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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine

Mukesh Kumar Zope¹, Deepali Patil¹, Rishi Raj²

¹Department of Medical Physics, State Cancer Institute, Indira Gandhi Institute of Medical Sciences, Patna, Bihar, India.

Journal of Medical Physics

|April 6, 2026

Summary

Volumetric modulated arc therapy (VMAT) for left-sided breast cancer shows VMAT-4P as the optimal technique. Machine learning models accurately predict lung doses, aiding treatment planning.

Keywords:

Dosimetric comparison left-sided breast cancer lung dose prediction machine learning treatment planning volumetric modulated arc therapy

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Area of Science:

Radiation Oncology
Medical Physics
Machine Learning in Healthcare

Background:

Radiotherapy for left-sided breast cancer requires careful lung dose management.
Volumetric modulated arc therapy (VMAT) offers advanced dose delivery options.
Predictive modeling can improve treatment planning accuracy.

Purpose of the Study:

To compare the efficacy of different VMAT techniques (VMAT-2P, VMAT-4P, VMAT-5P) for left-sided breast cancer.
To develop machine learning-based predictive models for lung doses.
To identify key anatomical predictors of lung dose in this patient cohort.

Main Methods:

Retrospective analysis of 101 left-sided breast cancer patients.
Comparison of three VMAT techniques: VMAT-2P, VMAT-4P, and VMAT-5P.
Development of lung dose predictive models using regression algorithms (LASSO, Ridge, Linear Regression, Random Forest Regressor).

Main Results:

VMAT-4P demonstrated the lowest mean lung dose and V20 Gy values.
VMAT-5P showed the lowest exposure to low lung doses (V5 Gy).
Central lung distance (CLD) was the strongest predictor of lung dose; Random Forest Regressor yielded the best predictive performance for mean lung dose.

Conclusions:

VMAT-4P is recommended for left-sided breast cancer radiotherapy due to its balance of target coverage and organ-at-risk sparing.
Machine learning, particularly the Random Forest Regressor, effectively predicts mean lung dose, enhancing treatment planning.