Objective: This study investigated the associations of changes in metabolic syndrome status and the number of metabolic syndrome risk factors after cancer diagnosis with the incidence of cardiovascular disease (CVD) events among cancer survivors. Methods: Using nationwide health screening data, we identified 344,681 individuals diagnosed with cancer at age ≥19 years from 2012 to 2017, who survived for at least 3 years without CVD events. Participants were classified according to their metabolic syndrome status and risk factor count both before and after cancer diagnosis. A CVD event was defined as a composite of myocardial infarction, stroke, or cardiovascular death. Results: During a median follow-up period of 5.0 years after surviving cancer for 3 years, 7,529 CVD events occurred. The cumulative incidence of CVD was highest among participants with persistent metabolic syndrome. Compared to participants consistently free of metabolic syndrome, the multivariable-adjusted hazard ratios (HRs) for CVD were 1.21 (95% confidence interval [CI], 1.13–1.30) for newly developed metabolic syndrome, 1.19 (95% CI, 1.10–1.27) for recovered metabolic syndrome, and 1.37 (95% CI, 1.30–1.45) for persistent metabolic syndrome. Participants who recovered from metabolic syndrome exhibited a lower risk compared to those with persistent metabolic syndrome (HR, 0.86; 95% CI, 0.80–0.93).Each +1 increase in risk factor count after cancer diagnosis was associated with increased CVD risk (HR, 1.09; 95% CI, 1.07–1.11). Conclusion Changes in metabolic syndrome status and metabolic syndrome risk factor count after cancer diagnosis were significantly associated with CVD risk among cancer survivors.

Lazertinib is an oral, third-generation, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) for the treatment of non-small cell lung cancer (NSCLC). This case report presents a rare instance of small cell carcinoma transformation in pancreatic metastasis in a patient with EGFR-mutated NSCLC undergoing treatment with lazertinib. Small cell carcinoma transformation indicates a mechanism of treatment resistance, and tissue biopsy is essential to confirm this. When isolated progression of a lesion is suspected during TKI therapy in EGFR-mutated NSCLC, histological evaluation is necessary to confirm the transformation for the treatment strategy.

Lazertinib is an oral, third-generation, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) for the treatment of non-small cell lung cancer (NSCLC). This case report presents a rare instance of small cell carcinoma transformation in pancreatic metastasis in a patient with EGFR-mutated NSCLC undergoing treatment with lazertinib. Small cell carcinoma transformation indicates a mechanism of treatment resistance, and tissue biopsy is essential to confirm this. When isolated progression of a lesion is suspected during TKI therapy in EGFR-mutated NSCLC, histological evaluation is necessary to confirm the transformation for the treatment strategy.

Lazertinib is an oral, third-generation, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) for the treatment of non-small cell lung cancer (NSCLC). This case report presents a rare instance of small cell carcinoma transformation in pancreatic metastasis in a patient with EGFR-mutated NSCLC undergoing treatment with lazertinib. Small cell carcinoma transformation indicates a mechanism of treatment resistance, and tissue biopsy is essential to confirm this. When isolated progression of a lesion is suspected during TKI therapy in EGFR-mutated NSCLC, histological evaluation is necessary to confirm the transformation for the treatment strategy.

Lazertinib is an oral, third-generation, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) for the treatment of non-small cell lung cancer (NSCLC). This case report presents a rare instance of small cell carcinoma transformation in pancreatic metastasis in a patient with EGFR-mutated NSCLC undergoing treatment with lazertinib. Small cell carcinoma transformation indicates a mechanism of treatment resistance, and tissue biopsy is essential to confirm this. When isolated progression of a lesion is suspected during TKI therapy in EGFR-mutated NSCLC, histological evaluation is necessary to confirm the transformation for the treatment strategy.

Lazertinib is an oral, third-generation, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) for the treatment of non-small cell lung cancer (NSCLC). This case report presents a rare instance of small cell carcinoma transformation in pancreatic metastasis in a patient with EGFR-mutated NSCLC undergoing treatment with lazertinib. Small cell carcinoma transformation indicates a mechanism of treatment resistance, and tissue biopsy is essential to confirm this. When isolated progression of a lesion is suspected during TKI therapy in EGFR-mutated NSCLC, histological evaluation is necessary to confirm the transformation for the treatment strategy.

Immunoglobulin A nephropathy (IgAN) is a major cause of end-stage kidney disease (ESKD). The International IgA Nephropathy Prediction Tool (IIgAN-PT) predicts IgAN prognosis, but improvement in the prediction performance using machine learning (ML)-based methods is needed. Methods: We analyzed 4,425 biopsy-confirmed patients with IgAN and ≥6 months of follow-up from nine tertiary university hospitals in Korea. The study population was divided into development and validation cohorts. Using the collected 87 clinicodemographic and pathological variables, ML-based prediction models for ESKD or estimated glomerular filtration rate decline (50% reduction or <15 mL/min/1.73 m2 ) were constructed: 1) the conventional CatBoost model, 2) the optimized CatBoost model with Cox proportional hazards, 3) the deep Cox proportional hazards model, and 4) the deep Cox mixture model. The area under the curve (AUC) and calibration plots were used to investigate the discriminative and calibration performance of the models, which were then compared with those of the IIgAN-PT full model. Results: The full model showed excellent performance (AUC [95% confidence interval] for 5-year outcome, 0.896 [0.853–0.940]), with acceptable calibration results. The ML-based models showed good performance in predicting adverse kidney outcomes and revealed acceptable discrimination performance in the external validation (AUC [95% confidence interval] for the 5-year outcome: 1) 0.829 [0.791–0.866]; 2) 0.847 [0.804–0.890]; 3) 0.823 [0.784–0.862]; and 4) 0.832 [0.794–0.870]), although the models showed underestimation in calibration analysis of the external validation cohort. With the validation data, the overall performance of the IIgAN-PT was non-inferior to that of the ML-based model. Conclusions: Our ML-based models showed good performance in predicting adverse kidney outcomes in patients with IgAN but they did not outperform the IIgAN-PT.

Purpose: The purpose of this study was to investigate the accuracy of image merging of an intraoral scan of an edentulous arch to a facial scan using wax rim and markers. . Materials and Methods: For registration of oral scan to face scan, a wax rim with markers wasmade. The markers were cuboid and divided into four groups according to size (5, 10 mm) and attachment location (midline, canine region). The evaluation of registration accuracy was compared with a standard created using cone-beam computed tomographydata. Anterior linear variation of the edentulous arch and the 3D variation of the overall arch were measured. Kruskal-Wallis test and Mann-Whitney U test were used for statistics, and the significance level was set at 0.05 and evaluated under Bonferroni correction (0.05/6 = 0.083). Results: In the anterior deviation and global deviation results, there was no statistically significant difference in the oral scan position displacement values between the 5 mm and 10 mm groups. When the midline marker was used, the intraoral scan position displacement value was significantly lower than when only the canine marker was used. Conclusion Marker attached wax rim can be used for image matching between facial and intraoral scans of the edentulous arch. Marker location at the middle area increases the accuracy of image matching.

Purpose: The purpose of this study was to investigate the accuracy of image merging of an intraoral scan of an edentulous arch to a facial scan using wax rim and markers. . Materials and Methods: For registration of oral scan to face scan, a wax rim with markers wasmade. The markers were cuboid and divided into four groups according to size (5, 10 mm) and attachment location (midline, canine region). The evaluation of registration accuracy was compared with a standard created using cone-beam computed tomographydata. Anterior linear variation of the edentulous arch and the 3D variation of the overall arch were measured. Kruskal-Wallis test and Mann-Whitney U test were used for statistics, and the significance level was set at 0.05 and evaluated under Bonferroni correction (0.05/6 = 0.083). Results: In the anterior deviation and global deviation results, there was no statistically significant difference in the oral scan position displacement values between the 5 mm and 10 mm groups. When the midline marker was used, the intraoral scan position displacement value was significantly lower than when only the canine marker was used. Conclusion Marker attached wax rim can be used for image matching between facial and intraoral scans of the edentulous arch. Marker location at the middle area increases the accuracy of image matching.

OBJECTIVES: The escalating burden of cardiovascular disease (CVD) is a critical public health issue worldwide. CVD, especially acute myocardial infarction (AMI) and stroke, is the leading contributor to morbidity and mortality in Korea. We aimed to develop algorithms for identifying AMI and stroke events from the National Health Insurance Service (NHIS) database and validate these algorithms through medical record review. METHODS: We first established a concept and definition of “hospitalization episode,” taking into account the unique features of health claims-based NHIS database. We then developed first and recurrent event identification algorithms, separately for AMI and stroke, to determine whether each hospitalization episode represents a true incident case of AMI or stroke. Finally, we assessed our algorithms’ accuracy by calculating their positive predictive values (PPVs) based on medical records of algorithm- identified events. RESULTS: We developed identification algorithms for both AMI and stroke. To validate them, we conducted retrospective review of medical records for 3,140 algorithm-identified events (1,399 AMI and 1,741 stroke events) across 24 hospitals throughout Korea. The overall PPVs for the first and recurrent AMI events were around 92% and 78%, respectively, while those for the first and recurrent stroke events were around 88% and 81%, respectively. CONCLUSIONS We successfully developed algorithms for identifying AMI and stroke events. The algorithms demonstrated high accuracy, with PPVs of approximately 90% for first events and 80% for recurrent events. These findings indicate that our algorithms hold promise as an instrumental tool for the consistent and reliable production of national CVD statistics in Korea.