Objectives: This study aimed to assess the clinical burden, a critical determinant of medication adherence in patients with schizophrenia, after the administration of Aripiprazole once-monthly (AOM). Methods: This study was a retrospective, non-interventional, multicenter, naturalistic observational study conducted through the analysis of participants’ electronic medical records. Study participants were recruited from eight sites. Data were collected at baseline, defined as the time of AOM administration, and at 1, 3, 6, 9, and 12 months thereafter. The primary outcome measure was the change in the Clinical Global Impression-Clinical Benefit (CGI-CB) score over 12 months, and the secondary outcome measure was the change in the Clinical Global Impression-Improvement (CGI-I) score. Results: The data of 139 participants were analyzed, revealing a statistically significant decrease of 26.8% in CGI-CB scores and 13.4% in CGI-I scores over 12 months. Upon comparison between adjacent visit intervals, significant reductions were observed for both measures between month 3 and month 6. Conclusions This study is the first multicenter investigation to simultaneously evaluate the clinical efficacy and tolerability of transitioning to AOM in the context of polypharmacy. The study suggested that AOM may contribute to reducing the clinical burden, thereby improving the quality of life for patients with schizophrenia.

Objectives: This study aimed to assess the clinical burden, a critical determinant of medication adherence in patients with schizophrenia, after the administration of Aripiprazole once-monthly (AOM). Methods: This study was a retrospective, non-interventional, multicenter, naturalistic observational study conducted through the analysis of participants’ electronic medical records. Study participants were recruited from eight sites. Data were collected at baseline, defined as the time of AOM administration, and at 1, 3, 6, 9, and 12 months thereafter. The primary outcome measure was the change in the Clinical Global Impression-Clinical Benefit (CGI-CB) score over 12 months, and the secondary outcome measure was the change in the Clinical Global Impression-Improvement (CGI-I) score. Results: The data of 139 participants were analyzed, revealing a statistically significant decrease of 26.8% in CGI-CB scores and 13.4% in CGI-I scores over 12 months. Upon comparison between adjacent visit intervals, significant reductions were observed for both measures between month 3 and month 6. Conclusions This study is the first multicenter investigation to simultaneously evaluate the clinical efficacy and tolerability of transitioning to AOM in the context of polypharmacy. The study suggested that AOM may contribute to reducing the clinical burden, thereby improving the quality of life for patients with schizophrenia.

Purpose: To develop a deep learning model classifying the laterality of optical coherence tomography (OCT) images. Methods: The study included two-dimensional OCT images (horizontal/vertical macular section) from Seoul National University Hospital. A deep learning model based on ResNet-18 was developed and trained to classify whether OCT images were horizontal or vertical sections and to predict the laterality of the images. Analysis of the results included calculating a mean area under the receiver operating characteristic curve (AUROC) and evaluating accuracy, specificity, and sensitivity. Gradient-weighted class activation for mapping visualization highlighted critical regions for classification. Results: A total of 5,000 eyes of 2,500 patients (10,000 images) was included in the development process. The test dataset consisted of 1,000 eyes of 500 patients (590 eyes without macular abnormalities, 208 epiretinal membranes, 111 age-related macular degenerations, 56 central macular edemas, 23 macular holes, and 12 other macular abnormalities). The deep learning model predicted the OCT section of the eyes in the test dataset with a mean AUROC of 0.9967. The accuracy, sensitivity, and specificity were 0.9835, 0.9870, and 0.9800, respectively. The model predicted the laterality of the eyes in horizontal OCT images with a mean AUROC of 1.0000. The accuracy, sensitivity, and specificity were 0.9970, 1.0000, and 0.9940, respectively. Using vertical OCT images, deep learning models failed to demonstrate any predictive performance in laterality classification. Conclusions We developed a deep learning model to classify the horizontal/vertical sections of OCT images and predict the laterality of horizontal OCT images with high accuracy, sensitivity, and specificity.

Objectives: This study aimed to assess the clinical burden, a critical determinant of medication adherence in patients with schizophrenia, after the administration of Aripiprazole once-monthly (AOM). Methods: This study was a retrospective, non-interventional, multicenter, naturalistic observational study conducted through the analysis of participants’ electronic medical records. Study participants were recruited from eight sites. Data were collected at baseline, defined as the time of AOM administration, and at 1, 3, 6, 9, and 12 months thereafter. The primary outcome measure was the change in the Clinical Global Impression-Clinical Benefit (CGI-CB) score over 12 months, and the secondary outcome measure was the change in the Clinical Global Impression-Improvement (CGI-I) score. Results: The data of 139 participants were analyzed, revealing a statistically significant decrease of 26.8% in CGI-CB scores and 13.4% in CGI-I scores over 12 months. Upon comparison between adjacent visit intervals, significant reductions were observed for both measures between month 3 and month 6. Conclusions This study is the first multicenter investigation to simultaneously evaluate the clinical efficacy and tolerability of transitioning to AOM in the context of polypharmacy. The study suggested that AOM may contribute to reducing the clinical burden, thereby improving the quality of life for patients with schizophrenia.

Objectives: This study aimed to assess the clinical burden, a critical determinant of medication adherence in patients with schizophrenia, after the administration of Aripiprazole once-monthly (AOM). Methods: This study was a retrospective, non-interventional, multicenter, naturalistic observational study conducted through the analysis of participants’ electronic medical records. Study participants were recruited from eight sites. Data were collected at baseline, defined as the time of AOM administration, and at 1, 3, 6, 9, and 12 months thereafter. The primary outcome measure was the change in the Clinical Global Impression-Clinical Benefit (CGI-CB) score over 12 months, and the secondary outcome measure was the change in the Clinical Global Impression-Improvement (CGI-I) score. Results: The data of 139 participants were analyzed, revealing a statistically significant decrease of 26.8% in CGI-CB scores and 13.4% in CGI-I scores over 12 months. Upon comparison between adjacent visit intervals, significant reductions were observed for both measures between month 3 and month 6. Conclusions This study is the first multicenter investigation to simultaneously evaluate the clinical efficacy and tolerability of transitioning to AOM in the context of polypharmacy. The study suggested that AOM may contribute to reducing the clinical burden, thereby improving the quality of life for patients with schizophrenia.

Background/Aims: Synchronous multiple gastric cancer (SMGC) accounts for approximately 6% to 14% of gastric cancer (GC) cases. This study aimed to identify risk factors for SMGC. Methods: A total of 14,603 patients diagnosed with GC were prospectively enrolled. Data including age, sex, body mass index, smoking, alcohol consumption, family history, p53 expression, microsatellite instability, cancer classification, lymph node metastasis, and treatment were collected. Risk factors were analyzed using logistic regression analysis between a single GC and SMGC. Results: The incidence of SMGC was 4.04%, and that of early GC (EGC) and advanced GC (AGC) was 5.43% and 3.11%, respectively. Patients with SMGC were older (65.33 years vs 61.75 years, p<0.001) and more likely to be male. Lymph node metastasis was found in 27% of patients with SMGC and 32% of patients with single GC. Multivariate analysis showed that SMGC was associated with sex (male odds ratio [OR], 1.669; 95% confidence interval [CI], 1.223 to 2.278; p=0.001), age (≥65 years OR, 1.532; 95% CI, 1.169 to 2.008; p=0.002), and EGC (OR, 1.929; 95% CI, 1.432 to 2.600; p<0.001). Survival rates were affected by Lauren classification, sex, tumor size, cancer type, distant metastasis, and venous invasion but were not related to the number of GCs. However, the survival rate of AGC with SMGC was very high. Conclusions SMGC had unique characteristics such as male sex, older age, and EGC, and the survival rate of AGC, in which the intestinal type was much more frequent, was very good (Trial registration number: NCT04973631).

Differentiated thyroid cancer demonstrates a wide range of clinical presentations, from very indolent cases to those with an aggressive prognosis. Therefore, diagnosing and treating each cancer appropriately based on its risk status is important. The Korean Thyroid Association (KTA) has provided and amended the clinical guidelines for thyroid cancer management since 2007. The main changes in this revised 2024 guideline include 1) individualization of surgical extent according to pathological tests and clinical findings, 2) application of active surveillance in low-risk papillary thyroid microcarcinoma, 3) indications for minimally invasive surgery, 4) adoption of World Health Organization pathological diagnostic criteria and definition of terminology in Korean, 5) update on literature evidence of recurrence risk for initial risk stratification, 6) addition of the role of molecular testing, 7) addition of definition of initial risk stratification and targeting thyroid stimulating hormone (TSH) concentrations according to ongoing risk stratification (ORS), 8) addition of treatment of perioperative hypoparathyroidism, 9) update on systemic chemotherapy, and 10) addition of treatment for pediatric patients with thyroid cancer.

Objective: This study was performed to evaluate the efficacy and safety of lurasidone (160 mg/day) compared to quetiapine XR (QXR; 600 mg/day) in the treatment of acutely psychotic patients with schizophrenia. Methods: Patients were randomly assigned to 6 weeks of double-blind treatment with lurasidone 160 mg/day (n=105) or QXR 600 mg/day (n=105). Primary efficacy measure was the change from baseline to week 6 in Positive and Negative Syndrome Scale (PANSS) total score and Clinical Global Impressions severity (CGI-S) score. Adverse events, body measurements, and laboratory parameters were assessed. Results: Lurasidone demonstrated non-inferiority to QXR on the PANSS total score. Adjusted mean±standard error change at week 6 on the PANSS total score was -26.42±2.02 and -27.33±2.01 in the lurasidone and QXR group, respectively. The mean difference score was -0.91 (95% confidence interval -6.35–4.53). The lurasidone group showed a greater reduction in PANSS total and negative subscale on week 1 and a greater reduction in end-point CGI-S score compared to the QXR group. Body weight, body mass index, and waist circumference in the lurasidone group were reduced, with significantly lower mean change compared to QXR. Endpoint changes in glucose, cholesterol, triglycerides, and low-density lipoprotein levels were also significantly lower. The most common adverse drug reactions with lurasidone were akathisia and nausea. Conclusion Lurasidone 160 mg/day was found to be non-inferior to QXR 600 mg/day in the treatment of schizophrenia with comparable efficacy and tolerability. Adverse effects of lurasidone were generally tolerable, and beneficial effects on metabolic parameters can be expected.

Purpose: To develop a deep learning model that can predict the axial lengths of eyes using ultra-widefield (UWF) fundus photography. Methods: We retrospectively enrolled patients who visited the ophthalmology clinic at the Seoul National University Hospital between September 2018 and December 2021. Patients with axial length measurements and UWF images taken within 3 months of axial length measurement were included in the study. The dataset was divided into a development set and a test set at an 8:2 ratio while maintaining an equal distribution of axial lengths (stratified splitting with binning). We used transfer learning-based on EfficientNet B3 to develop the model. We evaluated the model’s performance using mean absolute error (MAE), R-squared (R2), and 95% confidence intervals (CIs). We used vanilla gradient saliency maps to illustrate the regions predominantly used by convolutional neural network. Results: In total, 8,657 UWF retinal fundus images from 3,829 patients (mean age, 63.98 ±15.25 years) were included in the study. The deep learning model predicted the axial lengths of the test dataset with MAE and R2 values of 0.744 mm (95% CI, 0.709–0.779 mm) and 0.815 (95% CI, 0.785–0.840), respectively. The model’s accuracy was 73.7%, 95.9%, and 99.2% in prediction, with error margins of ±1.0, ±2.0, and ±3.0 mm, respectively. Conclusions We developed a deep learning-based model for predicting the axial length from UWF images with good performance.

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R2 and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R2 value was 0.27 and in set II, LightGBM was the best model with the highest R2 value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R2 value of 0.56 and the lowest MSE of 174.07. Conclusion The LightGBM model showed the best performance in predicting postoperative lung function.