Lipogranuloma is a chronic inflammatory response to lipid material within tissues and may develop secondary to fat necrosis, trauma, or exogenous lipid exposure. We report a case of postoperative mid-sternal lipogranuloma with milky discharge in a 35-year-old man that occurred 6 months after surgery for gynecomastia performed through a periareolar incision with adjunctive liposuction. The patient presented with two firm, non-tender nodules at the sternal midline, distant from the operative site, and laboratory findings were unremarkable. Surgical exploration revealed milky white discharge, and histopathologic examination demonstrated lipid-laden macrophages, multinucleated giant cells, and fibrosis, consistent with lipogranulomatous inflammation. No evidence of malignancy or infection was identified, and no recurrence was observed during 2 years of follow-up. The proposed mechanisms include displacement of residual ductal epithelium or lipid material during surgical manipulation, as well as postoperative fat necrosis leading to secondary lipogranulomatous inflammation. This case highlights that postoperative lipogranulomatous inflammation may present at sites distant from the primary operative field. Recognition of this entity is important in the evaluation of postoperative chest wall masses to avoid misdiagnosis and unnecessary intervention.

Epidermoid and dermoid cysts are common congenital lesions of the pediatric periorbital region arising from ectodermal inclusion during embryologic development. Although these lesions originate in the soft tissue, chronic pressure from long-standing masses may induce cortical depression or remodeling of adjacent craniofacial bones, particularly in young children with malleable skeletal structures. We report a case of a 26-month-old girl presenting with a small periorbital epidermoid cyst associated with pressure-induced depression of the zygomatic surface. Radiologic evaluation revealed a well-circumscribed cystic lesion abutting the frontal process of the zygoma with smooth cortical indentation and no features suggestive of aggressive osseous pathology. Complete excision was performed through a lateral brow approach with preservation of the periosteum. Histopathologic examination confirmed an epidermoid cyst. Postoperative follow-up demonstrated progressive spontaneous restoration of the zygomatic contour without the need for bone grafting or alloplastic materials, reflecting the osteogenic potential of the pediatric periosteum. This case emphasizes the importance of distinguishing secondary pressure-related bone remodeling from true intraosseous lesions in the pediatric craniofacial region and highlights the capacity for natural osseous recovery following removal of the compressive source. A brief review of relevant embryologic and radiologic considerations is also provided.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated. This study involves 30 centers across Korea. The study aims to improve the long-term prognosis of Korean patients with rare endocrine diseases by collecting comprehensive clinical data, biospecimens, and patient-reported outcomes to identify complications and unmet needs in patient care. Patients with childhood-onset pituitary, adrenal, or gonadal disorders, such as craniopharyngioma, congenital adrenal hyperplasia (CAH), and Turner syndrome were prioritized. The planned enrollment is 1,300 patients during the first study phase (2022–2024). Clinical, biochemical, and imaging data from diagnosis, treatment, and follow-up during 1980–2023 were retrospectively reviewed. For patients who agreed to participate in the prospective cohort, clinical data and biospecimens will be prospectively collected to discover ideal biomarkers that predict the effectiveness of disease control measures and prognosis. Patient-reported outcomes, including quality of life and depression scales, will be evaluated to assess psychosocial outcomes. Additionally, a substudy on CAH patients will develop a steroid hormone profiling method using liquid chromatography-tandem mass spectrometry to improve diagnosis and monitoring of treatment outcomes. This study will address unmet clinical needs by discovering ideal biomarkers, introducing evidence-based treatment guidelines, and ultimately improving long-term outcomes in the areas of rare endocrine and metabolic diseases.

Low-dose radiotherapy (LDRT) has been explored as a treatment option for various inflammatory diseases; however, its application in the context of rheumatoid arthritis (RA) is lacking. This study aimed to elucidate the mechanism underlying LDRT-based treatment for RA and standardize it. LDRT reduced the total numbers of immune cells, but increased the apoptotic CD4+ T and B220+ B cells, in the draining lymph nodes of collagen induced arthritis and K/BxN models. In addition, it significantly reduced the severity of various pathological manifestations, including bone destruction, cartilage erosion, and swelling of hind limb ankle. Post-LDRT, the proportion of apoptotic CD4+ T and CD19 + B cells increased significantly in the PBMCs derived from human patients with RA. LDRT showed a similar effect in fibroblast-like synoviocytes as well. In conclusion, we report that LDRT induces apoptosis in immune cells and fibro-blast-like synoviocytes, contributing to attenuation of arthritis.

Background: The optimal duration and net clinical benefit of dual antiplatelet therapy (DAPT) after transcatheter aortic valve replacement (TAVR) have not been elucidated in realworld situations. Methods: Using nationwide claims data from 2013 to 2021, we selected patients who underwent TAVR and categorized them into two groups: short- and long-term (≤ 3 and > 3 months, respectively) DAPT group. Propensity score matching was used to balance baseline characteristics. The primary endpoint was the occurrence of net adverse clinical events (NACEs), including all-cause death, myocardial infarction, stroke, any coronary and peripheral revascularization, systemic thromboembolism, and bleeding events, at 1 year. Survival analyses were conducted using Kaplan-Meier estimation and Cox proportional hazards regression. Results: Patients who met the inclusion criteria (1,695) were selected. Propensity score matching yielded 1,215 pairs of patients: 416 and 799 in the short- and long-term DAPT groups, respectively. In the unmatched cohort, the mean ages were 79.8 ± 6.1 and 79.7 ± 5.8 years for the short- and long-term DAPT groups, respectively. In the matched cohort, the mean ages were 80.6 ± 5.9 and 79.9 ± 5.9 years for the short- and long-term DAPT groups, respectively. Over one year in the unmatched cohort, the NACE incidence was 11.9% and 11.5% in the short- and long-term DAPT groups, respectively (P = 0.893). The all-cause mortality rates were 7.4% and 4.7% (P = 0.042), composite ischemic event rates were 2.5% and 4.7% (P = 0.056), and bleeding event rates were 2.7% and 4.7% (P = 0.056) in the shortand long-term groups, respectively. In the matched cohort, the incidence of NACE was 9.6% in the short-term DAPT group and 11.6% in the long-term DAPT group, respectively (P = 0.329).The all-cause mortality rates were 6.5% and 4.9% (P = 0.298), composite ischemic event rates were 1.4% and 4.5% (P = 0.009), and bleeding event rates were 2.2% and 4.4% (P = 0.072) in the short- and long-term groups, respectively. Conclusion In patients who successfully underwent transfemoral TAVR, the short- and longterm DAPT groups exhibited similar one-year NACE rates. However, patients in the long-term DAPT group experienced more bleeding and ischemic events.