ArticlePhysics and imaging in radiation oncology2020
Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis.
Article in Physics and imaging in radiation oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 54 citations in OpenAlex.
- Interpretable Machine Learning for Choosing Radiation Dose-volume Constraints on Cardio-pulmonary Substructures Associated with Overall Survival in NRG Oncology RTOG 0617.International journal of radiation oncology, biology, physics · 2023Trial
- 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
- Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy.Advances in radiation oncology · 2025Article
- Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.Current cardiology reports · 2025Review
- Under-representation for Female Pelvis Cancers in Commercial Auto-segmentation Solutions and Open-source Imaging Datasets.Clinical oncology (Royal College of Radiologists (Great Britain)) · 2025Article
- Cardiopulmonary Substructure Doses are Not Correlated With Cardiorespiratory Fitness Among Breast Cancer Survivors Treated With Contemporary Radiation Therapy.Advances in radiation oncology · 2024Article
- Overview of cardiac toxicity from radiation therapy.Journal of medical imaging and radiation oncology · 2024Review
- Enhancing Precision in Cardiac Segmentation for Magnetic Resonance-Guided Radiation Therapy Through Deep Learning.International journal of radiation oncology, biology, physics · 2024Article
- Deep learning-based automatic segmentation of cardiac substructures for lung cancers.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024Article
- Assessment of heart-substructures auto-contouring accuracy for application in heart-sparing radiotherapy for lung cancer.BJR open · 2024Article
- Cardiac radiation dose is associated with inferior survival but not cardiac events in patients with locally advanced non-small cell lung cancer in the era of immune checkpoint inhibitor consolidation.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024Article
- A deep learning segmentation method to assess dose to organs at risk during breast radiotherapy.Physics and imaging in radiation oncology · 2023Article
- Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.Reviews in cardiovascular medicine · 2023Review
- Development of a comprehensive cardiac atlas on a 1.5 Tesla Magnetic Resonance Linear Accelerator.Physics and imaging in radiation oncology · 2023Article
- Death without progression as an endpoint to describe cardiac radiation effects in locally advanced non-small cell lung cancer.Clinical and translational radiation oncology · 2023Article
- Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation.Physical and engineering sciences in medicine · 2023Article
- Association of cardiac calcium burden with overall survival after radiotherapy for non-small cell lung cancer.Physics and imaging in radiation oncology · 2023Article
- Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans.Physics and imaging in radiation oncology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
background and purposeRadiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardio-pulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures. MATERIALS AND
methodsThe DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability.
resultsThe DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95-0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value
conclusionThe cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.