Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Endometriosis, a prevalent but debilitating condition affecting women, poses significant challenges in diagnosis and management. The current 2024 guideline, developed by the Korean Society of Endometriosis (KSE), builds upon the 2018 KSE guideline. This guideline aims to provide customized recommendations tailored to Korea’s unique clinical aspects and medical environment, and addresses key areas such as diagnosis, medical and surgical management, considerations for special populations, and its complex relationship with cancer.

Nephrogenic diabetes insipidus (NDI) is characterized by polyuria and polydipsia, high serum osmolality, and low urine osmolality because of resistance to antidiuretic hormone. Lithium is commonly used to treat psychiatric disorders, and NDI is one of the common renal side effects of lithium therapy. On the other hand, NDI induced by lithium medication in a patient after a coronavirus disease-19 (COVID-19) infection has not been reported. This paper presents a patient who developed new-onset NDI secondary to a COVID-19 infection despite being on a stable lithium dose for several years. This case highlights the importance of considering a COVID-19 infection as a possible cause of NDI in patients taking lithium medication.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Objective: Extraction of the mandibular second molar (L7) and substitution by the mandibular third molar (L8) is an effective treatment option. This study aimed to evaluate spontaneously erupted L8 occlusion after L7 extraction, and identify the influencing factors. Methods: This study assessed 46 L8 from 28 patients using dental study models, panoramic radiographs, and lateral cephalograms obtained during L7 extraction (T1) and completion of L8 eruption (T2). At T2, samples were categorized as acceptable (A-group) or unacceptable (U-group) based on the American Board of Orthodontics index.L8 angulation and position, retromolar space, distance between the Xi point and mandibular first molar (L6), and Nolla stage were compared between the groups to identify the predictive factors for successful eruption. Results: At T2, 58.7% of L8 exhibited acceptable occlusion. Age at T1 was significantly higher in the U-group than that in the A-group. Angles ∠6-MnP and ∠8-MnP differed significantly between the groups at T2. Xi-L6 distance was considerably longer in the A-group than that in the U-group at T1 and T2. Younger age at extraction and Xi-L6 distance at T1 affected the acceptable occlusion. Conclusions Younger age at L7 extraction and adequate eruption space (Xi-L6 distance) appear to be the key factors for achieving acceptable L8 occlusion.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background: s/Aims: Microwave ablation (MWA) is an emerging ablative therapy that surpasses previous methods by achieving higher temperatures and creating larger ablation zones within shorter periods. This study compared the therapeutic outcomes of MWA with those of liver resection in real-world clinical practice. Methods: A total of 178 patients with 259 nodules who underwent MWA or liver resection between January 2015 and July 2023 were enrolled. Local tumor progression (LTP)-free survival, overall progression (OP)-free survival, and overall survival (OS) were assessed based on the treatment modality for the index nodule. Results: Of the 178 patients, 134 with 214 nodules underwent MWA, and 44 with 45 nodules underwent liver resection. The median follow-up period was 2.0±1.5 years. The annual incidence of LTP was 3.7% for MWA and 1.4% for liver resection. Treatment modality did not significantly affect LTP-free survival (hazard ratio, 0.61; 95% confidence interval, 0.14-2.69; P=0.511). For nodules larger than 3 cm, LTP-free survival was not affected by the treatment modality. Similarly, OP-free survival and OS were not influenced by treatment modality. Conclusions MWA and liver resection demonstrated comparable treatment outcomes in terms of local tumor control, overall recurrence, and survival. MWA may be an alternative treatment option for select patients; however, further studies are necessary to generalize these findings.

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.

The detrimental effects of hyperlipidemia on the healing of rotator cuff tears are well documented. The proposed underlying mechanisms for these effects include alterations in the extracellular matrix, inflammation, and oxidative stress, which hamper the reparative processes in the affected tendon tissues. Recent therapeutic strategies target these pathways, reflecting a growing body of research dedicated to mitigating these effects and promoting healing. This literature review aims to provide a comprehensive understanding of the pathophysiology underlying rotator cuff tears, examine the interplay between hyperlipidemia and rotator cuff tear healing, synthesize current knowledge on contributing biological mechanisms, and outline potential therapeutic interventions to optimize clinical management and treatment outcomes for patients.