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Clinically interpretable machine-learning model for predicting heart mean dose using simple BEV-based metrics.

Sathiyaraj Palanivel¹

¹Department of Radiation Physics, Kidwai Memorial Institute of Oncology, Bengaluru, Karnataka 560029, India.

Medical Dosimetry : Official Journal of the American Association of Medical Dosimetrists

|February 22, 2026

Summary

This summary is machine-generated.

Machine learning accurately predicts mean heart dose (MHD) in breast cancer radiotherapy using simple beam's-eye-view (BEV) metrics. This allows for early selection of cardiac-sparing techniques to reduce radiation-induced cardiac toxicity.

Keywords:

Beam’s-eye view Breast radiotherapy Geometric predictors Linear regression Logistic regression Mean heart dose

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Area of Science:

Radiation Oncology
Medical Physics
Machine Learning in Healthcare

Background:

Radiation-induced cardiac toxicity is a significant concern in left-sided breast cancer radiotherapy.
Mean heart dose (MHD) is a key predictor of long-term cardiac morbidity.
Accurate prediction of MHD is crucial for implementing cardiac-sparing strategies.

Purpose of the Study:

To develop a clinically interpretable machine learning model for predicting MHD.
To utilize simple beam's-eye-view (BEV)-based heart-projection metrics for MHD prediction.
To enable rapid pre-treatment assessment and selection of radiotherapy techniques.

Main Methods:

Retrospective analysis of 127 patients undergoing left breast/chest wall radiotherapy with supraclavicular nodal irradiation.
Measurement of heart projections (horizontal and vertical) from tangential fields.
Development of multivariable linear regression and logistic regression models using cross-validation.

Main Results:

The linear regression model showed strong predictive performance (R²=0.69, RMSE=0.61 Gy) and significant correlation (r=0.83, p<0.001).
Independent validation further improved prediction accuracy (R²=0.76, r=0.90, p<0.001).
The logistic classifier achieved 88% accuracy and high discrimination (AUC=0.95) for dose-based technique selection.

Conclusions:

Simple BEV-based heart-projection metrics can reliably predict MHD in left-sided breast cancer radiotherapy.
The developed machine learning models offer a practical tool for pre-treatment assessment.
This approach supports the early selection of cardiac-sparing radiotherapy techniques, potentially reducing cardiac morbidity.