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Mammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection

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dc.contributor.author김가람-
dc.contributor.author김은경-
dc.contributor.author윤정현-
dc.date.accessioned2022-03-11T06:17:55Z-
dc.date.available2022-03-11T06:17:55Z-
dc.date.issued2022-01-
dc.identifier.issn0361-803X-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/188024-
dc.description.abstractBACKGROUND. Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosyn-thesis (DBT) and artificial intelligence-based computer-aided detection (AI-CAD) after breast-conserving therapy (BCT) has not been widely investigated. OBJECTIVE. The purpose of our study was to assess the impact of additional DBT or AI-CAD on recall rate and diagnostic performance in women undergoing mammographic surveillance after BCT. METHODS. This retrospective study included 314 women (mean age, 53.3 ± 10.6 [SD] years; four with bilateral breast cancer) who underwent BCT followed by DBT (mean interval from surgery to DBT, 15.2 ± 15.4 months). Three breast radiologists independently reviewed images in three sessions: digital mammography (DM), DM with DBT (DM plus DBT), and DM with AI-CAD (DM plus AI-CAD). Recall rates and diagnostic performance were compared between DM, DM plus DBT, and DM plus AI-CAD using the readers' mean results. RESULTS. Of the 314 women, six breast recurrences (three ipsilateral and three contralateral) had developed at the time of surveillance mammography. The ipsilateral breast recall rate was lower for DM plus AI-CAD (1.9%) than for DM (11.2%) or DM plus DBT (4.1%) (p < .001). The contralateral breast recall rate was significantly lower for DM plus AI-CAD (1.5%, p < .001) than for DM (6.6%) but for not DM plus DBT (2.7%, p = .08). In the ipsilateral breast, accuracy was higher for DM plus AI-CAD (97.0%) than for DM (88.5%) or DM plus DBT (94.8%) (p < .05); specificity was higher for DM plus AI-CAD (98.3%) than for DM (89.3%) or DM plus DBT (96.1%) (p < .05); sensitivity was significantly lower for DM plus AI-CAD (22.2%) than for DM (66.7%, p = .03) but not DM plus DBT (22.2%, p > .99). In the contralateral breast, accuracy was significantly higher for DM plus AI-CAD (97.1%) than for DM (92.5%, p < .001) but not DM plus DBT (96.1%, p = .25); specificity was significantly higher for DM plus AI-CAD (98.6%) than for DM (93.7%, p < .001) but not DM plus DBT (97.5%) (p = .09); sensitivity was not different between DM (33.3%), DM plus DBT (22.2%), and DM plus AI-CAD (11.1%) (p > .05). CONCLUSION. After BCT, adjunct DBT or AI-CAD reduced recall rates and improved accuracy in the ipsilateral and contralateral breasts compared with DM. In the ipsilateral breast, the addition of AI-CAD resulted in a lower recall rate and higher accuracy than the addition of DBT. CLINICAL IMPACT. AI-CAD may help address the challenges of interpreting post-BCT surveillance mammograms.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringfield, Ill., Thomas-
dc.relation.isPartOfAMERICAN JOURNAL OF ROENTGENOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAdult-
dc.subject.MESHAged-
dc.subject.MESHAged, 80 and over-
dc.subject.MESHArtificial Intelligence*-
dc.subject.MESHBreast / diagnostic imaging-
dc.subject.MESHBreast / surgery-
dc.subject.MESHBreast Neoplasms / diagnostic imaging*-
dc.subject.MESHBreast Neoplasms / surgery-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMammography / methods*-
dc.subject.MESHMastectomy, Segmental*-
dc.subject.MESHMiddle Aged-
dc.subject.MESHNeoplasm Recurrence, Local / diagnostic imaging*-
dc.subject.MESHRadiographic Image Interpretation, Computer-Assisted / methods*-
dc.subject.MESHRetrospective Studies-
dc.subject.MESHYoung Adult-
dc.titleMammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorJung Hyun Yoon-
dc.contributor.googleauthorEun-Kyung Kim-
dc.contributor.googleauthorGa Ram Kim-
dc.contributor.googleauthorKyunghwa Han-
dc.contributor.googleauthorHee Jung Moon-
dc.identifier.doi10.2214/AJR.21.26506-
dc.contributor.localIdA00284-
dc.contributor.localIdA00801-
dc.contributor.localIdA02595-
dc.relation.journalcodeJ00116-
dc.identifier.eissn1546-3141-
dc.identifier.pmid34378399-
dc.identifier.urlhttps://www.ajronline.org/doi/10.2214/AJR.21.26506-
dc.subject.keywordartificial intelligence-
dc.subject.keywordbreast-
dc.subject.keyworddigital breast tomosynthesis-
dc.subject.keywordmammography-
dc.subject.keywordsurveillance-
dc.contributor.alternativeNameKim, Ga Ram-
dc.contributor.affiliatedAuthor김가람-
dc.contributor.affiliatedAuthor김은경-
dc.contributor.affiliatedAuthor윤정현-
dc.citation.volume218-
dc.citation.number1-
dc.citation.startPage42-
dc.citation.endPage51-
dc.identifier.bibliographicCitationAMERICAN JOURNAL OF ROENTGENOLOGY, Vol.218(1) : 42-51, 2022-01-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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