ArticleJournal of applied clinical medical physics2025
Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Cardiac substructure dosimetry in thoracic radiotherapy: moving beyond mean heart dose and toward precision cardiac risk mitigation.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Review
- Review
- Radiation Doses to Cardiac Substructures Predict Elevation in High-sensitivity Cardiac Troponin T Levels in Radiation Therapy for Lung Cancer.International journal of radiation oncology, biology, physics · 2026Article
- Radiomics features and clinical factors for predicting restenosis following endovascular therapy in patients with peripheral artery disease.BMC medical informatics and decision making · 2026Article
- Imaging-based detection and prediction of radiation-induced cardiac toxicity: a narrative review.BJR open · 2026Review
- Application of fractal dimension combined with radiomics and clinical scores in recurrence prediction of atrial fibrillation after radiofrequency ablation.Frontiers in cardiovascular medicine · 2026Article
- Risk of cardiovascular-related death after radiotherapy for thoracic cancer.Radiation oncology (London, England) · 2025Article
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- Ensemble learning approach for prediction of early complications after radiotherapy for head and neck cancer using CT and MRI radiomic features.Scientific reports · 2025Article
- Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.Journal of applied clinical medical physics · 2025Article
- Cardioprotection for radiation-induced heart disease in breast cancer patients.Frontiers in pharmacology · 2025Review
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Machine Learning-based Prediction of Mean Heart Dose and Deep Inspiration Breath-hold Selection in Left-sided Breast Cancer Volumetric Modulated Arc Therapy Radiotherapy Planning.Journal of medical physicsArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeCardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential advantages of treatment methods with the risks of harm to healthy tissues, including the heart. There is currently a lack of comprehensive, data-driven evidence on effective risk stratification strategies. The aim of this study is to investigate the prediction of cardiotoxicity using machine learning methods combined with radiomics, clinical, and dosimetric features. MATERIALS AND
methodsA cohort of 83 left-sided breast cancer patients without a history of cardiac disease was examined. Two- and three-dimensional echocardiography were performed before and after 6 months of treatment to evaluate cardiotoxicity. Cardiac dose-volume histograms, demographic data, echocardiographic parameters, and ultrasound imaging radiomics features were collected for all patients. Toxicity modeling was developed with three feature selection methods and five classifiers in four separate groups (Dosimetric, Dosimetric + Demographic, Dosimetric + Demographic + Clinical, and Dosimetric + Demographic + Clinical + Imaging). The prediction performance of the models was validated using five-fold cross-validation and evaluated by AUCs.
results58% of patients showed cardiotoxicity 6 months after treatment. Mean left ventricular ejection fraction and Global longitudinal strain decreased significantly compared to pre-treatment (p-value < 0.001). After feature selection and prediction modeling, the Dosimetric, Dosimetric + Demographic, Dosimetric + Demographic + Clinical, Dosimetric + Demographic + Clinical + Imaging models showed prediction performance (AUC) up to 73%, 75%, 85%, and 97%, respectively.
conclusionIncorporating clinical and imaging features along with dose descriptors are beneficial for predicting cardiotoxicity after radiotherapy.
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