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/Aims: We investigated the clinical practice patterns of Korean endoscopists for the endoscopic resection of colorectal polyps. Methods: From September to November 2021, an online survey was conducted regarding the preferred resection methods for colorectal polyps, and responses were compared with the international guidelines. Results: Among 246 respondents, those with <4 years, 4–9 years, and ≥10 years of experiencein colonoscopy practices accounted for 25.6%, 34.1%, and 40.2% of endoscopists, respectively. The most preferred resection methods for non-pedunculated lesions were cold forceps polypectomy for ≤3 mm lesions (81.7%), cold snare polypectomy for 4–5 mm (61.0%) and 6–9 mm (43.5%) lesions, hot endoscopic mucosal resection (EMR) for 10–19 mm lesions (72.0%), precut EMR for 20–25 mm lesions (22.0%), and endoscopic submucosal dissection (ESD) for ≥26 mm lesions (29.3%). Hot EMR was favored for pedunculated lesions with a head size <20 mm and stalk size <10 mm (75.6%) and for those with a head size ≥20 mm or stalk size ≥10 mm (58.5%). For suspected superficial and deep submucosal lesions measuring 10–19 mm and ≥20 mm, ESD (26.0% and 38.6%) and surgery (36.6% and 46.3%) were preferred, respectively. The adherence rate to the guidelines ranged from 11.2% to 96.9%, depending on the size, shape, and histology of the lesions. Conclusions Adherence to the guidelines for endoscopic resection techniques varied depend-ing on the characteristics of colorectal polyps. Thus, an individualized approach is required to increase adherence to the guidelines.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

Background: The Korean Occupational Stress Scale (KOSS) was developed in 2004. During this time, industrial structures have evolved, and societal awareness of occupational stress has changed. This study aims to develop and validate a revised version of the Korean Occupational Stress Scale (KOSS®19), tailored for workers, reflecting these changes. Methods: The KOSS®19 was developed based on the 26-item KOSS–short form (SF) through a review by eight experts. A survey was conducted including 359 service industry workers, comprising the KOSS®19, Burnout, and Depression scales. The KOSS®19 subscales were restructured, and their reliability and validity were evaluated. Results: The KOSS®19 composed of eight subscales: hazardous physical environment (2 items), high job demand (3 items), insufficient job control (2 items), low social support (2 items), job insecurity (2 items), organizational injustice (4 items), lack of reward (2 items), and work-life imbalance (2 items). The reliability and validity of the KOSS®19 were found to be satisfactory. Conclusions The KOSS®19 is a suitable tool for assessing occupational stress, effectively replacing the original KOSS and KOSS-SF.

Background: Emotional labor refers to the management of emotions and expressions to meet the emotional requirements of a job role. This study aimed to develop a revised version of the Korean Emotional Labor Scale (KELS®11), based on the first edition (KELS-24) introduced in 2014, and to provide practical applications and guidelines for its use in the Korean workplace through a validation process. Methods: The revised version of KELS®11 was derived from the 24-item KELS, following a review process involving eight experts. To validate the scale’s reliability and validity, a self-administered survey was conducted among 359 service industry workers using KELS®11, burnout, and depression scales. KELS®11 was reclassified, and its reliability and validity were evaluated. Receiver operating characteristic curve analysis was conducted to establish sex-specific cutoff values (normal vs. high-risk groups). Results: KELS®11 was designed to account for individual, organizational, and cultural contexts. It consists of four subscales and 11 items: “emotional regulation” (2 items), “emotional dissonance” (3 items), “organizational monitoring” (2 items), and “organizational protective system for emotional labor” (4 items). KELS®11 demonstrated good validity (content validity ratio: 0.84; item convergence/discriminant validity success rates: 100%; correlation with burnout: r = 0.185–0.436, p < 0.01; correlation with depression: r = 0.128–0.339, p < 0.05) and reliability (Cronbach’s alpha: 0.597–0.795). Additionally, sex-specific reference values were established to determine risk groups based on the intensity of emotional labor exposure. Conclusions KELS®11 is a validated and reliable measurement tool designed to assess the intensity and magnitude of emotional labor in the workplace. The revised tool reflects critical considerations in the development of emotional labor measurement scales.

Background: Workplace violence refers to any act or threat of physical violence, verbal abuse, harassment, intimidation, bullying, mobbing, or other aggressive and disruptive behaviors that occur at work. This study aims to develop and validate a revision of the Korean Workplace Violence Scale (KWVS®13), based on the first edition of the Korean Workplace Violence Scale (KWVS-24), and to provide practical applications and guidelines for the Korean workplace environment. Methods: The revised KWVS®13 was developed by restructuring the 24-item KWVS through a review process involving eight experts. To validate the reliability and validity of KWVS®13, a self-administered survey comprising KWVS®13, burnout, and depression scales was conducted among 359 service industry workers. KWVS®13 was reclassified, and its reliability and validity were assessed. Receiver operating characteristic curve analysis was performed to establish sex-specific cutoff values (normal vs. risk) of the scale. Results: KWVS®13 consists of 13 items across four subscales: “psychological and sexual violence from customers” (4 items), “psychological and sexual violence from supervisors or coworkers” (4 items), “physical assault from customers, supervisors, or coworkers” (2 items), and “organizational protective system for workplace violence” (3 items). We found that KWVS®13 shows relatively good validity (content validity ratio for content validity: 0.888; success rate of item convergent and discriminant validity: 100%, and significant correlation coefficient with burnout (r = 0.115–0.83, p < 0.05) and depression (r = 0.098–0.348, p < 0.05) with the exception of Organizational Violence Protection System for Workplace Violence) and reliability (Cronbach’s alpha: 0.827–0.860). The reference values for determining risk groups according to the intensity of exposure to workplace violence are presented separately by sex. Conclusions KWVS®13 is a robust and useful measurement tool to objectively and quantitatively assess the intensity and magnitude of workplace violence. It incorporates important considerations for workplace violence assessment and provides a reliable framework for evaluating workplace violence in various professional settings.

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/Aims: We investigated the clinical practice patterns of Korean endoscopists for the endoscopic resection of colorectal polyps. Methods: From September to November 2021, an online survey was conducted regarding the preferred resection methods for colorectal polyps, and responses were compared with the international guidelines. Results: Among 246 respondents, those with <4 years, 4–9 years, and ≥10 years of experiencein colonoscopy practices accounted for 25.6%, 34.1%, and 40.2% of endoscopists, respectively. The most preferred resection methods for non-pedunculated lesions were cold forceps polypectomy for ≤3 mm lesions (81.7%), cold snare polypectomy for 4–5 mm (61.0%) and 6–9 mm (43.5%) lesions, hot endoscopic mucosal resection (EMR) for 10–19 mm lesions (72.0%), precut EMR for 20–25 mm lesions (22.0%), and endoscopic submucosal dissection (ESD) for ≥26 mm lesions (29.3%). Hot EMR was favored for pedunculated lesions with a head size <20 mm and stalk size <10 mm (75.6%) and for those with a head size ≥20 mm or stalk size ≥10 mm (58.5%). For suspected superficial and deep submucosal lesions measuring 10–19 mm and ≥20 mm, ESD (26.0% and 38.6%) and surgery (36.6% and 46.3%) were preferred, respectively. The adherence rate to the guidelines ranged from 11.2% to 96.9%, depending on the size, shape, and histology of the lesions. Conclusions Adherence to the guidelines for endoscopic resection techniques varied depend-ing on the characteristics of colorectal polyps. Thus, an individualized approach is required to increase adherence to the guidelines.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

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.