To ensure high-quality bioresources and standardize biobanks, there is an urgent need to develop and disseminate educational training programs in accordance with ISO 20387, which was developed in 2018. The standardization of biobank education programs is also required to train biobank experts. The subdivision of categories and levels of education is necessary for jobs such as operations manager (bank president), quality manager, practitioner, and administrator. Essential training includes programs tailored for beginner, intermediate, and advanced practitioners, along with customized training for operations managers. We reviewed and studied ways to develop an appropriate range of education and training opportunities for standard biobanking education and the training of experts based on KS J ISO 20387. We propose more systematic and professional biobanking training programs in accordance with ISO 20387, in addition to the certification programs of the National Biobank and the Korean Laboratory Accreditation System. We suggest various training programs appropriate to a student’s affiliation or work, such as university biobanking specialized education, short-term job training at unit biobanks, biobank research institute symposiums by the Korean Society of Pathologists, and education programs for biobankers and researchers. Through these various education programs, we expect that Korean biobanks will satisfy global standards, meet the needs of users and researchers, and contribute to the advancement of science.

To ensure high-quality bioresources and standardize biobanks, there is an urgent need to develop and disseminate educational training programs in accordance with ISO 20387, which was developed in 2018. The standardization of biobank education programs is also required to train biobank experts. The subdivision of categories and levels of education is necessary for jobs such as operations manager (bank president), quality manager, practitioner, and administrator. Essential training includes programs tailored for beginner, intermediate, and advanced practitioners, along with customized training for operations managers. We reviewed and studied ways to develop an appropriate range of education and training opportunities for standard biobanking education and the training of experts based on KS J ISO 20387. We propose more systematic and professional biobanking training programs in accordance with ISO 20387, in addition to the certification programs of the National Biobank and the Korean Laboratory Accreditation System. We suggest various training programs appropriate to a student’s affiliation or work, such as university biobanking specialized education, short-term job training at unit biobanks, biobank research institute symposiums by the Korean Society of Pathologists, and education programs for biobankers and researchers. Through these various education programs, we expect that Korean biobanks will satisfy global standards, meet the needs of users and researchers, and contribute to the advancement of science.

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.

To ensure high-quality bioresources and standardize biobanks, there is an urgent need to develop and disseminate educational training programs in accordance with ISO 20387, which was developed in 2018. The standardization of biobank education programs is also required to train biobank experts. The subdivision of categories and levels of education is necessary for jobs such as operations manager (bank president), quality manager, practitioner, and administrator. Essential training includes programs tailored for beginner, intermediate, and advanced practitioners, along with customized training for operations managers. We reviewed and studied ways to develop an appropriate range of education and training opportunities for standard biobanking education and the training of experts based on KS J ISO 20387. We propose more systematic and professional biobanking training programs in accordance with ISO 20387, in addition to the certification programs of the National Biobank and the Korean Laboratory Accreditation System. We suggest various training programs appropriate to a student’s affiliation or work, such as university biobanking specialized education, short-term job training at unit biobanks, biobank research institute symposiums by the Korean Society of Pathologists, and education programs for biobankers and researchers. Through these various education programs, we expect that Korean biobanks will satisfy global standards, meet the needs of users and researchers, and contribute to the advancement of science.

We report a case of a ruptured triple hormone-secreting adrenal mass with hyperaldosteronism, hypercortisolism, and elevated normetanephrine levels, diagnosed as adrenal cortical carcinoma (ACC) by histology. A 53-year-old male patient who initially presented with abdominal pain was referred to our hospital for angiocoagulation of an adrenal mass rupture. Abdominal computed tomography revealed a heterogeneous 19×11×15 cm right adrenal mass with invasion into the right lobe of the liver, inferior vena cava, retrocaval lymph nodes, and aortocaval lymph nodes. Angiocoagulation was performed. Laboratory evaluation revealed excess cortisol via a positive 1-mg overnight dexamethasone suppression test, primary hyperaldosteronism via a positive saline infusion test, and plasma normetanephrine levels three times higher than normal. An adrenal mass biopsy was performed for pathological confirmation to commence palliative chemotherapy because surgical management was not deemed appropriate considering the extent of the tumor. Pathological examination revealed stage T4N1M1 ACC. The patient started the first cycle of adjuvant mitotane therapy along with adjuvant treatment with doxorubicin, cisplatin, and etoposide, and was discharged. Clinical cases of dual cortisol- and aldosterone-secreting ACCs or ACCs presenting as pheochromocytomas have occasionally been reported; however, both are rare. Moreover, to the best of our knowledge, a triple hormone-secreting ACC has not yet been reported. Here, we report a rare case and its management. This case report underscores the necessity of performing comprehensive clinical and biochemical hormone evaluations in patients with adrenal masses because ACC can present with multiple hormone elevations.

Background/Aims: Sex differences in the prognosis of heart failure (HF) have yielded inconsistent results, and data from Asian populations are even rare. This study aimed to investigate sex differences in clinical characteristics and long-term prognosis among Korean patients with HF. Methods: A total of 5,625 Korean patients hospitalized for acute HF were analyzed using a prospective multi-center registry database. Baseline clinical characteristics and long-term outcomes including HF readmission and death were compared between sexes. Results: Women were older than men and had worse symptoms with higher N-terminal pro B-type natriuretic peptide levels. Women had a significantly higher proportion of HF with preserved ejection fraction (HFpEF). There were no significant differences in in-hospital mortality and rate of guideline-directed medical therapies in men and women. During median follow- up of 3.4 years, cardiovascular death (adjusted hazard ratio [HR], 1.38; 95% confidence interval [CI], 1.07–1.78; p = 0.014), and composite outcomes of death and HF readmission (adjusted HR, 1.13; 95% CI, 1.01–1.27; p = 0.030) were significantly higher in men than women. When evaluating heart failure with reduced ejection fraction (HFrEF) and HFpEF separately, men were an independent risk factor of cardiovascular death in patients with HFrEF. Clinical outcome was not different between sexes in HFpEF. Conclusions In the Korean multi-center registry, despite having better clinical characteristics, men exhibited a higher risk of all-cause mortality and readmission for HF. The main cause of these disparities was the higher cardiovascular mortality rate observed in men compared to women with HFrEF.

Background: We aimed to examine the prevalence of burnout among imaging cardiologists in Korea and to identify its associated factors. Methods: An online survey of imaging cardiologists affiliated with university hospitals in Korea was conducted using SurveyMonkey ® in November 2023. The validated Korean version of the Maslach Burnout Inventory-Human Service Survey was used to assess burnout across three dimensions: emotional exhaustion, depersonalization, and lack of personal accomplishment. Data on demographics, work environment factors, and job satisfaction were collected using structured questionnaires. Results: A total of 128 imaging cardiologists (46.1% men; 76.6% aged ≤ 50 years) participated in the survey. Regarding workload, 74.2% of the respondents interpreted over 50 echocardiographic examinations daily, and 53.2% allocated > 5 of 10 working sessions per week to echocardiographic laboratory duties. Burnout levels were high, with a significant proportion of participants experiencing emotional exhaustion (28.1%), depersonalization (63.3%), and a lack of personal accomplishment (92.2%). Younger age (< 50 years) was correlated with higher emotional exhaustion risk, while more research time was protective against burnout in the depersonalization domain. Factors, such as being single, living with family, and specific job satisfaction facets, including uncontrollable workload and value mismatch, were associated with varying levels of burnout risk across different dimensions Conclusion Our study underscores the high burnout rates among Korean imaging cardiologists, attributed to factors such as the subjective environment and job satisfaction.Hence, evaluating and supporting cardiologists in terms of individual values and subjective factors are important to effectively prevent burnout..