Screening ultrasound (US) can increase the detection of breast cancer. However, little is known about the clinicopathologic characteristics of breast cancers detected by screening US. A search of the database for patients with breast cancer yielded a dataset in 6837 women who underwent breast surgery at Seoul National University Hospital (Korea). Of 6837 women, 1047 were asymptomatic and had a non-palpable cancer. Two hundred fifty-four women with 256 cancers detected by US (US-detected cancer) and 793 women with 807 cancers detected by mammography (MG-detected cancer) were identified. The imaging, clinicopathologic, and molecular data were reviewed. Univariate and multivariate analyses were carried out. Women with US-detected cancer were younger and were more likely to undergo breast-conserving surgery and to have node-negative invasive cancer (P < 0.0001). By multivariate analysis, the significant independent characteristics were tumor size, mammographic density, final assessment category according to the American College of Radiology Breast Imaging Reporting and Data System, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and molecular subtype. Compared to tumors that were >2 cm in size, tumors that were ≤1 cm in size were 2.2-fold more likely to be US-detected cancers (P = 0.02). Compared to the luminal A subtype tumors (estrogen receptor [ER]+, PR+, HER2−), luminal B subtype tumors (ER+, PR+, HER2 + ) were less likely to be in the US-detected cancer group (P < 0.01). Women with dense breasts were more likely to have US-detected cancer (P < 0.01) versus those with non-dense breasts. Screening US-detected cancers were less likely to be diagnosed as category 5 instead of category 4 (P < 0.01). In conclusion, women with US-detected breast cancer are more likely to have small-sized invasive cancer and more likely associated with the luminal A subtype. (Cancer Sci 2011; 102: 1862–1867)

To determine survival outcomes in women with breast cancer detected at combined screening with breast MRI and mammography versus screening mammography alone. This is an institutional review board-approved retrospective study, and the need for informed consent was waived. A total of 3002 women with an increased risk of breast cancer were screened between 2001 and 2004. Of the 3002 women, 1534 (51.1%) had 2780 combined screenings (MRI and mammography) and 1468 (48.9%) had 4811 mammography-only screenings. The Χ² test and the Kaplan-Meier method were used to compare cancer detection rates and survival rates. The overall cancer detection rate was significantly higher in the MRI plus mammography group compared with the mammography-only group (1.4% [40 of 2780] vs 0.5% [23 of 4811]; P < 0.001). No interval cancers occurred in the MRI plus mammography group, whereas 9 interval cancers were found in the mammography-only group. During a median follow-up of 10.9 years (range: 0.7 to 15.2), a total of 11 recurrences and 5 deaths occurred. Of the 11 recurrences, 6 were in the MRI plus mammography group and 5 were in the mammography-only group. All five deaths occurred in the mammography-only group. Disease-free survival showed no statistically significant difference between the two groups (P = 0.32). However, overall survival was significantly improved in the MRI plus mammography group (P = 0.002). Combined screening with MRI and mammography in women at elevated risk of breast cancer improves cancer detection and overall survival.

The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women.