Purpose: The male predominance in the incidence of gastric cancer (GC) is established; however, sex differences in the prognosis of GC remain controversial. As such, this study analyzed the prognosis of patients with GC based on age and sex. Materials and Methods: Data from 14,739 patients diagnosed with GC at Seoul National University Bundang Hospital between 2003 and 2023 were analyzed. Baseline characteristics, histological types of GC, overall and GC-specific survival rates (age and stage stratification), and associated risk factors were analyzed. Results: Females were significantly younger (p < 0.001) and exhibited more gastric body cancers (p < 0.001) and tumors with diffuse-type or poorly differentiated histology (p < 0.001) than males. Females exhibited an advantage over males in terms of overall survival (p=0.004), but not in GC-specific survival. However, age stratification revealed significant sex differences, that females < 50 years of age exhibited survival disadvantages (p < 0.001); however, this trend was reversed with age, and females > 60 years exhibited survival advantages (p < 0.001) for both overall and GC-specific survival. This may be explained by the lower ratio of diffuse-type GC as females age. Furthermore, in the analysis according to stage, females with stage IV disease exhibited significant survival disadvantages, with significantly younger age and a higher proportion of diffuse-type GC which exhibits aggressive features, resulting in poorer survival than in males. Conclusion Age and stage stratification revealed significant differences in survival between the sexes, which can be helpful for public health strategies.

Purpose: This study evaluated the feasibility of radical nephrectomy and thrombectomy without cardiopulmonary bypass (CPB) in patients with renal cell carcinoma (RCC) and level IV venous tumor thrombus, compared with CPB-assisted surgery. Materials and Methods: This retrospective cohort study analyzed patients with RCC and level IV venous tumor thrombus who underwent surgery at a single center between 2014 and 2020. Feasibility of non-CPB surgery was assessed by comparing perioperative safety-related outcomes, overall survival (OS), and progression-free survival (PFS) between the non-CPB and CPB groups. Perioperative outcomes included operative time, blood loss, severe complications (Clavien-Dindo classification grade ≥III), intensive care unit (ICU) stay, and mortality. Kaplan-Meier analysis and generalized Wilcoxon tests were used to compare survival outcomes. Results: A total of 16 patients met eligibility criteria: 5 underwent surgery without CPB, and 11 underwent CPB-assisted surgery. Median operative time was similar between the CPB and non-CPB groups (490 minutes vs. 480 minutes, p=0.650). Compared with the CPB group, blood loss was lower in the non-CPB group (4000 mL vs. 1080 mL, p=0.333). Severe complications occurred in 36.4% of CPB patients and 0% of non-CPB patients (p=0.245). ICU stay was comparable between the non-CPB and CPB groups (2 days vs. 3 days, p=0.356). OS did not differ significantly between groups (p=0.180), whereas PFS was longer in the non-CPB group (p=0.041). Conclusions Radical nephrectomy and thrombectomy without CPB appears feasible and may be associated with lower perioperative morbidity and blood loss without compromising oncologic outcomes. Non-CPB surgery should be considered in selected patients with level IV venous tumor thrombus when technically feasible.

Background and Objectives: Clinical characteristics and outcomes in acute heart failure (AHF) vary by phenotype. We assessed phenotype-specific features, treatment patterns, and outcomes in a nationwide Korean cohort. Methods: The Korean Heart Failure III registry prospectively enrolled 7,351 AHF admissions at 47 hospitals. Among 6,777 patients with available left ventricular ejection fraction (EF), phenotypes were defined as heart failure with reduced EF (HFrEF, ≤40%), mildly reduced EF (HFmrEF,41–49%), or preserved EF (HFpEF, ≥50%). The primary endpoint was a 12-month composite of all-cause death or heart transplantation, evaluated from index admission and, among hospital survivors, from discharge. We used inverse probability weighting (multinomial generalized boosted models with stabilized, trimmed weights) and weighted Cox proportional-hazards models to estimate hazard ratios (HRs). Results: Phenotype distribution was 58.9% HFrEF, 13.6% HFmrEF, and 27.5% HFpEF. Crude 12-month composite rates from index admission were 13.4% (HFrEF), 12.7% (HFmrEF), and 16.8% (HFpEF). After weighting, from index admission, HFmrEF (HR, 0.892; 95% confidence interval [CI], 0.731–1.088) and HFpEF (HR, 1.101; 95% CI, 0.939–1.291) did not differ from HFrEF; from discharge, HFpEF had modestly higher risk (HR, 1.207; 95% CI, 1.008–1.445) whereas HFmrEF did not (HR, 1.039; 95% CI, 0.844–1.279). Hyponatremia and chronic kidney disease were consistent adverse markers, while angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker use at discharge was protective. Conclusions Across the EF spectrum, phenotypes showed distinct profiles and risk. Postdischarge risk was modestly higher in HFpEF, supporting phenotype-tailored care and systematic discharge optimization in Korean patients with AHF.

In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

Objective: We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods: Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models. Results: Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Purpose: 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. Methods: 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. Results: 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. Conclusion 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.

Background/Aims: Cholecystectomy for gallbladder (GB) polyps is performed primarily based on preoperative images. This study examined the accuracy of surgical indications commonly used in clinical practice for detecting neoplastic polyps and investigated further clues for predicting neoplastic polyps. Methods: This retrospective study included 385 patients who underwent a cholecystectomy for GB polyps. The predictive performances of seven surgical indications were compared by fitting the receiver operating characteristic curves. Logistic regression analysis was used to identify the candidate variables associated with predicting neoplastic polyps. Results: Neoplastic polyps were identified in 18.9% (n=62) of the 385 patients assessed. The neoplastic group contained more females than males, larger polyps, more frequent solitary lesions, and lower platelet counts than the non-neoplastic group. Current surgical indications revealed an unsatisfactory prediction for neoplastic polyps. The optimal cutoff polyp size for neoplastic polyps by ultrasound (US) was larger than by computed tomography (CT) (12 mm vs. 10 mm). The proportion of pathologic neoplastic polyps was higher when both US and CT images were used than that predicted using a single test. Logistic regression analysis revealed larger polyps, increasing age, female sex, and lower platelet count to be associated with neoplastic polyps. Conclusions The current indications for cholecystectomy in GB polyps have a low predictive value for neoplastic lesions that can lead to overtreatment. Combining the polyp size from US and CT images may reduce unnecessary surgery. In addition, knowledge of the patient's age, sex, and platelet count could help make more selective surgical decisions for neoplastic polyps.

Purpose: Adjuvant treatment for craniopharyngioma after surgery is controversial. Adjuvant external beam radiation therapy (EBRT) can increase the risk of long-term sequelae. Stereotactic radiosurgery (SRS) is used to reduce treatment-related toxicity.In this study, we compared the treatment outcomes and toxicities of adjuvant therapies for craniopharyngioma. Materials and Methods: We analyzed patients who underwent craniopharyngioma tumor removal between 2000 and 2017. Of the 153 patients, 27 and 20 received adjuvant fractionated EBRT and SRS, respectively. We compared the local control (LC), progression-free survival (PFS), and overall survival between groups that received adjuvant fractionated EBRT, SRS, and surveillance. Results: The median follow-up period was 77.7 months. For SRS and surveillance, the 10-year LC was 57.2% and 57.4%, respectively. No local progression was observed after adjuvant fractionated EBRT. One patient in the adjuvant fractionated EBRT group died owing to glioma 94 months after receiving radiotherapy (10-year PFS: 80%). The 10-year PFS was 43.6% and 50.7% in the SRS and surveillance groups, respectively. The treatment outcomes significantly differed according to adjuvant treatment in nongross total resection (GTR) patients. Additional treatment-related toxicity was comparable in the adjuvant fractionated EBRT and other groups. Conclusion Adjuvant fractionated EBRT could be effective in controlling local failure, especially in patients with non-GTR, while maintaining acceptable treatment-related toxicity.

Objective: We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods: Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models. Results: Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Purpose: 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. Methods: 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. Results: 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. Conclusion 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.