Cited 7 times in
Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study
DC Field | Value | Language |
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dc.contributor.author | 김진성 | - |
dc.contributor.author | 김호진 | - |
dc.contributor.author | 서영주 | - |
dc.contributor.author | 양고운 | - |
dc.contributor.author | 장지석 | - |
dc.contributor.author | 홍채선 | - |
dc.contributor.author | 정승연 | - |
dc.date.accessioned | 2022-08-23T00:13:02Z | - |
dc.date.available | 2022-08-23T00:13:02Z | - |
dc.date.issued | 2022-04 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/189316 | - |
dc.description.abstract | 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 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. Methods: We 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. Results: While 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. Conclusion: Our findings displayed the feasibility of SCECT generation from NCT and its potential for cardiac substructure delineation in patients who underwent breast radiation therapy. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | BioMed Central | - |
dc.relation.isPartOf | RADIATION ONCOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Breast Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Breast Neoplasms* / radiotherapy | - |
dc.subject.MESH | Feasibility Studies | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Heart Diseases* | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Neural Networks, Computer | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Tomography, X-Ray Computed / methods | - |
dc.title | Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiation Oncology (방사선종양학교실) | - |
dc.contributor.googleauthor | Jaehee Chun | - |
dc.contributor.googleauthor | Jee Suk Chang | - |
dc.contributor.googleauthor | Caleb Oh | - |
dc.contributor.googleauthor | InKyung Park | - |
dc.contributor.googleauthor | Min Seo Choi | - |
dc.contributor.googleauthor | Chae-Seon Hong | - |
dc.contributor.googleauthor | Hojin Kim | - |
dc.contributor.googleauthor | Gowoon Yang | - |
dc.contributor.googleauthor | Jin Young Moon | - |
dc.contributor.googleauthor | Seung Yeun Chung | - |
dc.contributor.googleauthor | Young Joo Suh | - |
dc.contributor.googleauthor | Jin Sung Kim | - |
dc.identifier.doi | 10.1186/s13014-022-02051-0 | - |
dc.contributor.localId | A04548 | - |
dc.contributor.localId | A05970 | - |
dc.contributor.localId | A01892 | - |
dc.contributor.localId | A06279 | - |
dc.contributor.localId | A04658 | - |
dc.contributor.localId | A05846 | - |
dc.relation.journalcode | J02591 | - |
dc.identifier.eissn | 1748-717X | - |
dc.identifier.pmid | 35459221 | - |
dc.subject.keyword | Breast cancer | - |
dc.subject.keyword | Contrast-enhanced computed tomography | - |
dc.subject.keyword | Deep learning | - |
dc.subject.keyword | Radiation therapy | - |
dc.subject.keyword | Radiation-induced heart disease | - |
dc.contributor.alternativeName | Kim, Jinsung | - |
dc.contributor.affiliatedAuthor | 김진성 | - |
dc.contributor.affiliatedAuthor | 김호진 | - |
dc.contributor.affiliatedAuthor | 서영주 | - |
dc.contributor.affiliatedAuthor | 양고운 | - |
dc.contributor.affiliatedAuthor | 장지석 | - |
dc.contributor.affiliatedAuthor | 홍채선 | - |
dc.citation.volume | 17 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 83 | - |
dc.identifier.bibliographicCitation | RADIATION ONCOLOGY, Vol.17(1) : 83, 2022-04 | - |
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