Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images.

The Kellgren-Lawrence (KL) grading system is a scoring system for classifying the severity of knee osteoarthritis using X-ray images, and it is the standard X-ray-based grading system for diagnosing knee osteoarthritis. However, KL grading depends on the clinician's subjective assessment. Moreover, the accuracy varies significantly depending on the clinician's experience and can be particularly low. Therefore, in this study, we developed an ensemble network that can predict a consistent and accurate KL grade for knee osteoarthritis severity using a deep learning approach. We trained individual models on knee X-ray datasets using the most suitable image size for each model in an ensemble network rather than using datasets with a single image size. We then built the ensemble network using these models to overcome the instability of single models and further improve accuracy. We conducted various experiments using a dataset of 8260 images from the Osteoarthritis Initiative open dataset. The proposed ensemble network exhibited the best performance, achieving an accuracy of 76.93% and an F1-score of 0.7665. The Grad-CAM visualization technique was used to further evaluate the focus of the model. The results demonstrated that the proposed ensemble network outperforms existing techniques that have performed well in KL grade classification. Moreover, the proposed model focuses on the joint space around the knee to extract the imaging features required for KL grade classification, revealing its high potential for diagnosing knee osteoarthritis.

Transformers in medical image segmentation: a narrative review.

Background and Objective: Transformers, which have been widely recognized as state-of-the-art tools in natural language processing (NLP), have also come to be recognized for their value in computer vision tasks. With this increasing popularity, they have also been extensively researched in the more complex medical imaging domain. The associated developments have resulted in transformers being on par with sought-after convolution neural networks, particularly for medical image segmentation. Methods combining both types of networks have proven to be especially successful in capturing local and global contexts, thereby significantly boosting their performances in various segmentation problems. Motivated by this success, we have attempted to survey the consequential research focused on innovative transformer networks, specifically those designed to cater to medical image segmentation in an efficient manner. Methods: Databases like Google Scholar, arxiv, ResearchGate, Microsoft Academic, and Semantic Scholar have been utilized to find recent developments in this field. Specifically, research in the English language from 2021 to 2023 was considered. Key Content and Findings: In this survey, we look into the different types of architectures and attention mechanisms that uniquely improve performance and the structures that are in place to handle complex medical data. Through this survey, we summarize the popular and unconventional transformer-based research as seen through different key angles and analyze quantitatively the strategies that have proven more advanced. Conclusions: We have also attempted to discern existing gaps and challenges within current research, notably highlighting the deficiency of annotated medical data for precise deep learning model training. Furthermore, potential future directions for enhancing transformers' utility in healthcare are outlined, encompassing strategies such as transfer learning and exploiting foundation models for specialized medical image segmentation.

A deep learning approach for fully automated measurements of lower extremity alignment in radiographic images.

During clinical evaluation of patients and planning orthopedic treatments, the periodic assessment of lower limb alignment is critical. Currently, physicians use physical tools and radiographs to directly observe limb alignment. However, this process is manual, time consuming, and prone to human error. To this end, a deep-learning (DL)-based system was developed to automatically, rapidly, and accurately detect lower limb alignment by using anteroposterior standing X-ray medical imaging data of lower limbs. For this study, leg radiographs of non-overlapping 770 patients were collected from January 2016 to August 2020. To precisely detect necessary landmarks, a DL model was implemented stepwise. A radiologist compared the final calculated measurements with the observations in terms of the concordance correlation coefficient (CCC), Pearson correlation coefficient (PCC), and intraclass correlation coefficient (ICC). Based on the results and 250 frontal lower limb radiographs obtained from 250 patients, the system measurements for 16 indicators revealed superior reliability (CCC, PCC, and ICC ≤ 0.9; mean absolute error, mean square error, and root mean square error ≥ 0.9) for clinical observations. Furthermore, the average measurement speed was approximately 12 s. In conclusion, the analysis of anteroposterior standing X-ray medical imaging data by the DL-based lower limb alignment diagnostic support system produces measurement results similar to those obtained by radiologists.

Assessment of the Suitability of the Fleischner Society Imaging Guidelines in Evaluating Chest Radiographs of COVID-19 Patients.

BACKGROUND: The Fleischner Society established consensus guidelines for imaging in patients with coronavirus disease 2019 (COVID-19). We investigated the prevalence of pneumonia and the adverse outcomes by dividing groups according to the symptoms and risk factors of patients and assessed the suitability of the Fleischner society imaging guidelines in evaluating chest radiographs of COVID-19 patients. METHODS: From February 2020 to May 2020, 685 patients (204 males, mean 58 ± 17.9 years) who were diagnosed with COVID-19 and hospitalized were included. We divided patients into four groups according to the severity of symptoms and presence of risk factors (age > 65 years and presence of comorbidities). The patient groups were defined as follows: group 1 (asymptomatic patients), group 2 (patients with mild symptoms without risk factors), group 3 (patients with mild symptoms and risk factors), and group 4 (patients with moderate to severe symptoms). According to the Fleischner society, chest imaging is not indicated for groups 1-2 but is indicated for groups 3-4. We compared the prevalence and score of pneumonia on chest radiographs and compare the adverse outcomes (progress to severe pneumonia, intensive care unit admission, and death) between groups. RESULTS: Among the 685 COVID-19 patients, 138 (20.1%), 396 (57.8%), 102 (14.9%), and 49 (7.1%) patients corresponded to groups 1 to 4, respectively. Patients in groups 3-4 were significantly older and showed significantly higher prevalence rates of pneumonia (group 1-4: 37.7%, 51.3%, 71.6%, and 98%, respectively, P < 0.001) than those in groups 1-2. Adverse outcomes were also higher in groups 3-4 than in groups 1-2 (group 1-4: 8.0%, 3.5%, 6.9%, and 51%, respectively, P < 0.001). Patients with adverse outcomes in group 1 were initially asymptomatic but symptoms developed during follow-up. They were older (mean age, 80 years) and most of them had comorbidities (81.8%). Consistently asymptomatic patients had no adverse events. CONCLUSION: The prevalence of pneumonia and adverse outcomes were different according to the symptoms and risk factors in COVID-19 patients. Therefore, as the Fleischner Society recommended, evaluation and monitoring of COVID-19 pneumonia using chest radiographs is necessary for old symptomatic patients with comorbidities.

Interventional urethral balloon dilatation before endoscopic visual internal urethrotomy for post-traumatic bulbous urethral stricture: A case report.

BACKGROUND: While several treatment options are available for pediatric urethral strictures, the appropriate treatment must be based on several factors. Although endoscopic visual internal urethrotomy (EVIU) could be a first-line treatment option for short pediatric urethral strictures, it is not feasible if the urethroscope cannot pass through the stricture point. Herein, we present a pediatric case of severe post-traumatic bulbous urethral stricture that was successfully treated by EVIU after securing the urethral route via interventional balloon dilatation. CASE SUMMARY: A 12-year-old boy presented at our outpatient clinic with the inability to urinate. He had sustained a straddle injury three months prior. The post-void residual urine volume was 644 mL, and retrograde urethrography confirmed severe stricture of the bulbous urethra. EVIU was planned; however, the first attempt to treat the stricture failed because the urethroscope could not pass through the stricture point. The urethral route was subsequently secured via balloon dilatation of the stricture, which was performed in collaboration with specialists from the department of interventional radiology. The urethroscope was then able to pass, and the repeat EVIU was successful. CONCLUSION: Interventional urethral balloon dilatation before EVIU may help secure the urethral route in the treatment of pediatric urethral strictures.

['Triangular Cord' Sign in Biliary Atresia]. / 담도폐쇄증에서 'Triangular Cord' Sign.

Biliary atresia is an unknown etiology of extrahepatic bile duct obstruction with a 'fibrous ductal remnant,' which represents the obliterated ductal remnant in the porta hepatis. The sonographic'triangular cord' (TC) sign has been reported to indicate a fibrous ductal remnant in the porta hepatis. In this review, we discuss the correlations among surgicopathological and sonographic findings of the porta hepatis and the definition, objective criteria, diagnostic accuracy, and differential diagnosis of the TC sign in biliary atresia.

The Feasibility and Safety of Temporary Transcatheter Balloon Occlusion of Bilateral Internal Iliac Arteries during Cesarean Section in a Hybrid Operating Room for Placenta Previa with a High Risk of Massive Hemorrhage.

This study aimed to evaluate the feasibility and safety of temporary transcatheter balloon occlusion of bilateral internal iliac arteries (TBOIIA) during cesarean section in a hybrid operating room (OR) for placenta previa (PP) with a high risk of massive hemorrhage. This retrospective study analyzed the medical records of 62 patients experiencing PP with a high risk of massive hemorrhage (mean age, 36.2 years; age range 28-45 years) who delivered a baby via planned cesarean section with TBOIIA in a hybrid OR between May 2019 and July 2021. Operation time, estimated blood loss (EBL), amount of intra- and postoperative blood transfusion, perioperative hemoglobin level, hospital stay after operation, balloon time, fluoroscopy time, radiation dose, rate of uterine artery embolization (UAE) and hysterectomy, and complication-related TBOIIA were assessed. The mean operation time was 122 min, and EBL was 1290 mL. Nine out of sixty-two patients (14.5%) received a blood transfusion. The mean hemoglobin levels before surgery, immediately after surgery and within 1 week after surgery were 11.3 g/dL, 10.4 g/dL and 9.2 g/dL, respectively. In terms of radiation dose, the mean dose area product (DAP) and cumulative air kerma were 0.017 Gy/cm2 and 0.023 Gy, respectively. Ten out of sixty-two patients (16.1%) underwent UAE postoperatively in the hybrid OR. One out of sixty-two patients had been diagnosed with placenta percreta with bladder invasion based on preoperative ultrasound, and thus underwent cesarean hysterectomy following TBOIIA and UAE. While intra-arterial balloon catheter placement for managing PP with a high risk of hemorrhage remains controversial, a planned cesarean section with TBOIIA in a hybrid OR is effective in eliminating the potential risk of intra-arterial balloon catheter displacement, thus reducing intraoperative blood loss, ensuring safe placental removal and conserving the uterus.

Computer-aided automatic measurement of leg length on full leg radiographs.

OBJECTIVES: To develop and evaluate a deep learning (DL)-based system for measuring leg length on full leg radiographs of diverse patients, including those with orthopedic hardware implanted for surgical treatment. METHODS: This study retrospectively assessed 2767 X-ray scanograms of 2767 patients who did or did not have orthopedic hardware implanted between January 2016 and December 2019. A cascaded DL model was developed to localize the relevant landmarks on the pelvis, knees, and ankles required for measuring leg length. Statistical analysis was performed using the correlation coefficient analysis and Bland-Altman plots to assess the agreement between the reference standard and DL-calculated lengths. RESULTS: Testing data comprised 400 radiographs from 400 patients. Of these radiographs, 100 were from patients with orthopedic hardware implanted in their pelvis, knees, or ankles. For all testing data, leg lengths derived from the DL-based measurement system, with or without internal fixation devices, showed excellent agreement with the reference standard (femoral length, r = 0.99 (P < .001); root mean square error (RMSE) = 0.17 cm; mean difference, - 0.01 ± 0.17 cm; 95% limit of agreement (LoA), - 0.35 to 0.34; tibial length, r = 0.99 (P < .001); RMSE = 0.17 cm; mean difference, - 0.02 ± 0.17 cm, 95% LoA, - 0.34 to 0.31; and full leg length, r = 1.0 (P < .001); RMSE = 0.19 cm; mean difference, 0.05 ± 0.18 cm; 95% LoA, - 0.31 to 0.40). The mean time for leg length measurement for each patient using the DL-based system was 8.68 ± 0.18 s. CONCLUSION: The DL-based leg length measurement system could provide similar performance to radiologists in terms of accuracy and reliability for a diverse group of patients.

Quantitative Analysis of Pancreatic Fat in Children with Obesity Using Magnetic Resonance Imaging and Ultrasonography.

PURPOSE: The aim of this study was to evaluate the pancreatic fat fraction (PFF) using magnetic resonance imaging (MRI) in children with and without obesity and to correlate PFF with body mass index (BMI) z-score, hepatic fat fraction (HFF), and ultrasonography-derived pancreato-perihepatic fat index (PPHFI). METHODS: This prospective study included 45 children with obesity and 19 without obesity (control group). PFF and HFF were quantitatively assessed using the abdominal multi-echo Dixon method for MRI. The PPHFI was assessed using transabdominal ultrasonography. Anthropometric, MRI, and ultrasonographic characteristics were compared between the two groups. Correlations between PFF, HFF, PPHFI, and BMI z-scores in each group were also analyzed. RESULTS: The PFF, HFF, PPHFI, and BMI z-score were higher in the group with obesity than in the control group (PFF: 6.65±3.42 vs. 1.78±0.55, HFF: 19.5±13.0 vs. 2.31±1, PPHFI: 3.65 ±1.63 vs. 0.94±0.31, BMI z-score: 2.27±0.56 vs. 0.42±0.54, p<0.01, respectively). PFF was correlated with BMI z-scores, PPHFI, and HFF in the obesity group, and multivariate analysis showed that PFF was strongly correlated with BMI z-score and PPHFI (p<0.05). The BMI z-score was strongly correlated with PFF in the control group (p<0.01). CONCLUSION: These results suggest that MRI-derived PFF measures are associated with childhood obesity. PFF and PPHFI were also highly correlated in the obesity group. Therefore, PFF may be an objective index of pancreatic fat content and has the potential for clinical utility as a non-invasive biomarker for the assessment of childhood obesity.

Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning.

We examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior-anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.

Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment.

Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.

The Role of Interventional Radiology in Treatment of Patients with Acute Trauma: A Pictorial Essay.

Acute trauma is a common cause of mortality in individuals aged < 40 years. As organ preservation has become important in treating trauma patients, the treatment is shifting from surgical management to non-operative management. A multidisciplinary team approach, including interventional radiology (IR), is essential for the optimal management of trauma patients, as IR plays an important role in injury evaluation and management. IR also contributes significantly to achieving the best clinical outcomes in critically ill trauma patients. This pictorial essay aims to present and summarize various interventional treatments in trauma patients requiring critical care.

Evaluation of the clinical efficacy of a TW3-based fully automated bone age assessment system using deep neural networks.

PURPOSE: The aim of this study was to evaluate the clinical efficacy of a Tanner-Whitehouse 3 (TW3)-based fully automated bone age assessment system on hand-wrist radiographs of Korean children and adolescents. MATERIALS AND METHODS: Hand-wrist radiographs of 80 subjects (40 boys and 40 girls, 7-15 years of age) were collected. The clinical efficacy was evaluated by comparing the bone ages that were determined using the system with those from the reference standard produced by 2 oral and maxillofacial radiologists. Comparisons were conducted using the paired t-test and simple regression analysis. RESULTS: The bone ages estimated with this bone age assessment system were not significantly different from those obtained with the reference standard (P>0.05) and satisfied the equivalence criterion of 0.6 years within the 95% confidence interval (- 0.07 to 0.22), demonstrating excellent performance of the system. Similarly, in the comparisons of gender subgroups, no significant difference in bone age between the values produced by the system and the reference standard was observed (P>0.05 for both boys and girls). The determination coefficients obtained via regression analysis were 0.962, 0.945, and 0.952 for boys, girls, and overall, respectively (P=0.000); hence, the radiologist-determined bone ages and the system-determined bone ages were strongly correlated. CONCLUSION: This TW3-based system can be effectively used for bone age assessment based on hand-wrist radiographs of Korean children and adolescents.

Adverse Initial CT Findings Associated with Poor Prognosis of Coronavirus Disease.

BACKGROUND: The predictors of poor prognosis in patients with coronavirus disease 2019 (COVID-19) using computed tomography (CT) have not been investigated in a large cohort. Therefore, the purpose of this study was to investigate the adverse initial CT features to predict poor prognosis in COVID-19. METHODS: From February to April 2020, 281 COVID-19 patients who underwent CT at the time of admission were included. We divided the patients into the severe and non-severe disease groups. The severe group included patients with severe pneumonia or critical events. Intensive care unit admission or death were the critical events in this study. We compared the clinical and CT findings between the severe and non-severe groups and investigated the prognostic factors and critical events of the severe group using the regression analysis. RESULTS: Among the 281 patients, 36 (12.8%) patients were in the severe group and 245 (87.2%) patients were in the non-severe group. Critical events occurred in 10 patients (3.6%). In the severe group, patients showed significantly more pneumonia with consolidation, crazy-paving appearance, pleural effusion, and higher CT scores than those in the non-severe group (all, P < 0.05). In the multivariate regression, pleural effusion (odds ratio [OR], 8.96; 95% confidence interval [CI], 1.81-44.42; P = 0.007), CT score > 5 (OR, 3.70; 95% CI, 1.44-9.53; P = 0.007), old age (> 77 years, OR, 9.96; 95% CI, 3.78-26.28; P < 0.001), and elevated C-reactive protein (OR, 4.15; 95% CI, 1.62-10.6; P = 0.003) were significant prognostic factors of severe disease. CT score > 5 (OR, 7.29; 95% CI, 1.37-38.68; P = 0.020), pleural effusion (OR, 5.67; 95% CI, 1.04-30.8; P = 0.045) and old age (OR, 8.6; 95% CI, 1.80-41.0; P = 0.007) were also significant predictors of critical events. CONCLUSION: Pleural effusion and the extent of pneumonia on initial CT scans are associated with poor prognosis in patients with COVID-19.

Characteristics of COVID-19 Patients Who Progress to Pneumonia on Follow-Up Chest Radiograph: 236 Patients from a Single Isolated Cohort in Daegu, South Korea.

OBJECTIVE: We investigated the prevalence of pneumonia in novel coronavirus disease 2019 (COVID-19) patients using chest radiographs to identify the characteristics of those with initially negative chest radiographs, who were positive for pneumonia on follow-up. MATERIALS AND METHODS: Retrospective cohort data of 236 COVID-19 patients were reviewed. Chest radiography was performed on admission, with serial radiographs obtained until discharge. The 'positive conversion group' was defined as patients whose initial chest radiographs were negative but were positive for pneumonia during follow-up. Patients with initially positive chest radiographs were defined as the 'initial pneumonia group.' Patients with negative initial and follow-up chest radiographs were defined as the 'non-pneumonia group.' Clinical and laboratory findings were compared between groups, and predictors of positive conversion were investigated. RESULTS: Among 236 patients, 108 (45.8%) were in the non-pneumonia group, 69 (29.2%) were in the initial pneumonia group, and 59 (25%) were in the positive conversion group. The patients in the 'initial pneumonia group' and 'positive conversion group' were older, had higher C-reactive protein (CRP) and lactate dehydrogenase levels, and lower absolute lymphocyte counts than those in the 'non-pneumonia group' (all p < 0.001). Among patients with negative initial chest radiographs, age ≥ 45 years (odds ratio [OR]: 3.93, 95% confidence interval [CI]: 1.76-8.75, p = 0.001), absolute lymphocyte count < 1500 cells/µL (OR: 2.25, 95% CI: 1.03-4.89, p = 0.041), and CRP > 0.5 mg/dL (OR: 3.91, 95% CI: 1.54-9.91, p = 0.004) were independent predictors for future development of pneumonia. CONCLUSION: More than a half of COVID-19 patients initially had normal chest radiographs; however, elderly patients (≥ 45 years of age) with abnormal laboratory findings (elevated CRP and low absolute lymphocyte counts) developed pneumonia on follow-up radiographs.

Factors associated with Advanced Bone Age in Overweight and Obese Children.

PURPOSE: Obese children may often present with advanced bone age. We aimed to evaluate the correlation between factors associated with childhood obesity and advanced bone age. METHODS: We enrolled 232 overweight or obese children. Anthropometric and laboratory data, and the degree of nonalcoholic fatty liver disease (NAFLD) were measured. We analyzed factors associated with advanced bone age by measuring the differences between bone and chronological ages. RESULTS: The normal and advanced bone age groups were comprised of 183 (78.9%) and 49 (21.1%) children, respectively. The prevalence of advanced bone age significantly increased as the percentiles of height, weight, waist circumference, and body mass index (BMI) increased. BMI z-score was higher in the advanced bone age group than in the normal bone age group (2.43±0.52 vs. 2.10±0.46; p<0.001). The levels of insulin (27.80±26.13 µU/mL vs. 18.65±12.33 µU/mL; p=0.034) and homeostatic model assessment-insulin resistance (6.56±6.18 vs. 4.43±2.93; p=0.037) were significantly higher, while high density lipoprotein-cholesterol levels were lower (43.88±9.98 mg/dL vs. 48.95±10.50 mg/dL; p=0.005) in the advanced bone age group compared to those in the normal bone age group, respectively. The prevalence of advanced bone age was higher in obese children with metabolic syndrome than in those without (28.2% vs. 14.7%; p=0.016). The prevalence of advanced bone age was higher in obese children with a more severe degree of NAFLD. CONCLUSION: Advanced bone age is associated with a severe degree of obesity and its complications.

Ultrasonographic Quantitative Analysis of Fatty Pancreas in Obese Children: Its Correlation with Metabolic Syndrome and Homeostasis Model Assessment of Insulin Resistance.

OBJECTIVES: To evaluate pancreatic echogenicity on transabdominal ultrasonography and the correlation of fatty pancreas with metabolic syndrome (MetS), as well as insulin resistance (homeostasis model assessment of insulin resistance [HOMA-IR]). STUDY DESIGN: This retrospective study included 135 obese children and adolescents who underwent transabdominal ultrasonography from January 2015 to December 2015. Fatty pancreas was quantitatively analyzed using the pancreato-perihepatic fat index (PPHFI). The correlation between the PPHFI and HOMA-IR was analyzed, and multivariate logistic regression analysis was used to determine factors that were independently correlated with MetS. Receiver operating characteristic curve analysis was performed to determine the best cut-off value of the PPHFI for diagnosing MetS. RESULTS: The PPHFI and the HOMA-IR value were significantly higher in subjects with MetS than in those without MetS (P < .0001). The PPHFI also showed an association with the HOMA-IR value (r = 0.70; P <.0001). The PPHFI was an independent factor for diagnosing MetS (OR 4.36; P = .032). The best cut-off value for the PPHFI for a diagnosis of MetS was 2.34 with a sensitivity of 0.96 and specificity 0.70. CONCLUSIONS: These results suggest that an increased PPHFI is significantly correlated with MetS and insulin resistance, and that the PPHFI may be a useful indicator for diagnosing MetS in obese children and adolescents. The impact of the presence of fatty pancreas in obese children and adolescents must be evaluated.

Spectrum of imaging findings of chronic granulomatous disease: a single center experience.

The purpose of this pictorial essay is to present and summarize findings of various images of chronic granulomatous disease (CGD). CGD represents a heterogeneous group of disorders caused by defective generation of respiratory bursts in human phagocytes. This defect results in abnormal phagocytic functions and defective killing of bacteria by phagocytes. CGD may involve many organs and present with recurrent infections and inflammations. Radiologists should consider the possibility of CGD when a patient presents with atypical and recurrent infection. They must also consider other concurrent infections a patient may have.

Percutaneous Drainage and Povidone-Iodine Sclerotherapy of Cervical Lymphatic Malformation.

Lymphatic malformations in cases with macrocystic lesions can be treated with surgical excision or sclerotherapy using alcohol, bleomycin, doxycycline, or OK-432. We report a case of a 24-year-old woman who underwent percutaneous drainage and povidone-iodine sclerotherapy as primary treatment for cervical lymphatic malformation. The patient underwent povidone-iodine sclerotherapy for 3 consecutive days. After 8 months, ultrasonography of the lesion in the neck revealed complete resolution of the cervical lymphatic malformation without any complication. Povidone-iodine sclerotherapy can be a safe and cost-effective treatment option for cervical lymphatic malformation.

Associations among the Degree of Nonalcoholic Fatty Liver Disease, Metabolic Syndrome, Degree of Obesity in Children, and Parental Obesity.

PURPOSE: To analyze the associations among the degrees of nonalcoholic fatty liver disease (NAFLD) by ultrasonography and metabolic syndrome, degrees of obesity in children, and degrees of parental obesity. METHODS: A total of 198 children with obesity who visited a pediatric obesity clinic were prospectively enrolled in this study. The severity of NAFLD based on ultrasonography was classified into no, mild, moderate, or severe NAFLD group. The degree of obesity based on the percentage over standard weight for height per sex was classified into mild, moderate, or severe. RESULTS: Of 132 patients evaluated for the degree of NAFLD and metabolic syndrome, the p-value of correlation between the two factors was 0.009. Therefore, metabolic syndrome might significantly affect the degree of NAFLD. Of 158 patients evaluated for the degree of NAFLD and the degree of obesity, the p-value of correlation between the two factors was 0.122. Of 154 patients evaluated for the degree of obesity and father's obesity, the p-value was 0.076. Of 159 patients evaluated for the degree of obesity and mother's obesity, the p-value was 0.000, indicating that mother's obesity could significantly affect the degree of obesity in children. Of 142 patients evaluated for the degree of obesity and metabolic syndrome, the p-value was 0.288. CONCLUSION: Metabolic syndrome might significantly affect the degree of nonalcoholic fatty liver in children. In addition, mother's obesity might be a significant factor that affects the degree of obesity in children.