Jeongmin Lee1, Sanghee Kim2, Bong Joo Kang3, Sung Hun Kim4, Ga Eun Park5. 1. Department of Radiology Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea. jmlee328@gmail.com. 2. Department of Radiology Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea. 3. Department of Radiology Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea. lionmain@catholic.ac.kr. 4. Department of Radiology Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea. rad-ksh@catholic.ac.kr. 5. Department of Radiology Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea. hoonhoony@naver.com.
Abstract
AIM: To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions. MATERIALS AND METHODS: Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD. RESULTS: Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD. CONCLUSION: By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.
AIM: To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions. MATERIALS AND METHODS: Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD. RESULTS: Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD. CONCLUSION: By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.
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