Rectal cancer, which accounts for approximately 40% of colorectal cancers, remains a major clinical concern. Recent advances in diagnostic imaging, surgical techniques, radiotherapy, and systemic treatment have steadily improved rectal cancer outcomes. Considering this, the Korean Rectal Cancer Multidisciplinary (KRCM) Committee has aimed to provide clinicians and policymakers with up-to-date, evidence-based clinical practice guidelines to support optimal decision-making, reflecting current evidence, the Korean healthcare context, and patient values and preferences. The Clinical Practice Guidelines for Rectal Cancer version 2.0 were developed through multidisciplinary collaboration with related academic societies, building upon and updating the KRCM Clinical Practice Guidelines version 1.0 (titled “Multidisciplinary guidelines for the management of rectal cancer”). These consensus guidelines of the KRCM were established based on a comprehensive literature review, evidence synthesis, with recommendation development guided by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology, and consideration of applicability in real-world clinical practice under the national health insurance system. Each recommendation has been presented with its strength and level of evidence.

Background/Aims: Nocturnal acid reflux disrupts sleep and impairs quality of life. Proton pump inhibitors provide insufficient suppression of nocturnal acid secretion, whereas fexuprazan offers prolonged acid suppression. We compared the efficacy of fexuprazan and esomeprazole in controlling nocturnal reflux. Methods: In a randomized and crossover study, patients received fexuprazan or esomeprazole daily for 4 weeks, followed by a washout and crossover to the alternate medication for another 4 weeks, with a final washout completing the sequence. Severity (scores 0-10), frequency, sleep disturbance, and medication preferences were evaluated. Results: Thirty-nine patients were enrolled and randomized to receive either fexuprazan (n = 20) or esomeprazole (n = 19) first. After the first treatment, fexuprazan reduced severity from 7.5 ± 1.7 to 1.4 ± 1.7 (81.3% decrease), versus 7.8 ± 1.5 to 2.8 ± 1.9 (64.1% decrease) with esomeprazole (P = 0.012). In patients with severe symptoms (scores ≥ 7), fexuprazan led to significantly greater improvement than esomeprazole (P = 0.008). Following the first washout, the second crossover treatment resulted in greater improvement in symptom severity with fexuprazan (P = 0.001). During the second washout, nocturnal symptoms severity and frequencies were better controlled with fexuprazan than with esomeprazole (P = 0.005 and 0.019). Patients who switched from esomeprazole to fexuprazan preferred fexuprazan (P = 0.018). Conclusions Fexuprazan was more effective than esomeprazole in controlling nocturnal reflux symptom, particularly in patients with severe symptoms. Fexuprazan may offer a therapeutic advantage for patients with severe and persistent nocturnal reflux despite proton pump inhibitor therapy.

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.

Objective: To explain process of developing basic veterinary clinical performance guidelines, based on frequently observable clinical manifestations, thereby supporting competencybased veterinary education in Korea. Methods: A structured review of learning outcomes established by Korean Association of Veterinary Medical Colleges (KAVMC) was conducted by a planning committee including veterinary educators, practitioners, and advisory members. Owner-oriented descriptions were used to frame each performance task, and each was mapped to corresponding learning outcomes. These tasks were aligned with learning outcomes recommended by the KAVMC to support the development of communication, clinical reasoning, and performance-related competencies among veterinary students, thereby enhancing day-one clinical readiness. Results: In total, 63 clinical manifestations for a guidebook format that can be used for clinical education were identified and categorized by organ systems that are described in language understandable to animal owners. Conclusions and Relevance: The basic veterinary clinical performance guidelines based on common clinical manifestations would serve as a vital component in veterinary education to reinforce core graduation competencies.

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.

Levothyroxine overdose is uncommon in adults and often presents with variable clinical severity, ranging from overt thyrotoxicosis to an entirely asymptomatic course. We report the case of a 47-year-old woman with a history of total thyroidectomy who intentionally ingested 10 mg of levothyroxine along with supratherapeutic doses of zolpidem and clonazepam at once in a suicide attempt. She presented three days later without adrenergic or gastrointestinal symptoms. Laboratory tests revealed markedly elevated free thyroxine and total triiodothyronine with suppressed thyroid stimulating hormone. She was treated with propranolol for heart rate control and empirical hydrocortisone in case of adrenal insufficiency. She remained asymptomatic with gradual biochemical improvement and normalization of thyroid function on follow-up. The protective effect of co-ingested supratherapeutic doses of gamma-aminobutyric acid (GABA)-ergic agents suggests a potential role for anti-anxiety medications in mitigating severe adrenergic symptoms in endogenous thyrotoxicosis, such as Graves’ disease.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.