Cited 4 times in
Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness
DC Field | Value | Language |
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dc.contributor.author | 서영주 | - |
dc.date.accessioned | 2023-05-31T05:39:01Z | - |
dc.date.available | 2023-05-31T05:39:01Z | - |
dc.date.issued | 2023-03 | - |
dc.identifier.issn | 0938-7994 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/194239 | - |
dc.description.abstract | Objectives To evaluate commercial deep learning-based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). Methods This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. Results CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, -244.0, 283.9] vs. 45.26 [-248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score >= 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). Conclusions Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Springer International | - |
dc.relation.isPartOf | EUROPEAN RADIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Calcium* | - |
dc.subject.MESH | Coronary Angiography / methods | - |
dc.subject.MESH | Coronary Artery Disease* / diagnosis | - |
dc.subject.MESH | Coronary Vessels | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Reproducibility of Results | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Software | - |
dc.subject.MESH | Tomography, X-Ray Computed / methods | - |
dc.title | Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Hyun Woo Kang1 | - |
dc.contributor.googleauthor | Woo Jin Ahn | - |
dc.contributor.googleauthor | Ju Hyun Jeong | - |
dc.contributor.googleauthor | Young Joo Suh | - |
dc.contributor.googleauthor | Dong Hyun Yang | - |
dc.contributor.googleauthor | Hangseok Choi | - |
dc.contributor.googleauthor | Sung Ho Hwang | - |
dc.contributor.googleauthor | Hwan Seok Yong | - |
dc.contributor.googleauthor | Yu-Whan Oh | - |
dc.contributor.googleauthor | Eun-Young Kang | - |
dc.contributor.googleauthor | Cherry Kim | - |
dc.identifier.doi | 10.1007/s00330-022-09143-1 | - |
dc.contributor.localId | A01892 | - |
dc.relation.journalcode | J00851 | - |
dc.identifier.eissn | 1432-1084 | - |
dc.identifier.pmid | 36152039 | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s00330-022-09143-1 | - |
dc.subject.keyword | Artificial intelligence | - |
dc.subject.keyword | Calcium | - |
dc.subject.keyword | Coronary arteries | - |
dc.subject.keyword | Software | - |
dc.subject.keyword | Tomography, X-ray computed | - |
dc.contributor.alternativeName | Suh, Young Joo | - |
dc.contributor.affiliatedAuthor | 서영주 | - |
dc.citation.volume | 33 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1973 | - |
dc.citation.endPage | 1981 | - |
dc.identifier.bibliographicCitation | EUROPEAN RADIOLOGY, Vol.33(3) : 1973-1981, 2023-03 | - |
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