Why the study?
Deep inspiration breath-hold can reduce cardiac radiation exposure in left-sided breast cancer, but resource limitations require appropriate patient selection.
Does a machine learning-based tool accurately predict mean heart dose and identify left-sided breast cancer patients who benefit from deep inspiration breath-hold?
Does a machine learning-based tool accurately predict mean heart dose and identify left-sided breast cancer patients who benefit from deep inspiration breath-hold?
A machine learning tool using simple anatomical metrics from free-breathing CT scans can accurately predict mean heart dose and select left-sided breast cancer patients who would benefit from deep inspiration breath-hold to reduce cardiac radiation exposure.
May support selective DIBH to reduce cardiac dose in left-sided breast cancer; extends evidence for ML models but remains hypothesis-generating.[[1]](https://journals.lww.com/jomp/fulltext/2026/01000/machine_learning_based_prediction_of_mean_heart.8.aspx)[[1]](https://journals.lww.com/jomp/fulltext/2026/01000/machine_learning_based_prediction_of_mean_heart.8.
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 ( n = 25) with paired FB-DIBH scans validated predictions. Results: In the validation cohort ( n = 25), DIBH reduced mean heart dose by 34% (1.72–1.86 Gy, P < 0.001 for both techniques) and decreased high-risk patients (>5 Gy) by 69%–80%. Strong correlations were observed between FB predictions and DIBH-achieved doses for anatomical parameters and VMAT-2P ( r = 0.667–0.720, P < 0.001), with moderate correlation for VMAT-4P ( r = 0.545, P = 0.005). In the independent test cohort from the model development dataset ( n = 24), Elastic Net achieved mean absolute errors of 0.81–1.02 Gy. Logistic regression demonstrated 87.5% accuracy with 83%–92% sensitivity and 83%–92% specificity for VMAT-2P and VMAT-4P (area under the curve [AUC]: 0.85–0.94). The VMAT-5P technique showed reduced classification performance (58.3% accuracy, AUC: 0.83). 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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Patil et al. (2026) studied this question.
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