Objective: Automated breast ultrasound (ABUS) is a relevant imaging technique for early breast cancer diagnosis and is increasingly being used as a supplementary tool for mammography. This study compared the performance of ABUS and handheld ultrasound (HHUS) in detecting and characterizing the axillary lymph nodes (LNs) in patients with breast cancer. Materials and Methods: We retrospectively reviewed the medical records of women with recently diagnosed early breast cancer (≤ T2) who underwent both ABUS and HHUS examinations for axilla (September 2017–May 2018). ABUS and HHUS findings were compared using pathological outcomes as reference standards. Diagnostic performance in predicting any axillary LN metastasis and heavy nodal-burden metastases (i.e., ≥ 3 LNs) was evaluated. The ABUS-HHUS agreement for visibility and US findings was calculated. Results: The study included 377 women (53.1 ± 11.1 years). Among 385 breast cancers in 377 patients, 101 had axillary LN metastases and 30 had heavy nodal burden metastases. ABUS identified benign-looking or suspicious axillary LNs (average, 1.4 ± 0.8) in 246 axillae (63.9%, 246/385). According to the per-breast analysis, the sensitivity, specificity, positive and negative predictive values, and accuracy of ABUS in predicting axillary LN metastases were 43.6% (44/101), 95.1% (270/284), 75.9% (44/58), 82.6% (270/327), and 81.6% (314/385), respectively. The corresponding results for HHUS were 41.6% (42/101), 95.1% (270/284), 75.0% (42/56), 82.1% (270/329), and 81.0% (312/385), respectively, which were not significantly different from those of ABUS (P ≥ 0.53). The performance results for heavy nodal-burden metastases were 70.0% (21/30), 89.6% (318/355), 36.2% (21/58), 97.3% (318/327), and 88.1% (339/385), respectively, for ABUS and 66.7% (20/30), 89.9% (319/355), 35.7% (20/56), 97.0% (319/329), and 88.1% (339/385), respectively, for HHUS, also not showing significant difference (P ≥ 0.57). The ABUS–HHUS agreement was 95.9% (236/246; Cohen’s kappa = 0.883). Conclusion Although ABUS showed limited sensitivity in diagnosing axillary LN metastasis in early breast cancer, it was still useful as the performance was comparable to that of HHUS.

Objective: To investigate whether reader training improves the performance and agreement of radiologists in interpreting unenhanced breast magnetic resonance imaging (MRI) scans using diffusion-weighted imaging (DWI). Materials and Methods: A study of 96 breasts (35 cancers, 24 benign, and 37 negative) in 48 asymptomatic women was performed between June 2019 and October 2020. High-resolution DWI with b-values of 0, 800, and 1200 sec/mm 2 was performed using a 3.0-T system. Sixteen breast radiologists independently reviewed the DWI, apparent diffusion coefficient maps, and T1-weighted MRI scans and recorded the Breast Imaging Reporting and Data System (BI-RADS) category for each breast. After a 2-h training session and a 5-month washout period, they re-evaluated the BI-RADS categories. A BI-RADS category of 4 (lesions with at least two suspicious criteria) or 5 (more than two suspicious criteria) was considered positive.The per-breast diagnostic performance of each reader was compared between the first and second reviews. Inter-reader agreement was evaluated using a multi-rater κ analysis and intraclass correlation coefficient (ICC). Results: Before training, the mean sensitivity, specificity, and accuracy of the 16 readers were 70.7% (95% confidence interval [CI]: 59.4–79.9), 90.8% (95% CI: 85.6–94.2), and 83.5% (95% CI: 78.6–87.4), respectively. After training, significant improvements in specificity (95.2%; 95% CI: 90.8–97.5; P = 0.001) and accuracy (85.9%; 95% CI: 80.9–89.8; P = 0.01) were observed, but no difference in sensitivity (69.8%; 95% CI: 58.1–79.4; P = 0.58) was observed. Regarding inter-reader agreement, the κ values were 0.57 (95% CI: 0.52–0.63) before training and 0.68 (95% CI: 0.62–0.74) after training, with a difference of 0.11 (95% CI: 0.02–0.18; P = 0.01). The ICC was 0.73 (95% CI: 0.69–0.74) before training and 0.79 (95% CI: 0.76–0.80) after training (P = 0.002). Conclusion Brief reader training improved the performance and agreement of interpretations by breast radiologists using unenhanced MRI with DWI.

Objective: To prospectively investigate the influence of the menstrual cycle on the background parenchymal signal (BPS) and apparent diffusion coefficient (ADC) of the breast on diffusion-weighted MRI (DW-MRI) in healthy premenopausal women. Materials and Methods: Seven healthy premenopausal women (median age, 37 years; range, 33–49 years) with regular menstrual cycles participated in this study. DW-MRI was performed during each of the four phases of the menstrual cycle (four examinations in total). Three radiologists independently assessed the BPS visual grade on images with b-values of 800 sec/mm2 (b800), 1200 sec/mm2 (b1200), and a synthetic 1500 sec/mm2 (sb1500). Additionally, one radiologist conducted a quantitative analysis to measure the BPS volume (%) and ADC values of the BPS (ADCBPS) and fibroglandular tissue (ADCFGT). Changes in the visual grade, BPS volume (%), ADCBPS, and ADCFGT during the menstrual cycle were descriptively analyzed. Results: The visual grade of BPS in seven women varied from mild to marked on b800 and from minimal to moderate on b1200 and sb1500. As the b-value increased, the visual grade of BPS decreased. On b800 and sb1500, two of the seven volunteers showed the highest visual grade in the early follicular phase (EFP). On b1200, three of the seven volunteers showed the highest visual grades in EFP. The BPS volume (%) on b800 and b1200 showed the highest value in three of the six volunteers with dense breasts in EFP. Three of the seven volunteers showed the lowest ADCBPS in the EFP. Four of the seven volunteers showed the highest ADCBPS in the early luteal phase (ELP) and the lowest ADCFGT in the late follicular phase (LFP). Conclusion Most volunteers did not exhibit specific BPS patterns during their menstrual cycles. However, the highest BPS and lowest ADCBPS were more frequently observed in EFP than in the other menstrual cycle phases, whereas the highest ADCBPS was more common in ELP. The lowest ADCFGT was more frequent in LFP.

Purpose: Robotic single-site plus one-port myomectomy (RSOM) was designed to reduce the number of incision sites for greater cosmetic satisfaction of patients while retaining the benefits of conventional robotic multi-site myomectomy (CRM). Robotic single-site plus two-port myomectomy (RSTM) eliminated one port relative to conventional CRM, and RSOM achieved the same advantage with respect to RSTM. This study aimed to compare RSOM with RSTM in terms of their respective methodologies and surgical outcomes. Materials and Methods: The medical records of 230 patients who had undergone RSOM and 146 patients who had undergone RSTM were reviewed. The groups’ surgical outcomes were compared using propensity score matching (PSM) analysis. Results: In the total data, RSOM had a shorter operative time (135.1±57.4 min vs. 149.9±46.2 min, p=0.009) and a shorter hospital stay (5.2±0.5 days vs. 5.4±0.7 days, p=0.033) relative to RSTM. The PSM analysis showed that there were no statistically significant intergroup differences in the patients’ baseline characteristics. Regarding the surgical outcomes, the RSOM group showed shorter operative time (129.2±49.3 min vs. 148.7±46.3 min, p=0.001) compared to the RSTM group. Conclusion Compared with RSTM, RSOM was associated with shorter operative time. Additionally, more detailed comparative and prospective studies are needed to evaluate RSOM relative to RSTM.

Purpose: Several predictive models have been developed to predict the pathological complete response (pCR) after neoadjuvant chemotherapy (NAC); however, few are broadly applicable owing to radiologic complexity and institution-specific clinical variables, and none have been externally validated. This study aimed to develop and externally validate a machine learning model that predicts pCR after NAC in patients with breast cancer using routinely collected clinical and demographic variables. Methods: The electronic medical records of patients with advanced breast cancer who underwent NAC before surgical resection between January 2017 and December 2020 were reviewed. Patient data from Seoul National University Bundang Hospital were divided into training and internal validation cohorts. Five machine learning techniques, including gradient boosting machine (GBM), support vector machine, random forest, decision tree, and neural network, were used to build predictive models, and the area under the receiver operating characteristic curve (AUC) was compared to select the best model. Finally, the model was validated using an independent cohort from Seoul National University Hospital. Results: A total of 1,003 patients were included in the study: 287, 71, and 645 in the training, internal validation, and external validation cohorts, respectively. Overall, 36.3% of the patients achieved pCR. Among the five machine learning models, the GBM showed the highest AUC for pCR prediction (AUC, 0.903; 95% confidence interval [CI], 0.833–0.972).External validation confirmed an AUC of 0.833 (95% CI, 0.800–0.865). Conclusion Commonly available clinical and demographic variables were used to develop a machine learning model for predicting pCR following NAC. External validation of the model demonstrated good discrimination power, indicating that routinely collected variables were sufficient to build a good prediction model.

Purpose: Estrogen receptor (ER) expression in breast cancer plays an essential role in carcinogenesis and disease progression. Recently, tumors with low level (1%-10%) of ER expression have been separately defined as ER low positive (ERlow). It is suggested that ERlow tumors might be morphologically and behaviorally different from tumors with high ER expression (ERhigh). Materials and Methods: Retrospective analysis of a prospective cohort database was performed. Patients who underwent curative surgery for early breast cancer and had available medical records were included for analysis. Difference in clinicopathological characteristics, endocrine responsiveness and five-year recurrence-free survival was evaluated between different ER subgroups (ERhigh, ERlow, and ER-negative [ER–]). Results: A total of 2,162 breast cancer patients were included in the analysis, Tis and T1 stage. Among them, 1,654 (76.5%) were ERhigh, 54 (2.5%) were ERlow, and 454 (21.0%) were ER- patients. ERlow cases were associated with smaller size, higher histologic grade, positive human epidermal growth factor receptor 2, negative progesterone receptor, and higher Ki-67 expression. Recurrence rate was highest in ER– tumors and was inversely proportional to ER expression. Recurrence-free survival was not affected by hormonal therapy in the ERlow group (p=0.418). Conclusion ERlow breast cancer showed distinct clinicopathological features. ERlow tumors seemed to have higher recurrence rates compared to ERhigh tumors, and they showed no significant benefit from hormonal therapy. Future large scale prospective studies are necessary to validate the treatment options for ERlow breast cancer.

2019-nCoV Omicron (B.1.1.529) variant, which has brought new challenges to the prevention and control of COVID-19 pandemic, has the characteristics of stronger transmissibility and more rapid transmission and more significant immune evasion. It took only two months to become a predominant strain worldwide after its identification in South Africa in November 2021. Local epidemics caused by Omicron variant have been reported in several provinces in China. However, the epidemiological characteristics of highly mutated Omicron variant remain unclear. This article summarizes the progress in the research of functional mutations, transmissibility, virulence, immune evasion and cross-reactive immune responses of Omicron variant, to provide references for the effective prevention and control of COVID-19 pandemic caused by Omicron variant.
COVID-19 ; Humans ; Mutation ; Pandemics ; SARS-CoV-2

Background: Although biological links are unclear, low bone density and atherosclerosis are inversely associated. This study evaluated the association between bone mineral density (BMD) and coronary computed tomographic angiography (CCTA) findings, including coronary artery calcification (CAC) score and the presence, extent, and composition of coronary atherosclerotic plaque (CAP) in asymptomatic women. Methods: A symptomatic women aged ≥40 years (N=2, 100; median age, 52 years; range, 40-80 years) were selected from a retrospective observational cohort and stratified into normal, osteopenia, and osteoporosis groups according to BMD T-score grades. We evaluated CAC score and assessed the presence, extent, and stenosis severity of CAP on CCTA. Additionally, CAP was categorized as calcified, mixed, or non-calcified according to calcified component valiums (>130 Hounsfield units). Results: Osteopenia and osteoporosis were found in 28.8% and 5.3% of participants, respectively. CAC score and CAC severity significantly increased with decreased BMD grades (from normal to osteoporosis). The presence of CAP (overall, 15.6%; normal, 12.6%; osteopenia, 20.2%; osteoporosis, 28.8%; P<0.001) and number of segments with CAP significantly increased with decreased BMD grades. Furthermore, the number of segments with calcified or mixed plaques, excluding non-calcified plaques, increased with decreased BMD grades. Although most associations were attenuated or disappeared after adjusting for age and other covariates, calcified plaques showed a strong and age-independent association with BMD grades. Conclusions The presence and severity of CAC and CAP were significantly associated with BMD severity in asymptomatic women, particularly for the presence of calcified plaques. Further studies are required to determine the association between vascular calcification and bone health status.

Objective: The optimal imaging approach for evaluating pathological nipple discharge remains unclear. We investigated the value of adding ductography to ultrasound (US) for evaluating pathologic nipple discharge in patients with negative mammography findings. Materials and Methods: From July 2003 to December 2018, 101 women (mean age, 46.3 ± 12.2 years; range, 23–75 years) with pathologic nipple discharge were evaluated using pre-ductography (initial) US, ductography, and post-ductography US. The imaging findings were reviewed retrospectively. The standard reference was surgery (70 patients) or > 2 years of followup with US (31 patients). The diagnostic performances of initial US, ductography, and post-ductography US for detecting malignancy were compared using the McNemar’s test or a generalized estimating equation. Results: In total, 47 papillomas, 30 other benign lesions, seven high-risk lesions, and 17 malignant lesions were identified as underlying causes of pathologic nipple discharge. Only eight of the 17 malignancies were detected on the initial US, while the remaining nine malignancies were detected by ductography. Among the nine malignancies detected by ductography, eight were detected on post-ductography US and could be localized for US-guided intervention. The sensitivities of ductography (94.1% [16/17]) and post-ductography US (94.1% [16/17]) were significantly higher than those of initial US (47.1% [8/17]; p = 0.027 and 0.013, respectively). The negative predictive value of post-ductography US (96.9% [31/32]) was significantly higher than that of the initial US (83.3% [45/54]; p = 0.006). Specificity was significantly higher for initial US than for ductography and post-ductography US (p = 0.001 for all). Conclusion The combined use of ductography and US has a high sensitivity for detecting malignancy in patients with pathologic nipple discharge and negative mammography. Ductography findings enable lesion localization on second-look post-ductography US, thus facilitating the selection of optimal treatment plans.