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To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms.A commercial deep learning-based software for mammography was validated using external data collected from 200 patients, 100 each with and without breast cancer (40 with benign lesions and 60 without lesions) from one hospital. Ten readers, including five breast specialist radiologists (BSRs) and five general radiologists (GRs), assessed all mammography images using a seven-point scale to rate the likelihood of malignancy in two sessions, with and without the aid of the AI-based software, and the reading time was automatically recorded using a web-based reporting system. Two reading sessions were conducted with a two-month washout period in between. Differences in the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and reading time between reading with and without AI were analyzed, accounting for data clustering by readers when indicated.The AUROC of the AI alone, BSR (average across five readers), and GR (average across five readers) groups was 0.915 (95% confidence interval, 0.876-0.954), 0.813 (0.756-0.870), and 0.684 (0.616-0.752), respectively. With AI assistance, the AUROC significantly increased to 0.884 (0.840-0.928) and 0.833 (0.779-0.887) in the BSR and GR groups, respectively (AI-based software improved the performance of radiologists regardless of their experience and affected the reading time.

Objective To investigate the effects of different types of mammography equipment on screening outcomes by comparing the performance of film-screen mammography (FSM), computed radiography mammography (CRM), and digital mammography (DM). Materials and Methods We retrospectively enrolled 128756 sets of mammograms from 10 hospitals participating in the Alliance for Breast Cancer Screening in Korea between 2005 and 2010. We compared the diagnostic accuracy of the types of mammography equipment by analyzing the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI); performance indicators, including recall rate, cancer detection rate (CDR), positive predictive value1 (PPV1), sensitivity, specificity, and interval cancer rate (ICR); and the types of breast cancer pathology. Results The AUCs were 0.898 (95% CI, 0.878–0.919) in DM, 0.860 (0.815–0.905) in FSM, and 0.866 (0.828–0.903) in CRM (p = 0.150). DM showed better performance than FSM and CRM in terms of the recall rate (14.8 vs. 24.8 and 19.8%), CDR (3.4 vs. 2.2 and 2.1 per 1000 examinations), PPV1 (2.3 vs. 0.9 and 1.1%), and specificity (85.5 vs. 75.3 and 80.3%) (p < 0.001) but not in terms of sensitivity (86.3 vs. 87.4 and 86.3%) and ICR (0.6 vs. 0.4 and 0.4). The proportions of carcinoma in situ (CIS) were 27.5%, 13.6%, and 11.8% for DM, CRM, and FSM, respectively (p = 0.003). Conclusion In comparison to FSM and CRM, DM showed better performance in terms of the recall rate, CDR, PPV1, and specificity, although the AUCs were similar, and more CISs were detected using DM. The application of DM may help to improve the quality of mammography screenings. However, the overdiagnosis issue of CIS using DM should be evaluated.

Milk of calcium located in the breast is typically a benign entity. However, carcinoma may incidentally arise adjacent to or even within milk of calcium. Consequently, the characteristics of all observed calcific particles should be carefully analyzed. In this study, we report a case of carcinoma presented as malignant microcalcifications mixed within milk of calcium in a breast.

Objective To analyze participant factors that affect the diagnostic performance of screening mammography. Materials and Methods We enrolled 128756 cases from 10 hospitals between 2005 and 2010. We analyzed recall rate, cancer detection rate (CDR) per 1000 examinations, positive predictive value (PPV), sensitivity, specificity, false positive rate (FPR), and interval cancer rate (ICR) per 1000 negative examinations according to participant factors including age, breast density, and number of visit to the same institution, and adjusted for confounding variables. Results Increasing age improved recall rates (27.4% in 40's, 17.5% in 50's, 11.1% in 60's, and 8.6% in 70's), CDR (2.7, 3.2, 2.0, and 2.4), PPV (1.0, 1.8, 1.8, and 2.8%), sensitivity (81.3, 88.8, 90.3, and 94.7%), specificity (72.7, 82.7, 89.0, and 91.7%), and FPR (27.3, 17.3, 11.0, and 8.4%) (p < 0.05). Higher breast density impaired recall rates (4.0% in P1, 9.0% in P2, 28.9% in P3, and 27.8% in P4), PPV (3.3, 2.3, 1.2, and 1.3%), specificity (96.1, 91.2, 71.4, and 72.5%), and FPR (3.9, 8.9, 28.6, and 27.6%) (p < 0.001). It also increased CDR (1.3, 2.1, 3.3, and 3.6) and ICR (0.2, 0.3, 0.6, and 1.6) (p < 0.05). Successive visits to the same institution improved recall rates (20.9% for one visit, 10.7% for two visits, 7.7% for more than three visits), PPV (1.6, 2.8, and 2.7%), specificity (79.4, 89.6, and 92.5%), and FPR (20.6, 10.4, and 7.5%) (p < 0.001). Conclusion Young age and dense breasts negatively affected diagnostic performance in mammography screening, whereas successive visits to the same institution had a positive effect. Examinee education for successive visits to the same institution would improve the diagnostic performance.

Objective To analyze the diagnostic accuracy and trend in screening mammography in Korea. Materials and Methods We retrospectively linked the information from hospitals participating in the Alliance of Breast Cancer Screening in Korea (ABCS-K) and the database of the National Cancer Screening Program. We calculated performance indicators, including the recall rate, cancer detection rate (CDR), positive predictive value (PPV), sensitivity, specificity, false-positive rate (FPR), and interval cancer rate (ICR). Changes in the performance indicators were calculated as the annual percent change with 95% confidence interval (CI). Results We enrolled 128756 cases from 10 hospitals from 2005 to 2010. The recall rate was 19.1% with a downward trend over time (-12.1% per year; 95% CI, -15.9 to -8.2). The CDR was 2.69 per 1000 examinations, without a significant trend. The PPV was 1.4% with an upward trend (20.8% per year; 95% CI, 15.2 to 26.7). The sensitivity was 86.5% without a significant trend, whereas the specificity was 81.1% with an upward trend (3.3% per year; 95% CI, 2.1 to 4.5). The FPR was 18.9% with a downward trend (-12.4% per year; 95% CI, -16.2 to -8.4). The ICR was 0.5 per 1000 negative examinations without a significant trend. There were institutional variations in the diagnostic accuracy and trend except for the CDR, sensitivity, and ICR. Conclusion The sensitivity and CDR of screening mammography in the ABCS-K from 2005 to 2010 were compatible with those for Western women. The recall rate, PPV and specificity, however, were suboptimal, although they showed significant improvements over this period. A further analysis is required to explain institutional variations.