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AI-CAD for differentiating lesions presenting as calcifications only on mammography: outcome analysis incorporating the ACR BI-RADS descriptors for calcifications

DC Field Value Language
dc.contributor.author김민정-
dc.contributor.author김은경-
dc.contributor.author윤정현-
dc.contributor.author이혜선-
dc.contributor.author박영진-
dc.contributor.author윤지영-
dc.date.accessioned2022-12-22T04:41:19Z-
dc.date.available2022-12-22T04:41:19Z-
dc.date.issued2022-10-
dc.identifier.issn0938-7994-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/192191-
dc.description.abstractObjectives: To evaluate how AI-CAD triages calcifications and to compare its performance to an experienced breast radiologist. Methods: Among routine mammography performed between June 2016 and May 2018, 535 lesions detected as calcifications only on mammography in 500 women (mean age, 48.8 years) that were additionally interpreted with additional magnification views were included in this study. One dedicated breast radiologist retrospectively reviewed the magnification mammograms to assess morphology, distribution, and final assessment category according to ACR BI-RADS. AI-CAD analyzed routine mammograms providing AI-CAD marks and corresponding AI-CAD scores (ranging from 0 to 100%), for which values ≥ 10% were considered positive. Ground truth in terms of malignancy or benignity was confirmed with a histopathologic diagnosis or at least 1 year of imaging follow - up. Results: Of the 535 calcifications, 215 (40.2%) were malignant. Calcifications with positive AI-CAD scores showed significantly higher PPVs compared to calcifications with negative scores for all morphology (all p < 0.05). PPVs were significantly higher in calcifications with positive AI-CAD scores compared to those with negative scores for BI-RADS 3, 4a, or 4b assessments (all p < 0.05). AI-CAD and the experienced radiologist did not show significant difference in diagnostic performance; sensitivity 92.1% vs 95.4% (p = 0.125), specificity 71.9% vs 72.5% (p = 0.842), and accuracy 80.0% vs 81.7% (p = 0.413). Conclusion: Among calcifications with same morphology or BI-RADS assessment, those with positive AI-CAD scores had significantly higher PPVs. AI-CAD showed similar diagnostic performances to the experienced radiologist for calcifications detected on mammography. Key points: • Among calcifications with same morphology or BI-RADS assessment, those with positive AI-CAD scores had significantly higher PPVs. • AI-CAD showed similar diagnostic performance to an experienced radiologist in assessing lesions detected as calcifications only on mammography. • Among malignant calcifications, calcifications with positive AI-CAD scores showed higher rates of invasive cancers than calcifications with negative scores (all p > 0.05).-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringer International-
dc.relation.isPartOfEUROPEAN RADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHArtificial Intelligence-
dc.subject.MESHBreast Neoplasms* / diagnostic imaging-
dc.subject.MESHCalcinosis* / diagnostic imaging-
dc.subject.MESHCalcinosis* / pathology-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMammography-
dc.subject.MESHMiddle Aged-
dc.subject.MESHRetrospective Studies-
dc.titleAI-CAD for differentiating lesions presenting as calcifications only on mammography: outcome analysis incorporating the ACR BI-RADS descriptors for calcifications-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorJiyoung Yoon-
dc.contributor.googleauthorHye Sun Lee-
dc.contributor.googleauthorMin Jung Kim-
dc.contributor.googleauthorVivian Youngjean Park-
dc.contributor.googleauthorEun-Kyung Kim-
dc.contributor.googleauthorJung Hyun Yoon-
dc.identifier.doi10.1007/s00330-022-08961-7-
dc.contributor.localIdA00473-
dc.contributor.localIdA00801-
dc.contributor.localIdA02595-
dc.contributor.localIdA03312-
dc.contributor.localIdA01572-
dc.relation.journalcodeJ00851-
dc.identifier.eissn1432-1084-
dc.identifier.pmid35748900-
dc.identifier.urlhttps://link.springer.com/article/10.1007/s00330-022-08961-7-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordBreast neoplasms-
dc.subject.keywordMammography-
dc.subject.keywordRadiologists-
dc.subject.keywordRetrospective studies-
dc.contributor.alternativeNameKim, Min Jung-
dc.contributor.affiliatedAuthor김민정-
dc.contributor.affiliatedAuthor김은경-
dc.contributor.affiliatedAuthor윤정현-
dc.contributor.affiliatedAuthor이혜선-
dc.contributor.affiliatedAuthor박영진-
dc.citation.volume32-
dc.citation.number10-
dc.citation.startPage6565-
dc.citation.endPage6574-
dc.identifier.bibliographicCitationEUROPEAN RADIOLOGY, Vol.32(10) : 6565-6574, 2022-10-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers

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