Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study

Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study

Background: Adjuvant 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...

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Bibliographic Details
Main Authors: Chang, J.S (Author), Choi, M.S (Author), Chun, J. (Author), Chung, S.Y (Author), Hong, C.-S (Author), Kim, H. (Author), Kim, J.S (Author), Moon, J.Y (Author), Oh, C. (Author), Park, I.K (Author), Suh, Y.J (Author), Yang, G. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
Breast cancer
Breast Neoplasms
breast tumor
Contrast-enhanced computed tomography
Deep learning
diagnostic imaging
Feasibility Studies
feasibility study
female
Female
heart disease
Heart Diseases
human
Humans
Neural Networks, Computer
procedures
Radiation therapy
Radiation-induced heart disease
Retrospective Studies
retrospective study
Tomography, X-Ray Computed
x-ray computed tomography
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