Purpose: Considering the high disease burden and unique features of Asian patients with breast cancer (BC), it is essential to have a comprehensive view of genetic characteristics in this population. An institutional targeted sequencing platform was developed through the Korea Research-Driven Hospitals project and was incorporated into clinical practice. This study explores the use of targeted next-generation sequencing (NGS) and its outcomes in patients with advanced/metastatic BC in the real world. Materials and Methods: We reviewed the results of NGS tests administered to BC patients using a customized sequencing platform—FiRST Cancer Panel (FCP)—over 7 years. We systematically described clinical translation of FCP for precise diagnostics, personalized therapeutic strategies, and unraveling disease pathogenesis. Results: NGS tests were conducted on 548 samples from 522 patients with BC. Ninety-seven point six percentage of tested samples harbored at least one pathogenic alteration. The common alterations included mutations in TP53 (56.2%), PIK3CA (31.2%), GATA3 (13.8%), BRCA2 (10.2%), and amplifications of CCND1 (10.8%), FGF19 (10.0%), and ERBB2 (9.5%). NGS analysis of ERBB2 amplification correlated well with human epidermal growth factor receptor 2 immunohistochemistry and in situ hybridization. RNA panel analyses found potentially actionable and prognostic fusion genes. FCP effectively screened for potentially germline pathogenic/likely pathogenic mutation. Ten point three percent of BC patients received matched therapy guided by NGS, resulting in a significant overall survival advantage (p=0.022), especially for metastatic BCs. Conclusion Clinical NGS provided multifaceted benefits, deepening our understanding of the disease, improving diagnostic precision, and paving the way for targeted therapies. The concrete advantages of FCP highlight the importance of multi-gene testing for BC, especially for metastatic conditions.

Purpose: Considering the high disease burden and unique features of Asian patients with breast cancer (BC), it is essential to have a comprehensive view of genetic characteristics in this population. An institutional targeted sequencing platform was developed through the Korea Research-Driven Hospitals project and was incorporated into clinical practice. This study explores the use of targeted next-generation sequencing (NGS) and its outcomes in patients with advanced/metastatic BC in the real world. Materials and Methods: We reviewed the results of NGS tests administered to BC patients using a customized sequencing platform—FiRST Cancer Panel (FCP)—over 7 years. We systematically described clinical translation of FCP for precise diagnostics, personalized therapeutic strategies, and unraveling disease pathogenesis. Results: NGS tests were conducted on 548 samples from 522 patients with BC. Ninety-seven point six percentage of tested samples harbored at least one pathogenic alteration. The common alterations included mutations in TP53 (56.2%), PIK3CA (31.2%), GATA3 (13.8%), BRCA2 (10.2%), and amplifications of CCND1 (10.8%), FGF19 (10.0%), and ERBB2 (9.5%). NGS analysis of ERBB2 amplification correlated well with human epidermal growth factor receptor 2 immunohistochemistry and in situ hybridization. RNA panel analyses found potentially actionable and prognostic fusion genes. FCP effectively screened for potentially germline pathogenic/likely pathogenic mutation. Ten point three percent of BC patients received matched therapy guided by NGS, resulting in a significant overall survival advantage (p=0.022), especially for metastatic BCs. Conclusion Clinical NGS provided multifaceted benefits, deepening our understanding of the disease, improving diagnostic precision, and paving the way for targeted therapies. The concrete advantages of FCP highlight the importance of multi-gene testing for BC, especially for metastatic conditions.

Purpose: Considering the high disease burden and unique features of Asian patients with breast cancer (BC), it is essential to have a comprehensive view of genetic characteristics in this population. An institutional targeted sequencing platform was developed through the Korea Research-Driven Hospitals project and was incorporated into clinical practice. This study explores the use of targeted next-generation sequencing (NGS) and its outcomes in patients with advanced/metastatic BC in the real world. Materials and Methods: We reviewed the results of NGS tests administered to BC patients using a customized sequencing platform—FiRST Cancer Panel (FCP)—over 7 years. We systematically described clinical translation of FCP for precise diagnostics, personalized therapeutic strategies, and unraveling disease pathogenesis. Results: NGS tests were conducted on 548 samples from 522 patients with BC. Ninety-seven point six percentage of tested samples harbored at least one pathogenic alteration. The common alterations included mutations in TP53 (56.2%), PIK3CA (31.2%), GATA3 (13.8%), BRCA2 (10.2%), and amplifications of CCND1 (10.8%), FGF19 (10.0%), and ERBB2 (9.5%). NGS analysis of ERBB2 amplification correlated well with human epidermal growth factor receptor 2 immunohistochemistry and in situ hybridization. RNA panel analyses found potentially actionable and prognostic fusion genes. FCP effectively screened for potentially germline pathogenic/likely pathogenic mutation. Ten point three percent of BC patients received matched therapy guided by NGS, resulting in a significant overall survival advantage (p=0.022), especially for metastatic BCs. Conclusion Clinical NGS provided multifaceted benefits, deepening our understanding of the disease, improving diagnostic precision, and paving the way for targeted therapies. The concrete advantages of FCP highlight the importance of multi-gene testing for BC, especially for metastatic conditions.

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Purpose: This study aimed to investigate the incidence, risk factors, and clinical course of nonalcoholic fatty liver disease (NAFLD) following pancreaticoduodenectomy, focusing on the role of adjuvant chemotherapy and other metabolic changes. Methods: A retrospective analysis was conducted on 189 patients who underwent pancreaticoduodenectomy between 2013 and 2016. NAFLD was diagnosed using computed tomography (CT) imaging, defined as a liver-tospleen attenuation ratio <0.9. Sarcopenia and sarcopenic obesity were assessed using preoperative CT scans. Logistic regression analysis was performed to identify risk factors for NAFLD development. Results: The cumulative incidence of NAFLD increased over time, with rates of 15.9% at one year, 20.4% at three years, and 35.2% at five years post-pancreaticoduodenectomy. Adjuvant chemotherapy was identified as the only significant independent predictor of NAFLD development (odds ratio, 2.74; 95% confidence interval, 1.16-6.70; P=0.023). No significant associations were found between NAFLD and pancreatic enzyme replacement therapy (PERT), sarcopenia, or sarcopenic obesity. Serial analysis of NAFLD status in long-term survivors revealed dynamic changes, with some patients experiencing spontaneous remission or recurrence. Conclusion NAFLD is a common, progressive complication following pancreaticoduodenectomy, particularly in patients receiving adjuvant chemotherapy. Although no significant associations with PERT or sarcopenia were observed, these areas warrant further investigation. Long-term monitoring and targeted management strategies are recommended to address NAFLD in this population. Future prospective studies are needed to elucidate the natural history and contributing factors of NAFLD after pancreaticoduodenectomy.

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Purpose: To estimate the number of practicing nurses required to resolve staffing differences between capital and non-capital regions and analyze the relationship between regional differences in staffing and salary. Methods: Using public data on population, patients, newly licensed nurses, practicing nurses, and annual salaries, regional differences were analyzed in newly licensed nurses per population, practicing nurses per population, practicing nurses per patient (i.e., staffing level), and salary. The number of additionally required practicing nurses was estimated by multiplying staffing differences by the number of patients in the lower-staffed region. Results: During 2002~2022, 71,107 and 243,611 newly licensed nurses were supplied, while the number of practicing nurses increased by 91,886 and 88,070 in the capital and non-capital regions, respectively. The non-capital region had more practicing nurses per population, whereas the capital region had more practicing nurses per patient. In 2020, 31,330 practicing nurses were additionally required in the non-capital region. Salaries were higher in the capital region, and regional salary differences increased during 2011~2020. Regional salary differences were associated with regional staffing differences and the number of additionally required practicing nurses. Conclusion Government and health insurance policies are required to encourage hospitals in the non-capital region to improve staffing and salaries.

Purpose: This study aimed to examine changes in nurse staffing grades and nursing fee revenues following the third amendment of the resource-based relative value scale, implemented in January 2024. Methods: Revised nurse staffing grades were determined based on the number of patients per nurse (PpN), calculated by dividing the daily patient census by the number of registered nurses working in general wards. Changes in staffing grades were analyzed from the fourth quarter of 2023 to the first quarter of 2024 among 44 tertiary hospitals, 328 general hospitals, and 1,378 non-general hospitals. Results: In 2024, the previous "best grade" (grade 1) was subdivided into two or three grades. The best grade was redefined as grade S (PpN<1.5) in tertiary and general hospitals and grade A (PpN<2.0) in non-general hospitals. By 2024, 72.4%, 11.8%, and 22.5% of tertiary, general, and non-general hospitals, respectively, achieved the best grade. The estimated additional annual nursing fee revenues per nurse in 2024 (compared to 2023) for hospitals advancing from grade 1 to grade S ranged from 1,088,455 to 11,412,655 KRW in tertiary hospitals and 11,483,834 KRW in general hospitals. Conclusion To ensure appropriate nurse staffing levels, nursing fees should be proportionally differentiated based on staffing requirements. Additional revenues should be strategically allocated to enhance nurse compensation, thereby improving workforce sustainability and care quality.

Purpose: To estimate the number of practicing nurses required to resolve staffing differences between capital and non-capital regions and analyze the relationship between regional differences in staffing and salary. Methods: Using public data on population, patients, newly licensed nurses, practicing nurses, and annual salaries, regional differences were analyzed in newly licensed nurses per population, practicing nurses per population, practicing nurses per patient (i.e., staffing level), and salary. The number of additionally required practicing nurses was estimated by multiplying staffing differences by the number of patients in the lower-staffed region. Results: During 2002~2022, 71,107 and 243,611 newly licensed nurses were supplied, while the number of practicing nurses increased by 91,886 and 88,070 in the capital and non-capital regions, respectively. The non-capital region had more practicing nurses per population, whereas the capital region had more practicing nurses per patient. In 2020, 31,330 practicing nurses were additionally required in the non-capital region. Salaries were higher in the capital region, and regional salary differences increased during 2011~2020. Regional salary differences were associated with regional staffing differences and the number of additionally required practicing nurses. Conclusion Government and health insurance policies are required to encourage hospitals in the non-capital region to improve staffing and salaries.

Purpose: This study aimed to examine changes in nurse staffing grades and nursing fee revenues following the third amendment of the resource-based relative value scale, implemented in January 2024. Methods: Revised nurse staffing grades were determined based on the number of patients per nurse (PpN), calculated by dividing the daily patient census by the number of registered nurses working in general wards. Changes in staffing grades were analyzed from the fourth quarter of 2023 to the first quarter of 2024 among 44 tertiary hospitals, 328 general hospitals, and 1,378 non-general hospitals. Results: In 2024, the previous "best grade" (grade 1) was subdivided into two or three grades. The best grade was redefined as grade S (PpN<1.5) in tertiary and general hospitals and grade A (PpN<2.0) in non-general hospitals. By 2024, 72.4%, 11.8%, and 22.5% of tertiary, general, and non-general hospitals, respectively, achieved the best grade. The estimated additional annual nursing fee revenues per nurse in 2024 (compared to 2023) for hospitals advancing from grade 1 to grade S ranged from 1,088,455 to 11,412,655 KRW in tertiary hospitals and 11,483,834 KRW in general hospitals. Conclusion To ensure appropriate nurse staffing levels, nursing fees should be proportionally differentiated based on staffing requirements. Additional revenues should be strategically allocated to enhance nurse compensation, thereby improving workforce sustainability and care quality.