Journal of Cancer Science and Research
Open Access

Deep Learning Based Prediction of Radiotherapy-Induced Cardiotoxicity in Breast Cancer Survivors

Jonathan Wu^{* *}Correspondence: Jonathan Wu, Department of Medical Physics and Imaging Science, University of Barcelona, Barcelona, Spain, Email:

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Description

Radiotherapy remains one of the cornerstone treatments for breast cancer, significantly improving local tumor control and overall survival. However, in left-sided breast cancer, where the heart is often exposed to incidental radiation, long-term survivors face a heightened risk of developing radiation-induced cardiotoxicity. This adverse effect encompasses a range of cardiovascular complications, including pericarditis, coronary artery disease, myocardial fibrosis, and heart failure, often emerging years after treatment. Traditional methods for predicting these risks rely on dose–volume histograms and clinical factors, which fail to capture the complex, multidimensional interactions between patient anatomy, radiation dose distribution, and individual susceptibility. Recent advances in artificial intelligence, particularly Deep Learning (DL), offer transformative potential in predicting radiotherapyinduced cardiotoxicity with unprecedented accuracy by integrating imaging, dosimetric, and clinical data into predictive models.are complex and multifaceted. Exposure of cardiac tissues to ionizing radiation leads to endothelial cell damage, microvascular dysfunction, and chronic inflammation, culminating in myocardial fibrosis and vascular occlusion. The severity of these effects depends not only on the mean heart dose but also on spatial heterogeneity within the irradiated volume, the specific cardiac substructures involved such as the left anterior descending coronary artery or the left ventricle, and individual genetic predispositions. Traditional dose–response models based solely on the mean heart dose or dose-volume metrics cannot fully account for these spatial and biological complexities. Deep learning, however, offers a data-driven approach that can capture hidden spatial patterns of radiation injury and integrate them with patient-specific risk factors to enhance predictive performance.

In addition to imaging data, multimodal deep learning frameworks that combine clinical and radiomic features have shown significant promise. By incorporating variables such as age, smoking history, comorbidities, baseline cardiac function, and treatment modality, these hybrid models provide a comprehensive risk assessment. Radiomic features-quantitative descriptors derived from medical images such as texture, shape, and intensity-further enhance the model’s ability to detect subtle structural changes in cardiac tissue before overt clinical symptoms appear. For instance, pre and post treatment CT or Magnetic Resonance Imaging (MRI) can be analyzed using deep autoencoders to identify early markers of myocardial injury. Integrating such data with dose distribution maps allows the prediction of long-term cardiotoxicity with high sensitivity and specificity.

Another area of innovation lies in the application of Recurrent Neural Networks (RNNs) and transformer architectures to longitudinal data, enabling temporal modeling of cardiac changes over time. In breast cancer survivors undergoing followup imaging or echocardiographic monitoring, deep learning models can track progressive changes and dynamically update risk predictions. This temporal modeling provides a powerful tool for early intervention-allowing clinicians to adjust treatment or initiate cardioprotective strategies before irreversible damage occurs. Moreover, explainable AI techniques, such as saliency mapping and layer-wise relevance propagation, are being integrated to visualize which cardiac regions contribute most to the model’s predictions, thereby enhancing transparency and clinical interpretability.

One of the major challenges in developing reliable DL-based cardiotoxicity prediction models is the need for large, highquality datasets with consistent annotation and long-term followup. Cardiac toxicity events often occur years after radiotherapy, making prospective data collection difficult. Collaborative efforts between oncology centers, radiology departments, and cardiology clinics are essential to create multi-institutional datasets that capture diverse populations and imaging modalities. Federated learning has emerged as a promising solution to this challenge. By allowing multiple institutions to train a shared model on decentralized data without transferring patient information, federated learning preserves data privacy while expanding the diversity and size of training datasets. This approach accelerates model development and enhances generalizability across different healthcare settings.

Despite these advances, challenges remain before deep learning models can be routinely implemented in clinical practice.. Additionally, standardization of data preprocessing, validation protocols, and evaluation metrics is crucial to ensure reproducibility and regulatory acceptance. Ethical considerations, including algorithmic bias and data security, must also be addressed, particularly when dealing with sensitive medical imaging and patient outcomes.

Conclusion

Deep learning has opened new frontiers in predicting and managing radiotherapy-induced cardiotoxicity in breast cancer survivors. Through advanced data integration and pattern recognition, DL models can capture the intricate relationships between radiation dose, cardiac anatomy, and individual susceptibility that traditional models overlook. Although significant challenges related to data availability, interpretability, and clinical validation persist, the ongoing convergence of artificial intelligence, radiation oncology, and cardiovascular medicine holds tremendous promise. As research progresses toward explainable, generalizable, and clinically deployable models, deep learning is poised to become an indispensable tool in safeguarding the cardiovascular health of breast cancer survivors in the era of precision radiotherapy.