Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Background: Epidermal growth factor receptor (EGFR) gene mutation testing is crucial for the administration of tyrosine kinase inhibitors to treat non–small cell lung cancer. In addition to traditional tissue-based tests, liquid biopsies using plasma are increasingly utilized, particularly for detecting T790M mutations. This study compared tissue- and plasma-based EGFR testing methods. Methods: A total of 248 patients were tested for EGFR mutations using tissue and plasma samples from 2018 to 2023 at Pusan National University Yangsan Hospital. Tissue tests were performed using PANAmutyper, and plasma tests were performed using the Cobas EGFR Mutation Test v2. Results: All 248 patients underwent tissue-based EGFR testing, and 245 (98.8%) showed positive results. Of the 408 plasma tests, 237 (58.1%) were positive. For the T790M mutation, tissue biopsies were performed 87 times in 69 patients, and 30 positive cases (38.6%) were detected. Plasma testing for the T790M mutation was conducted 333 times in 207 patients, yielding 62 positive results (18.6%). Of these, 57 (27.5%) were confirmed to have the mutation via plasma testing. Combined tissue and plasma tests for the T790M mutation were positive in nine patients (13.4%), while 17 (25.4%) were positive in tissue only and 12 (17.9%) in plasma only. This mutation was not detected in 28 patients (43.3%). Conclusions Although the tissue- and plasma-based tests showed a sensitivity of 37.3% and 32.8%, respectively, combined testing increased the detection rate to 56.7%. Thus, neither test demonstrated superiority, rather, they were complementary.

Background: Cardiovascular diseases are the leading cause of mortality after liver transplantation (LT). Although the impact of secondary tricuspid regurgitation (TR) with severe pulmonary hypertension (PH) is well investigated, the impact of primary TR with tricuspid valve incompetence (TVI) on LT outcomes remains unclear. We aimed to investigate the prevalence and impact of primary TR with TVI on LT outcomes in a large-volume LT center. Methods: We retrospectively examined 5 512 consecutive LT recipients who underwent routine pretransplant echocardiography between 2008 and 2020. Patients were categorized based on the presence of anatomical TVI, specifically defined by incomplete coaptation, coaptation failure, prolapse, and flail leaflets of tricuspid valve (TV). Propensity score (PS)-based inverse probability weighting (IPW) was used to balance clinical and cardiovascular risk variables. The outcomes were one-year cumulative all-cause mortality and 30-day major adverse cardiovascular events (MACE). Results: Anatomical TVI was identified in 14 patients (0.3%). Although rare, these patients exhibited significantly lower post-LT one-year survival rates (64.3% vs. 91.5%, P < 0.001) and higher 30-day MACE rates (42.9% vs. 16.9%, P = 0.026) than patients without TVI. They also had worse survival irrespective of echocardiographic evidence of PH (P < 0.001) and exhibited higher one-year mortality (IPW-adjusted hazard ratio: 4.09, P = 0.002) and increased 30-day MACE rates (IPW-adjusted odds ratio: 1.24, P = 0.048). Conclusions Primary TR with anatomical TVI was associated with significantly reduced one-year survival and increased post-LT MACE rates. These patients should be prioritized similarly to those with secondary TR with severe PH, with appropriate pretransplant evaluations and treatments to improve survival outcomes.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Atopic dermatitis is the most common chronic inflammatory skin disease in children and adolescents. The Korean Academy of Pediatric Allergy and Respiratory Disease published the Atopic Dermatitis Treatment Guideline in 2008, which has been helpful in atopic dermatitis treatment until now. Various reports on the development and effectiveness of new drugs have suggested that there is a need to develop and revise old treatment guidelines. Part 1 aimed to provide evidence-based recommendations for skin care management and topical treatment for atopic dermatitis. Part 2 focuses on systemic treatment, novel therapeutics, and adjuvant therapy. The goal of this guideline is intended to assist front-line doctors treating pediatric and adolescent atopic dermatitis patients make safer, more effective, and more rational decisions regarding systemic treatment, novel therapeutics, and adjuvant therapy by providing evidence-based recommendations with a clear level of evidence and benefit regarding treatment.

Atopic dermatitis is the most common chronic inflammatory skin disease in children and adolescents. The Korean Academy of Pediatric Allergy and Respiratory Disease published the Atopic Dermatitis Treatment Guideline in 2008, which has been helpful in atopic dermatitis treatment until now. Various reports on the development and effectiveness of new drugs have suggested that there is a need to develop and revise old treatment guidelines. Part 1 aimed to provide evidence-based recommendations for skin care management and topical treatment for atopic dermatitis. Part 2 focuses on systemic treatment, novel therapeutics, and adjuvant therapy. The goal of this guideline is intended to assist front-line doctors treating pediatric and adolescent atopic dermatitis patients make safer, more effective, and more rational decisions regarding systemic treatment, novel therapeutics, and adjuvant therapy by providing evidence-based recommendations with a clear level of evidence and benefit regarding treatment.

Atopic dermatitis is the most common chronic inflammatory skin disease in children and adolescents. The Korean Academy of Pediatric Allergy and Respiratory Disease published the Atopic Dermatitis Treatment Guideline in 2008, which has been helpful in atopic dermatitis treatment until now. Various reports on the development and effectiveness of new drugs have suggested that there is a need to develop and revise old treatment guidelines. Part 1 aimed to provide evidence-based recommendations for skin care management and topical treatment for atopic dermatitis. Part 2 focuses on systemic treatment, novel therapeutics, and adjuvant therapy. The goal of this guideline is intended to assist front-line doctors treating pediatric and adolescent atopic dermatitis patients make safer, more effective, and more rational decisions regarding systemic treatment, novel therapeutics, and adjuvant therapy by providing evidence-based recommendations with a clear level of evidence and benefit regarding treatment.

Purpose: Recent development in perioperative treatment of resectable non–small cell lung cancer (NSCLC) have changed the landscape of early lung cancer management. The ADAURA trial has demonstrated the efficacy of adjuvant osimertinib treatment in resectable NSCLC patients; however, studies are required to show which subgroup of patients are at a high risk of relapse and require adjuvant epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor treatment. This study evaluated risk factors for postoperative relapse among patients who underwent complete resection. Materials and Methods: Data were obtained from the Korean Association for Lung Cancer Registry (KALC-R), a database created using a retrospective sampling survey by the Korean Central Cancer Registry (KCCR) and the Lung Cancer Registration Committee. Results: A total of 3,176 patients who underwent curative resection was evaluated. The mean observation time was approximately 35.4 months. Among stage I to IIIA NSCLC patients, the EGFR-mutant subgroup included 867 patients, and 75.2%, 11.2%, and 11.8% were classified as stage I, stage II, and stage III, respectively. Within the EGFR-mutant subgroup, 44 (5.1%) and 121 (14.0%) patients showed early and late recurrence, respectively. Multivariate analysis on association with postoperative relapse among the EGFR-mutant subgroup showed that age, pathologic N and TNM stages, pleural invasion status, and surgery type were independent significant factors. Conclusion Among the population that underwent complete resection for early NSCLC with EGFR mutation, patients with advanced stage, pleural invasion, or limited resection are more likely to show postoperative relapse.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.