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AI analytics can be used as imaging biomarkers for predicting invasive upgrade of ductal carcinoma in situ
DC Field | Value | Language |
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dc.contributor.author | 김민정 | - |
dc.contributor.author | 노미리비 | - |
dc.contributor.author | 박영진 | - |
dc.contributor.author | 윤정현 | - |
dc.contributor.author | 윤지영 | - |
dc.contributor.author | 이혜선 | - |
dc.date.accessioned | 2024-12-06T01:51:12Z | - |
dc.date.available | 2024-12-06T01:51:12Z | - |
dc.date.issued | 2024-04 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/200620 | - |
dc.description.abstract | Objectives To evaluate whether the quantitative abnormality scores provided by artificial intelligence (AI)-based computer-aided detection/diagnosis (CAD) for mammography interpretation can be used to predict invasive upgrade in ductal carcinoma in situ (DCIS) diagnosed on percutaneous biopsy. Methods Four hundred forty DCIS in 420 women (mean age, 52.8 years) diagnosed via percutaneous biopsy from January 2015 to December 2019 were included. Mammographic characteristics were assessed based on imaging features (mammographically occult, mass/asymmetry/distortion, calcifications only, and combined mass/asymmetry/distortion with calcifications) and BI-RADS assessments. Routine pre-biopsy 4-view digital mammograms were analyzed using AI-CAD to obtain abnormality scores (AI-CAD score, ranging 0-100%). Multivariable logistic regression was performed to identify independent predictive mammographic variables after adjusting for clinicopathological variables. A subgroup analysis was performed with mammographically detected DCIS. Results Of the 440 DCIS, 117 (26.6%) were upgraded to invasive cancer. Three hundred forty-one (77.5%) DCIS were detected on mammography. The multivariable analysis showed that combined features (odds ratio (OR): 2.225, p = 0.033), BI-RADS 4c or 5 assessments (OR: 2.473, p = 0.023 and OR: 5.190, p < 0.001, respectively), higher AI-CAD score (OR: 1.009, p = 0.007), AI-CAD score >= 50% (OR: 1.960, p = 0.017), and AI-CAD score >= 75% (OR: 2.306, p = 0.009) were independent predictors of invasive upgrade. In mammographically detected DCIS, combined features (OR: 2.194, p = 0.035), and higher AI-CAD score (OR: 1.008, p = 0.047) were significant predictors of invasive upgrade. Conclusion The AI-CAD score was an independent predictor of invasive upgrade for DCIS. Higher AI-CAD scores, especially in the highest quartile of >= 75%, can be used as an objective imaging biomarker to predict invasive upgrade in DCIS diagnosed with percutaneous biopsy. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Springer | - |
dc.relation.isPartOf | INSIGHTS INTO IMAGING | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | AI analytics can be used as imaging biomarkers for predicting invasive upgrade of ductal carcinoma in situ | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Jiyoung Yoon | - |
dc.contributor.googleauthor | Juyeon Yang | - |
dc.contributor.googleauthor | Hye Sun Lee | - |
dc.contributor.googleauthor | Min Jung Kim | - |
dc.contributor.googleauthor | Vivian Youngjean Park | - |
dc.contributor.googleauthor | Miribi Rho | - |
dc.contributor.googleauthor | Jung Hyun Yoon | - |
dc.identifier.doi | 10.1186/s13244-024-01673-0 | - |
dc.contributor.localId | A00473 | - |
dc.contributor.localId | A05327 | - |
dc.contributor.localId | A01572 | - |
dc.contributor.localId | A02595 | - |
dc.contributor.localId | A05730 | - |
dc.contributor.localId | A03312 | - |
dc.relation.journalcode | J04644 | - |
dc.identifier.eissn | 1869-4101 | - |
dc.identifier.pmid | 38578585 | - |
dc.subject.keyword | Artificial intelligence | - |
dc.subject.keyword | Breast | - |
dc.subject.keyword | Carcinoma (Intraductal noninfiltrating) | - |
dc.subject.keyword | Image-guided biopsy | - |
dc.subject.keyword | Mammography | - |
dc.contributor.alternativeName | Kim, Min Jung | - |
dc.contributor.affiliatedAuthor | 김민정 | - |
dc.contributor.affiliatedAuthor | 노미리비 | - |
dc.contributor.affiliatedAuthor | 박영진 | - |
dc.contributor.affiliatedAuthor | 윤정현 | - |
dc.contributor.affiliatedAuthor | 윤지영 | - |
dc.contributor.affiliatedAuthor | 이혜선 | - |
dc.citation.volume | 15 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 100 | - |
dc.identifier.bibliographicCitation | INSIGHTS INTO IMAGING, Vol.15(1) : 100, 2024-04 | - |
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