ArticleRadiation oncology (London, England)2022
Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study.
Article in Radiation oncology (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.International journal of computer assisted radiology and surgery · 2023Pooled it
- Deep learning for synthetic contrast-enhanced CT and MRI: a scoping review.European radiology · 2026Article
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.Cardio-oncology (London, England) · 2026Review
- A review of image processing and analysis of computed tomography images using deep learning methods.Physical and engineering sciences in medicine · 2025Review
- Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation.ArXiv · 2025Article
- Can diffusion-based generated magnetic resonance images predict glioma methylation accurately?Quantitative imaging in medicine and surgery · 2025Article
- Microbiota-host metabolism reprogramming in colorectal cancer: from pathogenesis to precision therapies.Frontiers in oncology · 2025Review
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- CT-based synthetic iodine map generation using conditional denoising diffusion probabilistic model.Medical physics · 2024Article
- CT-based synthetic contrast-enhanced dual-energy CT generation using conditional denoising diffusion probabilistic model.Physics in medicine and biology · 2024Article
- Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.Scientific reports · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Advances in automatic delineation of target volume and cardiac substructure in breast cancer radiotherapy (Review).Oncology letters · 2023Review
- Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media.Pharmaceutics · 2022Review
- Self-calibrated pixel-attention autoencoder: A strategy for contrast-enhanced spectral mammography image-to-image translation.Digital healthArticle
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12 authors at 1 institution in 1 country.
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Abstract
backgroundAdjuvant radiation therapy improves the overall survival and loco-regional control in patients with breast cancer. However, radiation-induced heart disease, which occurs after treatment from incidental radiation exposure to the cardiac organ, is an emerging challenge. This study aimed to generate synthetic contrast-enhanced computed tomography (SCECT) from non-contrast CT (NCT) using deep learning (DL) and investigate its role in contouring cardiac substructures. We also aimed to determine its applicability for a retrospective study on the substructure volume-dose relationship for predicting radiation-induced heart disease.
methodsWe prepared NCT-CECT cardiac scan pairs of 59 patients. Of these, 35, 4, and 20 pairs were used for training, validation, and testing, respectively. We adopted conditional generative adversarial network as a framework to generate SCECT. SCECT was validated in the following three stages: (1) The similarity between SCECT and CECT was evaluated; (2) Manual contouring was performed on SCECT and CECT with sufficient intervals and based on this, the geometric similarity of cardiac substructures was measured between them; (3) The treatment plan was quantitatively analyzed based on the contours of SCECT and CECT.
resultsWhile the mean values (± standard deviation) of the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure between SCECT and CECT were 20.66 ± 5.29, 21.57 ± 1.85, and 0.77 ± 0.06, those were 23.95 ± 6.98, 20.67 ± 2.34, and 0.76 ± 0.07 between NCT and CECT, respectively. The Dice similarity coefficients and mean surface distance between the contours of SCECT and CECT were 0.81 ± 0.06 and 2.44 ± 0.72, respectively. The dosimetry analysis displayed error rates of 0.13 ± 0.27 Gy and 0.71 ± 1.34% for the mean heart dose and V5Gy, respectively.
conclusionOur findings displayed the feasibility of SCECT generation from NCT and its potential for cardiac substructure delineation in patients who underwent breast radiation therapy.
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