Cited 1 times in
Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김은경 | - |
dc.contributor.author | 이시은 | - |
dc.date.accessioned | 2024-04-18T08:13:07Z | - |
dc.date.available | 2024-04-18T08:13:07Z | - |
dc.date.issued | 2024-04 | - |
dc.identifier.issn | 1229-6929 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/198870 | - |
dc.description.abstract | Objective: Artificial intelligence-based computer-aided diagnosis (AI -CAD) is increasingly used in mammography. While the continuous scores of AI -CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI -CAD system and analyzed them in relation to clinical and radiological findings. Materials and Methods: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 +/- 11.5 years, including 0.6% [4/599] high -risk women) who underwent mammography and received positive AI -CAD results (Lunit Insight MMG, abnormality score >= 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI -CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. Results: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI -CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). Conclusion: PPV increased with increasing AI -CAD abnormality scores. The PPVs of AI -CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Korean Society of Radiology | - |
dc.relation.isPartOf | KOREAN JOURNAL OF RADIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Artificial Intelligence | - |
dc.subject.MESH | Breast Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Breast Neoplasms* / pathology | - |
dc.subject.MESH | Computers | - |
dc.subject.MESH | Early Detection of Cancer | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Mammography* / methods | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Predictive Value of Tests | - |
dc.subject.MESH | Radiographic Image Interpretation, Computer-Assisted / methods | - |
dc.subject.MESH | Retrospective Studies | - |
dc.title | Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Si Eun Lee | - |
dc.contributor.googleauthor | Hanpyo Hong | - |
dc.contributor.googleauthor | Eun-Kyung Kim | - |
dc.identifier.doi | 10.3348/kjr.2023.0907 | - |
dc.contributor.localId | A00801 | - |
dc.contributor.localId | A05611 | - |
dc.relation.journalcode | J02884 | - |
dc.identifier.eissn | 2005-8330 | - |
dc.identifier.pmid | 38528692 | - |
dc.subject.keyword | Artificial intelligence | - |
dc.subject.keyword | Breast neoplasms | - |
dc.subject.keyword | Computer-aided diagnosis | - |
dc.subject.keyword | Digital mammography | - |
dc.contributor.alternativeName | Kim, Eun Kyung | - |
dc.contributor.affiliatedAuthor | 김은경 | - |
dc.contributor.affiliatedAuthor | 이시은 | - |
dc.citation.volume | 25 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 343 | - |
dc.citation.endPage | 350 | - |
dc.identifier.bibliographicCitation | KOREAN JOURNAL OF RADIOLOGY, Vol.25(4) : 343-350, 2024-04 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.