Background: As nationwide government-led whole-genome sequencing (WGS) projects progress, optimizing the clinical integration of large-scale WGS results is crucial. We explored how the initial analysis from Korea’s First WGS Pilot Study for Rare Diseases was applied in clinical practice, and then we reanalyzed the data comprehensively at Samsung Medical Center (SMC) Seoul, Korea. Methods: A prospective cohort study designed to collect WGS data under a Korean national initiative was conducted from August 2020 to December 2021. We focused on patients with rare diseases recruited from 16 university hospitals. The participants included 5,000 individuals (2,200 probands and 2,800 family members). The initial WGS data and diagnostic reference reports (from 682 probands and 484 family members), generated based on the First Korean WGS Pilot Study for Rare Diseases, were subsequently reanalyzed by SMC. Results: The initial analysis of the First Korean WGS Pilot Study data revealed a diagnostic rate of 17%. Upon receiving these results, the SMC conducted two rounds of reanalysis, increasing the diagnostic rate from 15% in the first analysis, to 18% in the second, and finally to 24% in the third (P = 1.6 × 10 −5 ). Key factors in improving the genetic diagnosis included increased detection of novel (likely) pathogenic variants (P = 1.0 × 10 −4 ), improved diagnostic rates with larger family recruitment (P = 0.004), and refined clinical information for more precise genotype–phenotype correlation analysis (40%). Conclusions Although national WGS projects lay a foundation for rare disease diagnosis, hospital-level reanalysis and multidisciplinary collaborations are crucial for optimizing diagnostic outcomes.

Purpose: The aim of this study is to compare the detection ability of quality of life (QoL) changes and responsiveness of the KOrean QUality of life in Stomach cancer patients Study group (KOQUSS)-40 and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ). Materials and Methods: A multicenter prospective observational study was conducted to evaluate QoL changes after various gastrectomies between January 2021 and April 2022. Participants were instructed to complete the KOQUSS-40 and EORTC QLQ-C30/STO22 preoperatively and at 1, 3, 6, and 12 months postoperatively. QoL changes over time and QoL responsiveness were assessed for each questionnaire. Results: Data from 491 patients who underwent curative gastrectomy for gastric cancer at 22 institutions were analyzed. The summary scores of the KOQUSS-40 and EORTC QLQ-STO22 showed significant differences between the total and proximal gastrectomy groups (p=0.044 and p=0.038, respectively), but no difference was observed for the EORTC QLQ-C30. Dysphagia on the KOQUSS-40 was significantly different between the total and proximal gastrectomy groups (p=0.031); however, dysphagia on the EORTC QLQ-STO22 did not differ. The responsiveness of the KOQUSS-40 was similar to that of the EORTC QLQ in patients who experienced ≥ 10% body weight loss, but approximately 10% less in patients receiving adjuvant chemotherapy than the EORTC QLQ. Conclusion KOQUSS-40 has several advantages over EORTC QLQ-C30/STO22 when comparing QoL between the total and proximal gastrectomy groups. The findings provide information for researchers investigating the QoL of patients who have undergone curative gastrectomy for gastric cancer.

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.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

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.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

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.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Purpose: Biliary atresia (BA) is a rare but progressive cholangiopathy and the leading cause of pediatric liver transplantation worldwide. The Korean Association of Pediatric Surgeons (KAPS) has conducted three national surveys (2001, 2011, and 2023) to assess long-term trends in the diagnosis, treatment, and outcomes of BA. This study provides a comparative analysis of the 2nd and 3rd national surveys, with reference to selected findings from the 1st survey. Methods: This study included 453 patients from the 3rd national survey (2011–2021) and 435 patients from the 2nd survey (2001–2010), all of whom underwent Kasai portoenterostomy. Data were collected via electronic case report forms from pediatric surgical centers nationwide. Comparisons were made regarding demographics, clinical features, diagnostic patterns, operative details, follow-up outcomes, and survival. Kaplan–Meier analysis was used to evaluate long-term survival. Results: The mean number of BA patients per year remained stable between surveys (43.5 in the 2nd, 41.18 in the 3rd), though centralization of care increased, with 61.5% of cases managed by two major institutions in the 3rd survey. The median age at surgery decreased, and the use of preoperative imaging (especially magnetic resonance cholangiopancreatography) increased. The 10-year native liver survival rate declined from 59.8% to 53.7%, while overall 10-year survival improved slightly (92.9% to 93.2%). Postoperative complications, such as cholangitis and liver failure, persisted but were better categorized. The 3rd survey also reported improved mortality (4.9%) and reduced follow-up loss (11.5%) compared to the 2nd survey. Conclusion While overall survival after Kasai operation has remained high and even improved, native liver survival has slightly declined. The findings reflect earlier diagnosis, more consistent diagnostic imaging, and increasing centralization of care. These trends underscore the importance of long-term nationwide data collection in guiding future strategies for BA management in Korea.