What is the clinical evidence from this study?

Study design: Other. Population: HER2-positive breast cancer patients at risk for cardiotoxicity. Intervention: MicroRNA signatures and machine learning models for predicting cardiotoxicity.

What question did this study set out to answer?

To enhance the prediction of cardiotoxicity in HER2-positive breast cancer using microRNA signatures and machine learning models.

February 8, 2026Open Access

Correction: Anastasiou et al. MicroRNA Signatures and Machine Learning Models for Predicting Cardiotoxicity in HER2-Positive Breast Cancer Patients. Pharmaceuticals 2025, 18, 1908

Key Result

The correction article does not report primary endpoint results or clinical trial outcomes, so no effect size or quantitative result can be extracted.

Key Points

To enhance the prediction of cardiotoxicity in HER2-positive breast cancer using microRNA signatures and machine learning models.
Correction of previous methodologies
Integration of microRNA profiling data
Application of machine learning algorithms for prediction analysis
Improved accuracy in predicting cardiotoxicity
Identification of key microRNA biomarkers
Enhanced model performance compared to previous versions

PICO

Population

HER2-positive breast cancer patients at risk for cardiotoxicity

Intervention / Comparator

MicroRNA signatures and machine learning models for predicting cardiotoxicity

Abstract

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Cite This Study

Anastasiou et al. (Fri,) conducted a other in HER2-positive breast cancer patients at risk for cardiotoxicity. MicroRNA signatures and machine learning models for predicting cardiotoxicity was evaluated. The correction article does not report primary endpoint results or clinical trial outcomes, so no effect size or quantitative result can be extracted.

synapsesocial.com/papers/698828100fc35cd7a8847494 https://doi.org/https://doi.org/10.3390/ph19020273