ArticleScientific reports2022
Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- An isodose-constrained automatic treatment planning strategy using a multicriteria predicted dose rating.Medical physics · 2025Article
- A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans.Physics and imaging in radiation oncology · 2025Article
- 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
- Reply: Deep Inspiration Breath Hold to Reduce Cardiovascular Disease Risk for Breast Radiotherapy: Challenges and Opportunities.JACC. CardioOncology · 2024Article
- Impact of deep inspiration breath hold, surface-guided radiotherapy, and daily CBCT on the organs at risk in breast cancer radiotherapy.Scientific reports · 2024Article
- Identification of variables and development of a prediction model for DIBH eligibility in left-sided breast cancer radiotherapy: a prospective cohort study with temporal validation.Radiation oncology (London, England) · 2024Observational
- Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- A quantitative evaluation method utilizing the homology concept to assess the state of chromatin within the nucleus of lung cancer.Scientific reports · 2023Article
- 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.Experimental and therapeutic medicine · 2023Article
- Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients.Current oncology (Toronto, Ont.) · 2023Article
- Deep Learning-based Lung dose Prediction Using Chest X-ray Images in Non-small Cell Lung Cancer Radiotherapy.Journal of medical physicsArticle
- 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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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a deep learning chest X-ray model for cardiac dose prediction to select patients with a potentially high risk of cardiac irradiation and need for DIBH radiotherapy. We used 103 pairs of anteroposterior and lateral chest X-ray data of left-sided breast cancer patients (training cohort: n = 59, validation cohort: n = 19, test cohort: n = 25). All patients underwent breast-conserving surgery followed by DIBH radiotherapy: the treatment plan consisted of three-dimensional, two opposing tangential radiation fields. The prescription dose of the planning target volume was 42.56 Gy in 16 fractions. A convolutional neural network-based regression model was developed to predict the mean heart dose (∆MHD) reduction between free-breathing (MHD
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