ArticleMedical & biological engineering & computing2025
Predicting doxorubicin-induced cardiotoxicity in breast cancer: leveraging machine learning with synthetic data.
Article in Medical & biological engineering & computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Machine learning-enhanced baseline risk prediction models for cancer therapy related cardiac dysfunction: a systematic review and critical appraisal of methodological quality.European heart journal. Digital health · 2026Article
- Article
- Development and validation of a predictive model for cancer therapy-related cardiac dysfunction in breast cancer patients using echocardiographic indicators.American journal of cancer research · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Doxorubicin (DOXO) is a primary treatment for breast cancer but can cause cardiotoxicity in over 25% of patients within the first year post-chemotherapy. Recognizing at-risk patients before DOXO initiation offers pathways for alternative treatments or early protective actions. We analyzed data from 78 Brazilian breast cancer patients, with 34.6% developing cardiotoxicity within a year of their final DOXO dose. To address the limited sample size, we utilized the DAS (Data Augmentation and Smoothing) method, creating 4892 synthetic samples that exhibited high statistics fidelity to the original data. By integrating routine blood biomarkers (C-Reactive protein, total cholesterol, LDL-c, HDL-c, hematocrit, and hemoglobin) and two clinical measures (weighted smoking status and body mass index), our model achieved an AUROC of 0.85±0.10, a sensitivity of 0.89, and a specificity of 0.69, positioning it as a potential screening instrument. Notably, DAS outperformed the established methods, Adaptive Synthetic Sampling (ADASYN), Synthetic Minority Over-Sampling Technique (SMOTE), and Synthetic Data Vault (SDV), underscoring its promise for medical synthetic data generation and pioneering a cardiotoxicity prediction model specifically for DOXO.
Indexed as
Identifiers
39828884What OpenQuestion 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 OpenQuestion graph.