic signature that predicts prehypertension in adolescence and higher systolic blood pressure in childhood.JCI insight · 2025Article
- Identification of Key Biomarkers for Systemic Lupus Erythematosus Progression and Therapy Response: A Bulk RNA-Sequencing-Based Bioinformatics Study.Brain and behavior · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundBreast cancer (BC) is the most common malignant tumor around the world. Timely detection of the tumor progression after treatment could improve the survival outcome of patients. This study aimed to develop machine learning models to predict events (defined as either (1) the first tumor relapse locally, regionally, or distantly; (2) a diagnosis of secondary malignant tumor; or (3) death because of any reason.) in BC patients post-treatment.
methodsThe patients with the response of stable disease (SD) and progressive disease (PD) after neoadjuvant chemotherapy (NAC) were selected. The clinicopathological features and the survival data were recorded in 1 year and 5 years, respectively. Patients were randomly divided into the training set and test set in the ratio of 8:2. A random forest (RF) and a logistic regression were established in both of 1-year cohort and the 5-year cohort. The performance was compared between the two models. The models were validated using data from the Surveillance, Epidemiology, and End Results (SEER) database.
resultsA total of 315 patients were included. In the 1-year cohort, 197 patients were divided into a training set while 87 were into a test set. The specificity, sensitivity, and AUC were 0.800, 0.833, and 0.810 in the RF model. And 0.520, 0.833, and 0.653 of the logistic regression. In the 5-year cohort, 132 patients were divided into the training set while 33 were into the test set. The specificity, sensitivity, and AUC were 0.882, 0.750, and 0.829 in the RF model. And 0.882, 0.688, and 0.752 of the logistic regression. In the external validation set, of the RF model, the specificity, sensitivity, and AUC were 0.765, 0.812, and 0.779. Of the logistics regression model, the specificity, sensitivity, and AUC were 0.833, 0.376, and 0.619.
conclusionThe RF model has a good performance in predicting events among BC patients with SD and PD post-NAC. It may be beneficial to BC patients, assisting in detecting tumor recurrence.
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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.