Background and Objectives: Studies investigating the correlation between papillary thyroid carcinoma (PTC) tumor size and the prevalence of the B-type Raf kinase (BRAF) mutation have yielded conflicting results. Therefore, we evaluated the prevalence of BRAF mutation according to tumor size in a large cohort of PTC patients to clarify this association. Materials and Methods: We retrospectively analyzed 6,438 patients with surgically diagnosed classic PTC between January 2009 and December 2017 at Samsung Medical Center, Seoul, Republic of Korea.During the study period, BRAF mutation testing was attempted on all fine-needle aspiration specimens, except for a small number of inadequate specimens. All other histologic subtypes were excluded. The prevalence of BRAF mutation was assessed based on tumor size, and further analyzed by age group and sex according to tumor size. Results: The overall prevalence of BRAF mutation was 79.2%. When PTCs ≤1 cm were excluded, the prevalence was 77.2%. The prevalence significantly decreased with increasing tumor size (p for trend ＜0.001). It was significantly higher in men than in women (p=0.013), but did not differ by age. The inverse correlation between tumor size and prevalence was prominent in patients aged 20-49 years but was less distinct in those aged 50 years and older. Conclusion In this large cohort of patients with PTC, the prevalence of BRAF mutation significantly decreased with increasing tumor size. These findings suggest that BRAF mutation is enriched in smaller surgically treated classic PTCs and may provide a hypothesis-generating clue regarding its role in early PTC development, although selection bias cannot be excluded.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Objectives: This study evaluated the effectiveness of rapid diagnostic testing (RDT) for the early detection of imported dengue cases at Gimhae International Airport in the Republic of Korea, and analyzed patient characteristics and response processes following positive results. Methods: From 2022 to 2024, 334 individuals underwent RDT at the airport. Testing was performed for travelers presenting with dengue-like symptoms or recent mosquito bites. Two dengue RDT kits (NS1 and immunoglobulin G/M) were used, and confirmatory tests—including real-time reverse transcription polymerase chain reaction and enzyme-linked immunosorbent assays—were performed for RDT-positive cases. Time intervals between sample collection and diagnostic confirmation were compared by institution type and day of the week. Results: Of the 334 cases tested, 12 yielded positive RDT results, and 3 were confirmed as dengue. No confirmed cases were identified among asymptomatic travelers or those with travel durations shorter than 5 days. All 3 confirmed cases showed moderate or higher RDT intensity. The confirmatory results were negative for all 7 marginally positive cases. The average turnaround time for diagnostic confirmation was 4.00 days in hospitals versus 2.71 days in public health centers. Samples collected on weekdays produced faster results (2.33 days) than those collected across weekends (5.00 days). One individual with a strong RDTpositive result declined confirmatory testing. Conclusion RDT is a valuable tool for detecting dengue at ports of entry. However, timely confirmatory diagnosis requires improved inter-agency coordination and logistical systems, particularly for weekend operations. These findings offer practical insights for strengthening quarantine-based infectious disease control.

Purpose: With the emergence of next-generation therapies, real-world evaluation of current treatment strategies for metastatic hormone-sensitive prostate cancer (mHSPC) is essential. This study aimed to investigate the clinical characteristics, primary treatment patterns, and survival outcomes of Korean patients with mHSPC. Materials and Methods: A nationwide, population-based retrospective cohort study was conducted using National Health Insurance claims data from 2011 to 2020, encompassing all Korean patients diagnosed with prostate cancer (n=217,895). Patient demographics, initial treatment patterns within 3 months of mHSPC diagnosis, time to initiation of metastatic castration-resistant prostate cancer (mCRPC) therapy, and survival outcomes were analyzed. Results: Primary treatment patterns for 10,821 newly diagnosed mHSPC patients were generally similar. Approximately half received first-line androgen deprivation therapy (ADT). Most (68.8%–76.3%) were treated with combination therapy consisting of a luteinizing hormone-releasing hormone (LHRH) agonist and a first-generation antiandrogen, while 5.8%–8.0% received ADT alone. Among 1,418 patients diagnosed in 2015, 20.9% initiated mCRPC therapy at a median of 14.6 months. Median times to initial mCRPC drug use were 14.7 months for those on combination therapy and 19.7 months for those on LHRH agonists alone. Median overall survival (OS) was 61.3 months for all patients, 37.0 months for combination therapy, 51.5 months for LHRH agonists, and 25.5 months for patients with visceral metastases. Conclusion Most patients with newly diagnosed mHSPC received combination therapy with LHRH agonists and first-generation antiandrogens. In this real-world cohort, patients treated with LHRH agonists demonstrated longer OS and delayed initiation of mCRPC-directed therapy compared with those receiving combination therapy.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Nasal dermoids are congenital midline nasal lesions that occur along with encephaloceles and gliomas. They can cause both deformity of nasal structure and intracranial infection as they grow. Treatment for these lesions is be concerned with two aspects, the complete removal of the lesions and making the surgical scar cosmetically acceptable. To that goal, many surgical approaches such as vertical incision, transverse incision, lateral rhinotomy and open rhinoplasty have been introduced. A 12-month male child presented with palpable mass at nasal root. The mass was easily movable, non-compressible and did not present fistula. A well-defined cystic mass without intracranial extension was found on the computerized tomography scans. Open rhinoplasty approach was opted for according to the guardians’ preference to avoid visible facial scar, and the lesions were completely resected. The pathologic examination confirmed the lesion to be nasal dermoids. The columellar scar was negligible and there was no recurrence at 5 year-follow up after surgery.