Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists

• Published in: Radiology. Artificial intelligence Vol. 6; no. 2; p. e230137
• Main Authors: Kim, Jiye G, Haslam, Bryan, Diab, Abdul Rahman, Sakhare, Ashwin, Grisot, Giorgia, Lee, Hyunkwang, Holt, Jacqueline, Lee, Christoph I, Lotter, William, Sorensen, A Gregory
• Format: Journal Article
• Language: English
• Published: United States Radiological Society of North America 01-03-2024
• Subjects: Artificial Intelligence; Breast Neoplasms - diagnostic imaging; Early Detection of Cancer - methods; Female; Humans; Male; Mammography - methods; Middle Aged; Original Research; Radiologists; Retrospective Studies; Screening; Artificial Intelligence; Computer-aided Diagnosis; Screening Mammography; Breast Cancer; Digital Breast Tomosynthesis; Convolutional Neural Network (CNN)
• ISSN: 2638-6100; 2638-6100
• DOI: 10.1148/ryai.230137

Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08;
< .001). Improvement in AUC was observed for both general radiologists (difference of 0.08;
< .001) and breast imaging specialists (difference of 0.04;
< .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics.
Computer-aided Diagnosis, Screening Mammography, Digital Breast Tomosynthesis, Breast Cancer, Screening, Convolutional Neural Network (CNN), Artificial Intelligence
. © RSNA, 2024.