The sixth edition of the Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) was published in 2025. This review compared KMAP-DD 2025 with four major international clinical practice guidelines: Canadian Network for Mood and Anxiety Treatments Clinical Guidelines for the Management of Major Depressive Disorders, National Institute for Health and Care Excellence Depression Guideline, Royal Australian and New Zealand College of Psychiatrists Clinical Practice Guidelines for Mood Disorders, and British Association for Psychopharmacology Guideline. While KMAP-DD is based on expert consensus, and others on evidence-based methods, overall treatment strategies for depressive episodes were fairly consistent. Especially, KMAP-DD 2025 offers more structured recommendations in areas lacking strong evidence, such as premenstrual dysphoric disorder, perinatal depression, and depression with medical comorbidities. KMAP-DD 2025 also reflected Korean clinical practice patterns emphasizing rapid symptom relief and early use of combination strategies. Despite limitations as a consensus-based guideline, KMAP-DD 2025 complements evidence-based approaches and provides practical, situation-specific guidance for real-world clinical decision-making in Korea.

Secondary rhabdoid tumors (RTs) with atypical teratoid/rhabdoid tumor-like features rarely arise from, or coexist with, pleomorphic xanthoastrocytomas (PXAs), and their clinicopathological and molecular characteristics remain poorly understood. We report a 17-year-old girl with a temporal lobe mass that, upon gross total resection, pathologically contained both RT and PXA components. Immunohistochemistry revealed loss of INI1 expression restricted to the RT component, while the PXA area retained INI1. Next-generation sequencing identified a shared BRAF::TRIM24 fusion and homozygous deletion of CDKN2A/2B in both components, indicating a shared clonal origin. Additionally, a germline ATM frameshift mutation (c.5288_5289insGA) was identified in both tumor components, making the first such report in central nervous system tumors. SMARCB1 loss was confined to the RT component, further supporting the hypotheses of clonal evolution and secondary transformation. Despite gross total resection, craniospinal irradiation, and chemotherapy, the patient developed rapid leptomeningeal dissemination and died 5 months after surgery. This case provides clinicopathological and molecular evidence for clonal evolution and secondary transformation of PXA into an RT. The presence of germline ATM mutation may have therapeutic and biological relevance. Further studies are required to clarify the pathogenesis and optimal management of these rare and aggressive tumors.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background: Small cell lung cancer (SCLC) is called ‘smoker’s disease’ because it is strongly associated with smoking and most cases occur in smokers. However, it can also occur in never smokers. We investigated the clinical features of never smokers with SCLC and compared their treatment outcomes with those of smokers with SCLC. Methods: We retrospectively reviewed the clinical data of patients who had proven SCLC and had received chemotherapy at a single cancer center between July 2002 and April 2021. Results: Of 1,643 patients, 1,416 (86.2%) were enrolled in this study. A total of 162 (11.4%) and 1,254 (88.6%) patients were never smokers and smokers, respectively. There were more female never smokers than smokers (n=130; 80.2% vs. 79, 6.3%, p=0.000), and the incidence of ischemic heart disease was lower among never smokers than among smokers (4/1,416, [2.5%] vs. 83/1,416 [6.6%], p=0.036). Never smokers showed less symptoms at diagnosis than smokers (80.9% vs. 87.2%, p=0.037); however, they showed more toxicity after first-line treatment (61.7% vs. 47.8%, p=0.001). The objective response rate (ORR) was significantly higher in never smokers (74.1% vs. 59.6%, p=0.000). In the multivariate analysis, never smoking and second-line treatment were associated with a better ORR. However, progression-free survival and overall survival were not significantly different between never smokers and smokers. Conclusion In conclusion, never smokers accounted for 11.4% of patients with SCLC. They had distinguishing clinical characteristics and showed better chemotherapeutic responses than smokers.

Background: Small cell lung cancer (SCLC) is called ‘smoker’s disease’ because it is strongly associated with smoking and most cases occur in smokers. However, it can also occur in never smokers. We investigated the clinical features of never smokers with SCLC and compared their treatment outcomes with those of smokers with SCLC. Methods: We retrospectively reviewed the clinical data of patients who had proven SCLC and had received chemotherapy at a single cancer center between July 2002 and April 2021. Results: Of 1,643 patients, 1,416 (86.2%) were enrolled in this study. A total of 162 (11.4%) and 1,254 (88.6%) patients were never smokers and smokers, respectively. There were more female never smokers than smokers (n=130; 80.2% vs. 79, 6.3%, p=0.000), and the incidence of ischemic heart disease was lower among never smokers than among smokers (4/1,416, [2.5%] vs. 83/1,416 [6.6%], p=0.036). Never smokers showed less symptoms at diagnosis than smokers (80.9% vs. 87.2%, p=0.037); however, they showed more toxicity after first-line treatment (61.7% vs. 47.8%, p=0.001). The objective response rate (ORR) was significantly higher in never smokers (74.1% vs. 59.6%, p=0.000). In the multivariate analysis, never smoking and second-line treatment were associated with a better ORR. However, progression-free survival and overall survival were not significantly different between never smokers and smokers. Conclusion In conclusion, never smokers accounted for 11.4% of patients with SCLC. They had distinguishing clinical characteristics and showed better chemotherapeutic responses than smokers.

Purpose: This study aimed to develop a comprehensive practical guide for enteral nutrition (EN) designed to enhance patient safety and reduce complications in Korea. Under the leadership of the Korean Society for Parenteral and Enteral Nutrition (KSPEN), the initiative sought to standardize EN procedures, improve decision-making, and promote effective multidisciplinary communication. Methods: The KSPEN EN committee identified key questions related to EN practices and organized them into seven sections such as prescribing, delivery route selection, formula preparation, administration, and quality management. Twenty-one experts, selected based on their expertise, conducted a thorough literature review to formulate evidence-based recommendations. Drafts underwent peer review both within and across disciplines, with final revisions completed by the KSPEN Guideline Committee. The guide, which will be published in three installments, addresses critical elements of EN therapy and safety protocols. Results: The practical guide recommends that EN orders include detailed elements and advocates the use of electronic medical records for communication. Standardized prescription forms and supplementary safety measures are outlined. Review frequency is adjusted according to patient condition—daily for critically ill or unstable patients and as dictated by institutional protocols for stable patients. Evidence indicates that adherence to these protocols reduces mortality, complications, and prescription errors. Conclusion The KSPEN practical guide offers a robust framework for the safe delivery of EN tailored to Korea’s healthcare context. It emphasizes standardized protocols and interdisciplinary collaboration to improve nutritional outcomes, patient safety, and operational efficiency. Rigorous implementation and monitoring of adherence are critical for its success.

Purpose: This study aimed to develop a comprehensive practical guide for enteral nutrition (EN) designed to enhance patient safety and reduce complications in Korea. Under the leadership of the Korean Society for Parenteral and Enteral Nutrition (KSPEN), the initiative sought to standardize EN procedures, improve decision-making, and promote effective multidisciplinary communication. Methods: The KSPEN EN committee identified key questions related to EN practices and organized them into seven sections such as prescribing, delivery route selection, formula preparation, administration, and quality management. Twenty-one experts, selected based on their expertise, conducted a thorough literature review to formulate evidence-based recommendations. Drafts underwent peer review both within and across disciplines, with final revisions completed by the KSPEN Guideline Committee. The guide, which will be published in three installments, addresses critical elements of EN therapy and safety protocols. Results: The practical guide recommends that EN orders include detailed elements and advocates the use of electronic medical records for communication. Standardized prescription forms and supplementary safety measures are outlined. Review frequency is adjusted according to patient condition—daily for critically ill or unstable patients and as dictated by institutional protocols for stable patients. Evidence indicates that adherence to these protocols reduces mortality, complications, and prescription errors. Conclusion The KSPEN practical guide offers a robust framework for the safe delivery of EN tailored to Korea’s healthcare context. It emphasizes standardized protocols and interdisciplinary collaboration to improve nutritional outcomes, patient safety, and operational efficiency. Rigorous implementation and monitoring of adherence are critical for its success.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background: Small cell lung cancer (SCLC) is called ‘smoker’s disease’ because it is strongly associated with smoking and most cases occur in smokers. However, it can also occur in never smokers. We investigated the clinical features of never smokers with SCLC and compared their treatment outcomes with those of smokers with SCLC. Methods: We retrospectively reviewed the clinical data of patients who had proven SCLC and had received chemotherapy at a single cancer center between July 2002 and April 2021. Results: Of 1,643 patients, 1,416 (86.2%) were enrolled in this study. A total of 162 (11.4%) and 1,254 (88.6%) patients were never smokers and smokers, respectively. There were more female never smokers than smokers (n=130; 80.2% vs. 79, 6.3%, p=0.000), and the incidence of ischemic heart disease was lower among never smokers than among smokers (4/1,416, [2.5%] vs. 83/1,416 [6.6%], p=0.036). Never smokers showed less symptoms at diagnosis than smokers (80.9% vs. 87.2%, p=0.037); however, they showed more toxicity after first-line treatment (61.7% vs. 47.8%, p=0.001). The objective response rate (ORR) was significantly higher in never smokers (74.1% vs. 59.6%, p=0.000). In the multivariate analysis, never smoking and second-line treatment were associated with a better ORR. However, progression-free survival and overall survival were not significantly different between never smokers and smokers. Conclusion In conclusion, never smokers accounted for 11.4% of patients with SCLC. They had distinguishing clinical characteristics and showed better chemotherapeutic responses than smokers.