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AI analytics can be used as imaging biomarkers for predicting invasive upgrade of ductal carcinoma in situ

Authors
 Jiyoung Yoon  ;  Juyeon Yang  ;  Hye Sun Lee  ;  Min Jung Kim  ;  Vivian Youngjean Park  ;  Miribi Rho  ;  Jung Hyun Yoon 
Citation
 INSIGHTS INTO IMAGING, Vol.15(1) : 100, 2024-04 
Journal Title
INSIGHTS INTO IMAGING
Issue Date
2024-04
Keywords
Artificial intelligence ; Breast ; Carcinoma (Intraductal noninfiltrating) ; Image-guided biopsy ; Mammography
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.
Files in This Item:
T202405625.pdf Download
DOI
10.1186/s13244-024-01673-0
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
Yonsei Authors
Kim, Min Jung(김민정) ORCID logo https://orcid.org/0000-0003-4949-1237
Rho, Miribi(노미리비) ORCID logo https://orcid.org/0000-0002-1703-7657
Park, Vivian Youngjean(박영진) ORCID logo https://orcid.org/0000-0002-5135-4058
Yoon, Jung Hyun(윤정현) ORCID logo https://orcid.org/0000-0002-2100-3513
Yoon, Jiyoung(윤지영) ORCID logo https://orcid.org/0000-0003-2266-0803
Lee, Hye Sun(이혜선) ORCID logo https://orcid.org/0000-0001-6328-6948
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/200620
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