Ewha Medical Journal最新文献

{"title":"Non-operative management of locally advanced rectal cancer with an emphasis on outcomes and quality of life: a narrative review.","authors":"In Ja Park","doi":"10.12771/emj.2025.00703","DOIUrl":"10.12771/emj.2025.00703","url":null,"abstract":"<p><p>Non‑operative management, particularly the watch and wait (WW) strategy, has emerged as an alternative to total mesorectal excision for selected patients with locally advanced rectal cancer who achieve a clinical complete response (cCR) after neoadjuvant treatment. This narrative review examines oncologic outcomes, functional and quality‑of‑life benefits, diagnostic challenges, and surveillance requirements associated with WW compared to radical surgery. Evidence from randomized trials and international registries indicates that WW provides overall and disease-free survival rates comparable to those of surgery, provided that stringent selection criteria and intensive surveillance are maintained for 3 to 5 years. Local regrowth occurs in 15%-40% of patients-most commonly within 24 months-but salvage surgery is curative in over 90% of cases and restores oncologic equivalence. Nevertheless, distant metastasis is more frequent in patients who experience regrowth, underscoring the importance of early detection and the need for optimized systemic therapy. Accurate determination of cCR remains the primary limitation; digital rectal examination, high‑resolution magnetic resonance imaging, and endoscopy, even when combined, cannot reliably exclude microscopic residual disease. Total neoadjuvant therapy increases cCR rates to 30%-60% and expands the pool of WW candidates, but also intensifies the need for standardized response definitions and surveillance algorithms. WW offers organ preservation and quality‑of‑life improvements without compromising survival in carefully selected patients, provided that multidisciplinary teams ensure rigorous response assessment and lifelong monitoring. Future advances in imaging, molecular biomarkers, and individualized risk stratification are expected to further enhance the safety of WW and expand eligibility to a broader patient population.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 3","pages":"e40"},"PeriodicalIF":0.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144754706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A history of 20 years of medical education at Ewha Womans University College of Medicine.","authors":"Ivo Kwon, Somi Jeong, Seung-Jung Kim, Ara Ko, Hyeonji Jeon","doi":"10.12771/emj.2025.00479","DOIUrl":"10.12771/emj.2025.00479","url":null,"abstract":"<p><p>The study aims to examine the 20-year developmental trajectory of medical education at Ewha Womans University College of Medicine (2004-2025). It analyzes educational support documents, self-evaluation reports, and Curriculum Committee meeting minutes to illuminate both the direction and significance of Ewha's medical education reforms. Key milestones include the formal establishment of the Medical Education Office in 2004 and the subsequent founding of the Department of Medical Education in 2005. Major innovations over this period encompass the expansion of objective structured clinical examinations and the introduction of problem-based learning modules. Additional advancements include the establishment of the Ewha Medical Simulation Center and Learning Resource Center, as well as the reversion to an undergraduate medical college format in 2015. The college has also prioritized faculty development workshops and medical education seminars, implemented the Ewha Social Active Communication program, and introduced team-based learning. Noteworthy initiatives include the enhancement of student research capacity and the launch of a dedicated medical education newsletter. In 2022, the Medical Education Office was reorganized as the Ewha Center for Medical Education, marking a new era of integrated leadership and expanded educational initiatives. Ewha has consistently achieved high accreditation statuses, reflecting ongoing excellence in curriculum development, assessment, and faculty development. This progress demonstrates the dedication and collaboration of both faculty and staff, resulting in a robust educational framework. The institution's continuous growth serves not only as a testament to past achievements but also as a foundation for future advancements in Ewha's medical education, with the ultimate aim of cultivating women leaders in Korean healthcare.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 3","pages":"e41"},"PeriodicalIF":0.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144754689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning for automated cause-of-death classification from 2021 to 2022 in Korea: development and validation of an ICD-10 prediction model.","authors":"Seokmin Lee, Gyeongmin Im","doi":"10.12771/emj.2025.00675","DOIUrl":"10.12771/emj.2025.00675","url":null,"abstract":"<p><strong>Purpose: </strong>This study evaluated the feasibility and performance of a deep learning approach utilizing the Korean Medical BERT (KM-BERT) model for the automated classification of underlying causes of death within national mortality statistics. It aimed to assess predictive accuracy throughout the cause-of-death coding workflow and to identify limitations and opportunities for further artificial intelligence (AI) integration.</p><p><strong>Methods: </strong>We performed a retrospective prediction study using 693,587 death certificates issued in Korea between January 2021 and December 2022. Free-text fields for immediate, antecedent, and contributory causes were concatenated and fine-tuned with KM-BERT. Three classification models were developed: (1) final underlying cause prediction (International Classification of Diseases, 10th Revision [ICD-10] code) from certificate inputs, (2) tentative underlying cause selection based on ICD-10 Volume 2 rules, and (3) classification of individual cause-of-death entries. Models were trained and validated using 2021 data (80% training, 20% validation) and evaluated on 2022 data. Performance metrics included overall accuracy, weighted F1 score, and macro F1 score.</p><p><strong>Results: </strong>On 306,898 certificates from 2022, the final cause model achieved 62.65% accuracy (F1-weighted, 0.5940; F1-macro, 0.1503). The tentative cause model demonstrated 95.35% accuracy (F1-weighted, 0.9516; F1-macro, 0.4996). The individual entry model yielded 79.51% accuracy (F1-weighted, 0.7741; F1-macro, 0.9250). Error analysis indicated reduced reliability for rare diseases and for specific ICD chapters, which require supplementary administrative data.</p><p><strong>Conclusion: </strong>Despite strong performance in mapping free-text inputs and selecting tentative underlying causes, there remains a need for improved data quality, administrative record integration, and model refinement. A systematic, long-term approach is essential for the broad adoption of AI in mortality statistics.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 3","pages":"e45"},"PeriodicalIF":0.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144754705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of pulmonary tuberculosis on lung cancer screening: a narrative review.","authors":"Jeong Uk Lim","doi":"10.12771/emj.2025.00052","DOIUrl":"10.12771/emj.2025.00052","url":null,"abstract":"<p><p>Lung cancer remains a leading cause of cancer-related mortality worldwide. Low-dose computed tomography (LDCT) screening has demonstrated efficacy in reducing lung cancer mortality by enabling early detection. In several countries, including Korea, LDCT-based screening for high-risk populations has been incorporated into national healthcare policies. However, in regions with a high tuberculosis (TB) burden, the effectiveness of LDCT screening for lung cancer may be influenced by TB-related pulmonary changes. Studies indicate that the screen-positive rate in TB-endemic areas differs from that in low-TB prevalence regions. A critical challenge is the differentiation between lung cancer lesions and TB-related abnormalities, which can contribute to false-positive findings and increase the likelihood of unnecessary invasive procedures. Additionally, structural lung damage from prior TB infections can alter LDCT interpretation, potentially reducing diagnostic accuracy. Nontuberculous mycobacterial infections further complicate this issue, as their radiologic features frequently overlap with those of TB and lung cancer, necessitating additional microbiologic confirmation. Future research incorporating artificial intelligence and biomarkers may enhance diagnostic precision and facilitate a more personalized approach to lung cancer screening in TB-endemic settings.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e23"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathophysiology, clinical manifestation, and treatment of tuberculosis-associated chronic obstructive pulmonary disease: a narrative review.","authors":"Joon Young Choi","doi":"10.12771/emj.2025.00059","DOIUrl":"10.12771/emj.2025.00059","url":null,"abstract":"<p><p>Chronic obstructive pulmonary disease (COPD) is a leading cause of respiratory morbidity and mortality, most often linked to smoking. However, growing evidence indicates that previous tuberculosis (TB) infection is also a critical risk factor for COPD. This review aimed at providing a comprehensive perspective on TB-COPD, covering its epidemiologic significance, pathogenesis, clinical characteristics, and current management approaches. Tuberculosis-associated chronic obstructive pulmonary disease (TB-COPD) is characterized by persistent inflammatory responses, altered immune pathways, and extensive structural lung damage-manifested as cavitation, fibrosis, and airway remodeling. Multiple epidemiologic studies have shown that individuals with a history of TB have a significantly higher likelihood of developing COPD and experiencing worse outcomes, such as increased breathlessness and frequent exacerbations. Key pathogenic mechanisms include elevated matrix metalloproteinase activity and excessive neutrophil-driven inflammation, which lead to alveolar destruction, fibrotic scarring, and the development of bronchiectasis. Treatment generally follows current COPD guidelines, advocating the use of long-acting bronchodilators and the selective application of inhaled corticosteroids. Studies have demonstrated that indacaterol significantly improves lung function and respiratory symptoms, while long-acting muscarinic antagonists have shown survival benefits.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e24"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of automatic organ segmentation based on positron-emission tomography analysis system using Swin UNETR in breast cancer patients in Korea.","authors":"Dong Hyeok Choi, Joonil Hwang, Hai-Jeon Yoon, So Hyun Ahn","doi":"10.12771/emj.2025.00094","DOIUrl":"10.12771/emj.2025.00094","url":null,"abstract":"<p><strong>Purpose: </strong>The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region-of-interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning-based quantitative analysis method that enhances diagnostic and prognostic accuracy.</p><p><strong>Methods: </strong>We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software.</p><p><strong>Results: </strong>In a dataset of 10 patients, our method achieved an auto-segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single-ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole-organ SUV analysis.</p><p><strong>Conclusion: </strong>This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning-based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e30"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Principles of Best Practice and Transparency in Scholarly Publishing ver. 4: a Korean translation.","authors":"","doi":"10.12771/emj.2025.00318","DOIUrl":"https://doi.org/10.12771/emj.2025.00318","url":null,"abstract":"","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e37"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ewha Medical Journal passed the scientific evaluation by PubMed Central and succeeded in being included in DOAJ, but failed to be accepted by Scopus.","authors":"Sun Huh","doi":"10.12771/emj.2025.00024","DOIUrl":"10.12771/emj.2025.00024","url":null,"abstract":"","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e21"},"PeriodicalIF":0.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of <i>Ewha Medical Journal</i>.","authors":"Hae-Sun Chung","doi":"10.12771/emj.2025.00395","DOIUrl":"10.12771/emj.2025.00395","url":null,"abstract":"","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e16"},"PeriodicalIF":0.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reflections on 25 hours a day at Ewha Womans University College of Medicine from August 2021 to January 2025: a dean's farewell message.","authors":"Eunhee Ha","doi":"10.12771/emj.2025.00045","DOIUrl":"10.12771/emj.2025.00045","url":null,"abstract":"","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e20"},"PeriodicalIF":0.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}