ArticleJournal of medical physics
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.
Article in Journal of medical physics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Deep inspiration breath-hold (DIBH) can reduce cardiac radiation exposure in left-sided breast cancer, but resource limitations necessitate appropriate patient selection. Purpose: To develop and evaluate a machine learning-based tool for predicting heart mean dose and identifying patients who would benefit from DIBH using simple anatomical predictors in left-sided breast cancer. Materials and Methods: A retrospective study analyzed 120 patients' treatment plans on free-breathing (FB) scans from left-sided postmastectomy breast cancer patients treated with volumetric modulated arc therapy (VMAT). All plans were generated using three techniques: VMAT 2-field plan (VMAT-2P), VMAT 4-field plan (VMAT-4P), and VMAT 5-field plan (VMAT-5P). Two anatomical predictors, maximum heart distance (MHD) and heart-to-PTV distance (HPD), were measured. Elastic Net regression was used for continuous dose prediction, whereas logistic regression was applied for binary classification of DIBH necessity, using a 5 Gy heart mean dose threshold. An independent cohort ( Results: In the validation cohort ( Conclusions: Machine learning software demonstrated accurate prediction of mean heart dose during pre-planning for left-sided breast cancer, enabling informed DIBH selection for cardiac sparing based on simple anatomical metrics from FB computed tomography (CT) scans.
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