ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2024
Deep learning-based automatic segmentation of cardiac substructures for lung cancers.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 20 citations in OpenAlex.
- Evaluation of AI-derived LAD artery dose metrics for survival stratification in stage III NSCLC: A secondary analysis of RTOG 0617 trial.Medical physics · 2026Trial
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- AI-surrogate structure and dose correlation for left anterior descending artery in lung stereotactic body radiotherapy.Journal of applied clinical medical physics · 2026Article
- Cardiac substructure dosimetry in thoracic radiotherapy: moving beyond mean heart dose and toward precision cardiac risk mitigation.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Review
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.Cardio-oncology (London, England) · 2026Review
- Heart Substructure Radiation Dose and Cardiac Outcomes: Contemporary Evidence and Actionable Opportunities: JACC CardioOncology State-of-the-Art Review.JACC. CardioOncology · 2026Review
- The illusory correlation between geometrical similarity and physician satisfaction in heart auto-segmentation revealed by a physician-blind test.Journal of applied clinical medical physics · 2026Article
- Changes in Cardiac Function in Patients Receiving Radiation Therapy for Lung Cancer.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2026Article
- Transformer-based cardiac substructure segmentation from contrast and non-contrast computed tomography for radiotherapy planning.Physics and imaging in radiation oncology · 2026Article
- Radiation Doses to Cardiac Substructures Predict Elevation in High-sensitivity Cardiac Troponin T Levels in Radiation Therapy for Lung Cancer.International journal of radiation oncology, biology, physics · 2026Article
- Comparative analysis of artificial intelligence-based contouring of cardiac substructures on computed tomography scans for radiation therapy.Physics and imaging in radiation oncology · 2026Article
- Modality-AGnostic image Cascade (MAGIC) for multi-modality cardiac substructure segmentation.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy.Advances in radiation oncology · 2025Article
- Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation.ArXiv · 2025Article
- Radiation-induced cardiac substructure damage and dose constraints: a review.Radiation oncology (London, England) · 2025Review
- Cardiac Effects of Modern Breast Radiation Therapy in Patients Receiving Systemic Cancer Therapy.JACC. CardioOncology · 2025Article
- Stereotactic arrhythmia radioablation for ventricular tachycardia: a review of clinical trials and emerging roles of imaging.Journal of radiation research · 2025Review
- Fully Automated Assessment of Cardiac Chamber Volumes and Myocardial Mass on Non-Contrast Chest CT with a Deep Learning Model: Validation Against Cardiac MR.Diagnostics (Basel, Switzerland) · 2024Article
- Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers.Diagnostics (Basel, Switzerland) · 2024Article
- Cardiac substructure delineation in radiation therapy - A state-of-the-art review.Journal of medical imaging and radiation oncology · 2024Review
Corrections and comments
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Authors and funding
20 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeAccurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning-based auto-segmentation models for cardiac substructures. MATERIALS AND
methodsNineteen cardiac substructures (whole heart, 4 heart chambers, 6 great vessels, 4 valves, and 4 coronary arteries) in 100 patients treated for non-small cell lung cancer were manually delineated by two radiation oncologists. The valves and coronary arteries were delineated as planning risk volumes. An nnU-Net auto-segmentation model was trained, validated, and tested on this dataset with a split ratio of 75:5:20. The auto-segmented contours were evaluated by comparing them with manually drawn contours in terms of Dice similarity coefficient (DSC) and dose metrics extracted from clinical plans. An independent dataset of 42 patients was used for subjective evaluation of the auto-segmentation model by 4 physicians.
resultsThe average DSCs were 0.95 (+/- 0.01) for the whole heart, 0.91 (+/- 0.02) for 4 chambers, 0.86 (+/- 0.09) for 6 great vessels, 0.81 (+/- 0.09) for 4 valves, and 0.60 (+/- 0.14) for 4 coronary arteries. The average absolute errors in mean/max doses to all substructures were 1.04 (+/- 1.99) Gy and 2.20 (+/- 4.37) Gy. The subjective evaluation revealed that 94% of the auto-segmented contours were clinically acceptable.
conclusionWe demonstrated the effectiveness of our nnU-Net model for delineating cardiac substructures, including coronary arteries. Our results indicate that this model has promise for studies regarding radiation dose to cardiac substructures.
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