Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Background: s/Aims: Hepatocellular carcinoma (HCC) is the sixth most common cancer and second leading cause of cancer-related deaths in South Korea. This study evaluated the characteristics of Korean patients newly diagnosed with HCC in 2016-2018. Methods: Data from the Korean Primary Liver Cancer Registry (KPLCR), a representative database of patients newly diagnosed with HCC in South Korea, were analyzed. This study investigated 4,462 patients with HCC registered in the KPLCR in 2016-2018. Results: The median patient age was 63 years (interquartile range, 55-72). 79.7% of patients were male. Hepatitis B infection was the most common underlying liver disease (54.5%). The Barcelona Clinic Liver Cancer (BCLC) staging system classified patients as follows: stage 0 (14.9%), A (28.8%), B (7.5%), C (39.0%), and D (9.8%). The median overall survival was 3.72 years (95% confidence interval, 3.47-4.14), with 1-, 3-, and 5-year overall survival rates of 71.3%, 54.1%, and 44.3%, respectively. In 2016-2018, there was a significant shift toward BCLC stage 0-A and Child-Turcotte-Pugh liver function class A (P<0.05), although survival rates did not differ by diagnosis year. In the treatment group (n=4,389), the most common initial treatments were transarterial therapy (31.7%), surgical resection (24.9%), best supportive care (18.9%), and local ablation therapy (10.5%). Conclusions Between 2016 and 2018, HCC tended to be diagnosed at earlier stages, with better liver function in later years. However, since approximately half of the patients remained diagnosed at an advanced stage, more rigorous and optimized HCC screening strategies should be implemented.

Purpose: This multicenter, open-label, phase II trial evaluated the efficacy and safety of bortezomib combined with dexamethasone for the treatment of relapsed/refractory cutaneous T-cell lymphoma (CTCL) in previously treated patients across 14 institutions in South Korea. Materials and Methods: Between September 2017 and July 2020, 29 patients with histologically confirmed CTCL received treatment, consisting of eight 4-week cycles of induction therapy followed by maintenance therapy, contingent upon response, for up to one year. The primary endpoint was the proportion of patients achieving an objective global response. Results: Thirteen of the 29 patients (44.8%) achieved an objective global response, including two complete responses. The median progression-free survival (PFS) was 5.8 months, with responders showing a median PFS of 14.0 months. Treatment-emergent adverse events were generally mild, with a low incidence of peripheral neuropathy and hematologic toxicities. Despite the trend toward shorter PFS in patients with higher mutation burdens, genomic profiling before and after treatment showed no significant emergence of new mutations indicative of disease progression. Conclusion This study supports the use of bortezomib and dexamethasone as a viable and safe treatment option for previously treated CTCL, demonstrating substantial efficacy and manageability in adverse effects. Further research with a larger cohort is suggested to validate these findings and explore the prognostic value of mutation profiles.

Purpose: In Korea, the Act on Hospice and Palliative Care and Decisions on Life-Sustaining Treatment (LST) was implemented on February 4, 2018. We aimed to investigate relevant factors and clinical changes associated with LST decisions after law enforcement. Materials and Methods: This single-center retrospective study included patients who completed LST documents using legal forms at Asan Medical Center from February 5, 2018, to June 30, 2020. Results: 5,896 patients completed LST documents, of which 2,704 (45.8%) signed the documents in person, while family members of 3,192 (54%) wrote the documents on behalf of the patients. Comparing first year and following year of implementation of the act, the self-documentation rate increased (43.9% to 47.2%, p=0.014). Moreover, the number of LST decisions made during or after intensive care unit admission decreased (37.8% vs. 35.2%, p=0.045), and the completion rate of LST documents during chemotherapy increased (6.6% vs. 8.9%, p=0.001). In multivariate analysis, age < 65 (odds ratio [OR], 1.724; 95% confidence interval [CI], 1.538 to 1.933; p < 0.001), unmarried status (OR, 1.309; 95% CI, 1.097 to 1.561; p=0.003), palliative care consultation (OR, 1.538; 95% CI, 1.340 to 1.765; p < 0.001), malignancy (OR, 1.864; 95% CI, 1.628 to 2.133; p < 0.001), and changes in timing on the first year versus following year (OR, 1.124; 95% CI, 1.003 to 1.260; p=0.045) were related to a higher self-documentation rate. Conclusion Age < 65 years, unmarried status, malignancy, and referral to a palliative care team were associated with patients making LST decisions themselves. Furthermore, the subject and timing of LST decisions have changed with the LST act.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits significant sex disparities in incidence, yet its molecular mechanisms remain unclear. We explored the role of telomerase reverse transcriptase (TERT) genetic alterations and hepatitis B virus (HBV) integration, both known major contributors to HCC, in sex-specific risk for HBV-related HCC. Methods: We examined 310 HBV-related HCC tissues to investigate sex-specific TERT promoter (TERT-pro) mutations and HBV integration profiles, stratified by sex and age, and validated with single-cell RNA sequencing (scRNA-seq) data. Results: Tumors predominantly exhibited TERT-pro mutations (26.0% vs. 0%) and HBV-TERT integration (37.0% vs. 3.0%) compared to non-tumorous tissues. While TERT-pro mutations increased with age in both sexes, younger males (≤60 years) showed marked predominance compared to younger females. Males had significantly more HBV integrations at younger ages, while females initially had fewer integrations that gradually increased with age. Younger males' integrations showed significantly greater enrichment in the TERT locus compared to younger females, alongside a preference for promoters, PreS/S regions, and CpG islands. Overall, TERT genetic alterations were significantly sex-differential in younger individuals (75.3% in males vs. 23.1% in females) but not in older individuals (76.9% vs. 83.3%, respectively). These alterations were associated with increased TERT expression. The skewed TERT abnormalities in younger males were further corroborated by independent scRNA-seq data. Conclusions Our findings highlight the critical role of TERT alterations and HBV integration patterns in the male predominance of HCC incidence among younger HBV carriers, offering insights for future exploration to optimize sex-specific patient care and HCC surveillance strategies.

A comprehensive list of occupational diseases among farmers is crucial for both compensation and prevention efforts. In Korea, most farmers are self-employed, and some occupational diseases are compensated through farmer safety insurance. However, it is not harmonized with industrial accident compensation insurance and does not adequately reflect the true burden of occupational diseases among farmers. To address this gap, the authors compiled a list of occupational diseases tailored to Korean farmers by reviewing the International Labor Organization’s list of occupational diseases, the Korean Industrial Accident Compensation Insurance List, the occupational disease lists of other countries, and relevant literature on farmers’ work-related diseases.

Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence.In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.