ArticleExperimental and therapeutic medicine2023
Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath‑hold radiotherapy after breast cancer surgery.
Article in Experimental and therapeutic medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- Optimizing Cardiac Dose Prediction in Left-Sided Breast Cancer Radiation Therapy: Clinical Strategies for Identifying Beneficiaries of the Deep Inspiration Breath-Hold Technique.Advances in radiation oncology · 2026Article
- Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy.Diagnostics (Basel, Switzerland) · 2025Article
- Estimation of heart dose in left breast cancer radiotherapy: Assessment of vDIBH feasibility using the supervised machine learning algorithm.Journal of applied clinical medical physics · 2025Article
- 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.Journal of medical physicsArticle
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Authors and funding
16 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increased heart dose during postoperative radiotherapy (RT) for left-sided breast cancer (BC) can cause cardiac injury, which can decrease patient survival. The deep inspiration breath-hold technique (DIBH) is becoming increasingly common for reducing the mean heart dose (MHD) in patients with left-sided BC. However, treatment planning and DIBH for RT are laborious, time-consuming and costly for patients and RT staff. In addition, the proportion of patients with left BC with low MHD is considerably higher among Asian women, mainly due to their smaller breast volume compared with that in Western countries. The present study aimed to determine the optimal machine learning (ML) model for predicting the MHD after RT to pre-select patients with low MHD who will not require DIBH prior to RT planning. In total, 562 patients with BC who received postoperative RT were randomly divided into the trainval (n=449) and external (n=113) test datasets for ML using Python (version 3.8). Imbalanced data were corrected using synthetic minority oversampling with Gaussian noise. Specifically, right-left, tumor site, chest wall thickness, irradiation method, body mass index and separation were the six explanatory variables used for ML, with four supervised ML algorithms used. Using the optimal value of hyperparameter tuning with root mean squared error (RMSE) as an indicator for the internal test data, the model yielding the best F2 score evaluation was selected for final validation using the external test data. The predictive ability of MHD for true MHD after RT was the highest among all algorithms for the deep neural network, with a RMSE of 77.4, F2 score of 0.80 and area under the curve-receiver operating characteristic of 0.88, for a cut-off value of 300 cGy. The present study suggested that ML can be used to pre-select female Asian patients with low MHD who do not require DIBH for the postoperative RT of BC.
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