Eun Cho1, Eun-Kyung Kim1, Mi Kyung Song2, Jung Hyun Yoon1. 1. Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea. 2. Department of Research Affairs, Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea.
Abstract
OBJECTIVES: To investigate the feasibility of a computer-aided diagnosis (CAD) system (S-Detect; Samsung Medison, Co, Ltd, Seoul, Korea) for breast ultrasonography (US), according to radiologists with various degrees of experience in breast imaging. METHODS: From December 2015 to March 2016, 119 breast masses in 116 women were included. Ultrasonographic images of the breast masses were retrospectively reviewed and analyzed by 2 radiologists specializing in breast imaging (7 and 1 years of experience, respectively) and S-Detect, according to the individual ultrasonographic descriptors from the fifth edition of the American College of Radiology Breast Imaging Reporting and Data System and final assessment categories. Diagnostic performance and the interobserver agreement among the radiologists and S-Detect was calculated and compared. RESULTS: Among the 119 breast masses, 54 (45.4%) were malignant, and 65 (54.6%) were benign. Compared to the radiologists, S-Detect had higher specificity (90.8% compared to 49.2% and 55.4%) and positive predictive value (PPV; 86.7% compared to 60.7% and 63.8%) (all P < .001). Both radiologists had significantly improved specificity, PPV, and accuracy when using S-Detect compared to US alone (all P < .001). The area under the receiving operating characteristic curves of the both radiologists did not show a significant improvement when applying S-Detect compared to US alone (all P > .05). Moderate agreement was seen in final assessments made by each radiologist and S-Detect (κ = 0.40 and 0.45, respectively). CONCLUSIONS: S-Detect is a clinically feasible diagnostic tool that can be used to improve the specificity, PPV, and accuracy of breast US, with a moderate degree of agreement in final assessments, regardless of the experience of the radiologist.
OBJECTIVES: To investigate the feasibility of a computer-aided diagnosis (CAD) system (S-Detect; Samsung Medison, Co, Ltd, Seoul, Korea) for breast ultrasonography (US), according to radiologists with various degrees of experience in breast imaging. METHODS: From December 2015 to March 2016, 119 breast masses in 116 women were included. Ultrasonographic images of the breast masses were retrospectively reviewed and analyzed by 2 radiologists specializing in breast imaging (7 and 1 years of experience, respectively) and S-Detect, according to the individual ultrasonographic descriptors from the fifth edition of the American College of Radiology Breast Imaging Reporting and Data System and final assessment categories. Diagnostic performance and the interobserver agreement among the radiologists and S-Detect was calculated and compared. RESULTS: Among the 119 breast masses, 54 (45.4%) were malignant, and 65 (54.6%) were benign. Compared to the radiologists, S-Detect had higher specificity (90.8% compared to 49.2% and 55.4%) and positive predictive value (PPV; 86.7% compared to 60.7% and 63.8%) (all P < .001). Both radiologists had significantly improved specificity, PPV, and accuracy when using S-Detect compared to US alone (all P < .001). The area under the receiving operating characteristic curves of the both radiologists did not show a significant improvement when applying S-Detect compared to US alone (all P > .05). Moderate agreement was seen in final assessments made by each radiologist and S-Detect (κ = 0.40 and 0.45, respectively). CONCLUSIONS: S-Detect is a clinically feasible diagnostic tool that can be used to improve the specificity, PPV, and accuracy of breast US, with a moderate degree of agreement in final assessments, regardless of the experience of the radiologist.
Authors: Qi Wei; Yu-Jing Yan; Ge-Ge Wu; Xi-Rong Ye; Fan Jiang; Jie Liu; Gang Wang; Yi Wang; Juan Song; Zhi-Ping Pan; Jin-Hua Hu; Chao-Ying Jin; Xiang Wang; Christoph F Dietrich; Xin-Wu Cui Journal: Eur Radiol Date: 2022-01-23 Impact factor: 5.315