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Objective@#This study evaluated the accuracy of the pre-hospital shock index multiplied by the AVPU scale (PSIAVPU) as a predictor of massive transfusion (MT) and traumatic coagulopathy. @*Methods@#This research was a retrospective single-center study that included patients consecutively presenting to a trauma center between 2017 and 2020. The predictive value of the PSIAVPU for MT, in-hospital mortality, and traumatic coagulopathy was measured using the area under the curve (AUC) of the receiver operating characteristic curve. The AUC of the PSIAVPU was compared with the Reverse Shock Index multiplied by the Glasgow Coma Scale (rSIG) measured at the trauma center presentation. @*Results@#One thousand seven hundred and ninety-two patients were included, of which 163 patients (9.09%) received MT and 195 patients (10.88%) died during their hospital stay. Traumatic coagulopathy was observed in 245 patients. The AUC values for the PSIAVPU in terms of predicting MT, hospital mortality, and traumatic coagulopathy were 0.755, 0.752, and 0.736, respectively. @*Conclusion@#In patients with trauma, the predictive power of the PSIAVPU was higher than that of the prehospital shock index and was comparable to that of the rSIG. The PSIAVPU is a useful indicator that can be used easily and quickly for trauma patients at the prehospital stage.
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Purpose@#This study aimed to predict high-risk neuroblastoma among neuroblastic tumors using radiomics features extracted from MRI. @*Materials and Methods@#Pediatric patients (age≤18 years) diagnosed with neuroblastic tumors who had pre-treatment MR images available were enrolled from institution A from January 2010 to November 2019 (training set) and institution B from January 2016 to January 2022 (test set). Segmentation was performed with regions of interest manually drawn along tumor margins on the slice with the widest tumor area by two radiologists. First-order and texture features were extracted and intraclass correlation coefficients (ICCs) were calculated. Multivariate logistic regression (MLR) and random forest (RF) models from 10-fold cross-validation were built using these features. The trained MLR and RF models were tested in an external test set. @*Results@#Thirty-two patients (M:F=23:9, 26.0±26.7 months) were in the training set and 14 patients (M:F=10:4, 33.4±20.4 months) were in the test set with radiomics features (n=930) being extracted. For 10 of the most relevant features selected, intra- and interobserver variability was moderate to excellent (ICCs 0.633–0.911, 0.695–0.985, respectively). The area under the receiver operating characteristic curve (AUC) was 0.94 (sensitivity 67%, specificity 91%, and accuracy 84%) for the MLR model and the average AUC was 0.83 (sensitivity 44%, specificity 87%, and accuracy 75%) for the RF model from 10-fold cross-validation. In the test set, AUCs of the MLR and RF models were 0.94 and 0.91, respectively. @*Conclusion@#An MRI-based radiomics model can help predict high-risk neuroblastoma among neuroblastic tumors.
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Objective@#To determine whether dynamic susceptibility contrast-enhanced (DSC) perfusion magnetic resonance imaging (MRI) can be used to evaluate posterior cerebral circulation in pediatric patients with moyamoya disease (MMD) who underwent anterior revascularization. @*Materials and Methods@#This study retrospectively included 73 patients with MMD who underwent DSC perfusion MRI (age, 12.2 ± 6.1 years) between January 2016 and December 2020, owing to recent-onset clinical symptoms during the follow-up period after completion of anterior revascularization. DSC perfusion images were analyzed using a dedicated software package (NordicICE; Nordic NeuroLab) for the middle cerebral artery (MCA), posterior cerebral artery (PCA), and posterior border zone between the two regions (PCA-MCA). Patients were divided into two groups; the PCA stenosis group included 30 patients with newly confirmed PCA involvement, while the no PCA stenosis group included 43 patients without PCA involvement. The relationship between DSC perfusion parameters and PCA stenosis, as well as the performance of the parameters in discriminating between groups, were analyzed. @*Results@#In the PCA stenosis group, the mean follow-up duration was 5.3 years after anterior revascularization, and visual disturbances were a common symptom. Normalized cerebral blood volume was increased, and both the normalized time-topeak (nTTP) and mean transit time values were significantly delayed in the PCA stenosis group compared with those in the no PCA stenosis group in the PCA and PCA-MCA border zones. TTP PCA (odds ratio [OR] = 6.745; 95% confidence interval [CI] = 2.665–17.074; P < 0.001) and CBVPCA-MCA (OR = 1.567; 95% CI = 1.021–2.406; P = 0.040) were independently associated with PCA stenosis. TTPPCA showed the highest receiver operating characteristic curve area in discriminating for PCA stenosis (0.895; 95% CI = 0.803–0.986). @*Conclusion@#nTTP can be used to effectively diagnose PCA stenosis. Therefore, DSC perfusion MRI may be a valuable tool for monitoring PCA stenosis in patients with MMD.
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Microcephalic osteodysplastic primordial dwarfism type II (MOPD-II) is a rare disease with characteristic skeletal abnormalities and severe comorbidities related to cerebrovascular diseases. It is frequently associated with early onset cerebrovascular diseases due to its predisposition to intracranial arteriopathies such as aneurysms and moyamoya syndrome. Herein, we report cases of two siblings presenting with multiple cerebral aneurysms and moyamoya syndrome. Two brothers with short stature were genetically diagnosed with MOPD-II at the age of 18 years and 9 years. Magnetic resonance angiography of the brother at the age of 20 years demonstrated a ruptured left A2 aneurysm with multiple variable-sized aneurysms in intracerebral arteries. The younger brother underwent brain imaging for screening at the age of 12 years which revealed a sizable basilar top aneurysm, multiple tiny aneurysms, and steno-occlusive changes in the left A1 and M1. In conclusion, cerebrovascular arteriopathy was diagnosed using brain magnetic resonance imaging in a pediatric patient with MOPD-II. Cerebrovascular comorbidities can occur at any point in life. It can lead to disability or death. Therefore, routine screening for cerebrovascular comorbidities in patients with MOPD-II who have reached adolescence is recommended.
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Purpose@#To develop and evaluate a deep learning technique to automatically segment bone structures in zero echo time (ZTE) for skull magnetic resonance imaging (MRI) in children. @*Materials and Methods@#From January to December 2021, 38 bone ZTE MRIs from infants and children (age range, 1–31 months) were collected for model development.Mask images were generated by manually segmenting the craniofacial bone using a commercial segmentation program. Among them, 35 ZTE series were used to train the three-dimensional (3D)-nnUnet deep learning model and the remaining three series were used for model validation. A temporally different dataset of 19 ZTE bone MRIs obtained in May 2022 from infants and children (age range, 3–168 months) was used to determine the model’s performance. Dice similarity coefficient was calculated for each test case.From 3D volume rendering images, segmentation accuracy, overall image quality, and visibility of cranial sutures were subjectively evaluated on a 5-point scale and compared with ground truth data from manual segmentation. Reasons for segmentation failure were analyzed using axially segmented ZTE images. @*Results@#For the test set, the mean Dice similarity coefficient was 0.985 ± 0.019. The segmentation accuracy was lower than the ground truth without showing a statistically significant difference between the two (3.39 ± 1.11 vs. 3.73 ± 0.77, p = 0.055). The overall image quality and suture visibility showed no significant difference (3.34 ± 0.75 vs.3.42 ± 0.69, p = 0.317; 3.55 ± 0.97 vs. 3.60 ± 0.95, p = 0.157). Common reasons for low segmentation accuracy were well-pneumatized sinuses, metal artifacts, skin at the vertex level, and bones too thin. @*Conclusion@#The deep learning-based automatic segmentation technique of bone ZTE MRIs showed comparable segmentation performance to manual segmentation. Using the deep learning-based segmentation results, acceptable 3D-volume rendering images of craniofacial bones were generated.
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Objective@#We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. @*Materials and Methods@#We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. @*Results@#The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%–91.27%] vs. [standardized, 93.16%–96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%–91.37% vs. standardized, 1.99%–4.41%). In all protocols, CCCs improved after image conversion (original, -0.006–0.964 vs. standardized, 0.990–0.998). @*Conclusion@#Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.
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Purpose@#This study aimed to evaluate the usefulness of two-dimensional shear wave elastography (2D-SWE) in diagnosing hepatic veno-occlusive disease (VOD) in pediatric patients. @*Methods@#This study retrospectively included pediatric patients who underwent hematopoietic stem cell transplantation (HSCT) between November 2019 and January 2021. All 34 patients (8.7±5.0 years) were examined using 2D-SWE for an initial diagnosis. A subgroup analysis was performed using the data from follow-up examinations of patients diagnosed with VOD. The characteristics of the initial VOD diagnosis were compared with the longitudinal changes observed in VOD patients who underwent multiple ultrasound examinations. @*Results@#In total, 19 patients were diagnosed with VOD at 17.6±9.4 days after HSCT. All VOD patients showed hepatomegaly, ascites, and gallbladder wall thickening. Liver stiffness was higher in VOD patients than in non-VOD patients (12.4±1.1 vs. 6.3±0.8 kPa, P<0.001). Liver stiffness values above 7.2 kPa showed 84.2% sensitivity and 93.3% specificity in distinguishing VOD from non-VOD (area under the curve, 0.925; 95% confidence interval, 0.780 to 0.987; P<0.001). A subgroup analysis of 11 patients showed a linear decrease in liver stiffness values after VOD diagnosis with treatment (first, second, and third follow-ups; 13.5±1.7, 11.3±1.4, and 9.5±0.8 kPa, respectively), but without statistical significance in the pairwise analysis. @*Conclusion@#Liver stiffness measured using 2D-SWE increased in pediatric patients who develop VOD after HSCT. Therefore, liver stiffness can be a predictive and quantitative parameter for diagnosing VOD.
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The Korean Society of Radiology and Medical Guidelines Committee amended the existing 2016 guidelines to publish the “Korean Clinical Practice Guidelines for Adverse Reactions to Iodide Contrast for Injection and Gadolinium Contrast for MRI: The Revised Clinical Consensus and Recommendations (2022 Third Edition).” Expert members recommended and approved by the Korean Society of Radiology, the Korean Academy of Asthma, Allergy and Clinical Immunology, and the Korean Nephrology Society participated together. According to the expert consensus or systematic literature review, the description of the autoinjector and connection line for the infection control while using contrast medium, the acute adverse reaction, and renal toxicity to iodized contrast medium were modified and added. We would like to introduce the revised contents.
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Objective@#To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. @*Materials and Methods@#This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). @*Results@#The AI model showed an AUROC of 0.922 (95% CI, 0.842–0.969) in the internal test set and 0.870 (95% CI, 0.785–0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%–92.0%) and specificity of 91.3% (95% CI, 79.2%–97.6%) for the internal test set and 78.9% (95% CI, 54.4%–93.9%) and 88.2% (95% CI, 78.7%– 94.4%), respectively, for the external test set. With the model’s assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020–0.168; p = 0.012) and 0.069 (95% CI, 0.002–0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074–0.090; p = 0.850). @*Conclusion@#A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.
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Hypoxic-ischemic encephalopathy in neonates is an important cause of brain damage that leads to severe neurological sequelae or death. Brain injury patterns on magnetic resonance imaging (MRI) scans are used to predict neurodevelopmental outcome severity. This case series describes the clinical manifestations and neurologic outcomes of four preterm infants with isolated deep gray matter injuries. Basal ganglia and thalamic lesions were noted without white matter and cerebral cortex lesion on brain MRI. All patients were preterm infants born at less than 33 weeks’ gestation and required resuscitation in the delivery room. All had seizures during the neonatal period requiring anti-seizure medications. Severe neurologic disability was identified in three patients using neurodevelopmental assessment tools. Another patient has not been evaluated with assessment tools yet as he was 2 months’ corrected age, but he was supported by home ventilation via a tracheostomy due to insufficient self-respiration. This case series demonstrates that isolated deep gray matter injuries in preterm infants could predict severe neurodevelopmental outcomes.
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Purpose@#In 2017, the Children’s Hepatic Tumors International Collaboration-Hepatoblastoma Stratification (CHIC-HS) system was introduced. We aimed to evaluate the accuracy of CHIC-HS System for the prediction of event-free survival (EFS) in Korean pediatric patients with hepatoblastoma. @*Materials and Methods@#This two-center retrospective study included consecutive Korean pediatric patients with histopathologically confirmed hepatoblastoma from March 1988 through September 2019. We compared EFS among four risk groups according to the CHIC-HS system. Discriminatory ability of CHIC-HS system was also evaluated using optimism-corrected C-statistics. Factors associated with EFS were explored using multivariable Cox regression analysis. @*Results@#We included 129 patients (mean age, 2.6±3.3 years; female:male, 63:66). The 5-year EFS rates in the very low, low, intermediate, and high-risk groups, according to the CHIC-HS system were 90.0%, 82.8%, 73.5%, and 51.3%, respectively. The CHIC-HS system aligned significantly well with EFS outcomes (p=0.004). The optimism-corrected C index of CHIC-HS was 0.644 (95% confidence interval [CI], 0.561 to 0.727). Age ≥ 8 (vs. age ≤ 2; hazard ratio [HR], 2.781; 95% CI, 1.187 to 6.512; p=0.018), PRE-Treatment EXTent of tumor (PRETEXT) stage IV (vs. PRETEXT I or II; HR, 2.774; 95% CI, 1.228 to 5.974; p=0.009), and presence of metastasis (HR, 2.886; 95% CI, 1.457 to 5.719; p=0.002), which are incorporated as the first three nodes in the CHIC-HS system, were independently associated with EFS. @*Conclusion@#The CHIC-HS system aligned significantly well with EFS outcomes in Korean pediatric patients with hepatoblastoma. Age group, PRETEXT stage, and presence of metastasis were independently associated with EFS.
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Purpose@#The purpose of this study was to use shear-wave elastography (SWE) to assess testicular stiffness changes during the Valsalva maneuver in adolescents and young adults with varicocele, to compare these changes according to clinical severity, and to evaluate the role of SWE in the diagnosis of varicocele. @*Methods@#This study included patients undergoing testicular ultrasonography for the diagnosis of varicocele or for post-varicocelectomy follow-up between June 2016 and February 2017. Fifty-four testicles of 27 consecutive patients (mean age, 15.9 years) were classified by clinical grade (grade 0-3). Using SWE, mean testicular stiffness (Emean) was measured at rest and during the Valsalva maneuver. The correlations between multiple ultrasonographic parameters (volume asymmetry, Emean at rest, and absolute and percentage changes in Emean during the Valsalva maneuver) and clinical grade were assessed using the Spearman correlation test. @*Results@#The Emean at rest was similar across clinical grades. During the Valsalva maneuver, increased testicular stiffness was frequently observed in patients with grade 2 or 3 varicocele but rarely observed in those with grade 0 or 1 varicocele. The changes in Emean were positively correlated with the clinical grade (all P0.05). @*Conclusion@#A transient, reversible increase in testicular stiffness during the Valsalva maneuver was observed in adolescents with high-grade varicocele, and the degree of stiffness change was correlated with the clinical grade. Stiffness change identified using SWE during the Valsalva maneuver is a potential surrogate indicator of venous congestion.
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Purpose@#To identify characteristic magnetic resonance imaging (MRI) features to differentiate between Krabbe disease and metachromatic leukodystrophy (MLD) in young children. @*Materials and Methods@#We collected all confirmed cases of Krabbe disease and MLD between October 2004 and September 2020 at Seoul National University Children's Hospital. Patients with initial MRI available were included. Their initial MRIs were retrospectively reviewed for the following: 1) presence of white matter signal abnormality involving the periventricular and deep white matter, subcortical white matter, internal capsule, brainstem, and cerebellum; 2) presence of volume decrease and signal alteration in the corpus callosum and thalamus; 3) presence of the tigroid sign; 4) presence of optic nerve hypertrophy; and 5) presence of enhancement or diffusion restriction. @*Results@#Eleven children with Krabbe disease and 12 children with MLD were included in this study. There was no significant difference in age or symptoms at onset.Periventricular and deep white matter signal alterations sparing the subcortical white matter were present in almost all patients of the two groups. More patients with Krabbe disease had T2 hyperintensities in the internal capsule and brainstem than patients with MLDs. In contrast, more patients with MLD had T2 hyperintensities in the splenium and genu of the corpus callosum. No patient with Krabbe disease showed T2 hyperintensity in the corpus callosal genu. A decrease in volume in the corpus callosum and thalamus was more frequently observed in patients with Krabbe disease than in those with MLD. Other MRI findings including the tigroid sign and optic nerve hypertrophy were not significantly different between the two groups. @*Conclusion@#Signal abnormalities in the internal capsule and brainstem, decreased thalamic volume, decreased splenial volume accompanied by signal changes, and absence of signal changes in the callosal genu portion were MRI findings suggestive of Krabbe disease rather than MLD based on initial MRI. Other MRI findings such as the tigroid sign could not help differentiate between these two diseases.
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Objective@#To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. @*Materials and Methods@#Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. @*Results@#The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). @*Conclusion@#The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.
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Objective@#The purposes of this study were to analyze the radiation doses for pediatric abdominopelvic and chest CT examinations from university hospitals in Korea and to establish the local diagnostic reference levels (DRLs) based on the body weight and size. @*Materials and Methods@#At seven university hospitals in Korea, 2494 CT examinations of patients aged 15 years or younger (1625 abdominopelvic and 869 chest CT examinations) between January and December 2017 were analyzed in this study. CT scans were transferred to commercial automated dose management software for the analysis after being de-identified. DRLs were calculated after grouping the patients according to the body weight and effective diameter. DRLs were set at the 75th percentile of the distribution of each institution’s typical values. @*Results@#For body weights of 5, 15, 30, 50, and 80 kg, DRLs (volume CT dose index [CTDIvol]) were 1.4, 2.2, 2.7, 4.0, and 4.7 mGy, respectively, for abdominopelvic CT and 1.2, 1.5, 2.3, 3.7, and 5.8 mGy, respectively, for chest CT. For effective diameters of 24 cm, DRLs (size-specific dose estimates [SSDE]) were 4.1, 5.0, 5.7, 7.1, and 7.2 mGy, respectively, for abdominopelvic CT and 2.8, 4.6, 4.3, 5.3, and 7.5 mGy, respectively, for chest CT.SSDE was greater than CTDIvol in all age groups. Overall, the local DRL was lower than DRLs in previously conducted dose surveys and other countries. @*Conclusion@#Our study set local DRLs in pediatric abdominopelvic and chest CT examinations for the body weight and size.Further research involving more facilities and CT examinations is required to develop national DRLs and update the current DRLs.
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Objective@#To describe the anatomic locations and imaging features of posterior lung herniation in unilateral pulmonary agenesis and aplasia, focusing on radiograph-CT/MRI correlation. @*Materials and Methods@#A total of 10 patients (seven with pulmonary agenesis and three with pulmonary aplasia, male:female = 1:9, mean age 7.3 years, age range from 1 month to 20 years) were included. Chest radiographs (n = 9), CT (n = 9), and MRI (n = 1) were reviewed to assess the type of lung underdevelopment, presence of anterior and posterior lung herniation, bronchus origin, supplying artery, and draining vein of the herniated lung. @*Results@#Pulmonary agenesis/aplasia more commonly affected the left lung (n = 7) than the right lung (n = 3). Anterior lung herniation was observed in nine of the 10 patients. Posterior lung herniation was observed in seven patients with left pulmonary agenesis/aplasia. Two patients showed posterior lung herniation crossing the midline but not beyond the aorta, and five patients showed the posteriorly herniated right lower lobe crossing the midline to extend into the left hemithorax farther beyond the descending thoracic aorta through the space between the esophagus and the aorta. This anatomical configuration resulted in a characteristic radiographic finding of a radiolucent area with a convex lateral border and a vertical medial border in the left lower lung zone, revealing a tongue-like projection on CT and MRI. @*Conclusion@#Posterior lung herniation occurs in unilateral left lung agenesis/aplasia. Approximately 70% of the cases of posterior lung herniation reveal a unique radiolucent tongue-like projection in the left lower lung zone on imaging studies, which is caused by the extension of the posteriorly herniated right lung farther beyond the descending aorta.
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Objective@#We aimed to investigate whether repeated intravascular administration of iodinated contrast media (ICM) or gadolinium-based contrast agents (GBCAs) within a short interval was associated with an increased risk of post-contrast acute kidney injury (PC-AKI). @*Materials and Methods@#This retrospective study included 300 patients (mean age ± standard deviation, 68.5 ± 8.1 years; 131 male and 169 female) who had undergone at least one ICM-enhanced perfusion brain CT scan, had their baseline and follow-up serum creatinine levels available, and had not undergone additional contrast-enhanced examinations 72 hours before and after a time window of interest were included. The study population was divided into three groups: single-dose group and groups of patients who had received multiple contrast administrations in the time window of interest with the minimum contrast repeat interval either within 4 hours (0–4-hour group) or between 4 to 48 hours (4–48-hour group).Multivariable logistic regression analysis was conducted to evaluate the association between AKI and repeated ICM administrations. A similar supplementary analysis was performed including both ICM and GBCA. @*Results@#When ICM was only considered ignoring GBCA, among 300 patients, 207 patients received a single dose of ICM, 58 had repeated doses within 4 hours (0–4-hour group), and 35 patients had repeated doses between 4 to 48 hours (4–48-hour group). Most patients (> 95%) had a baseline estimated glomerular filtration rate (eGFR) of ≥ 30 mL/min/1.73 m2 . AKI occurred in 7.2%, 13.8%, and 8.6% of patients in the single-dose, 0–4-hour, and 4–48-hour groups, respectively. In the 0–4-hour and 4–48-hour groups, additional exposure to ICM was not associated with AKI after adjusting for comorbidities and nephrotoxic drugs (all p values > 0.05). @*Conclusion@#Repeated intravascular administrations of ICM within a short interval did not increase the risk of AKI in our study patients suspected of acute stroke with a baseline eGFR of ≥ 30 mL/min/1.73 m2 .
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Objective@#To assess the role of arterial spin-labeling (ASL) perfusion MRI in identifying cerebral perfusion changes after indirect revascularization in children with moyamoya disease. @*Materials and Methods@#We included pre- and postoperative perfusion MRI data of 30 children with moyamoya disease (13 boys and 17 girls; mean age ± standard deviation, 6.3 ± 3.0 years) who underwent indirect revascularization between June 2016 and August 2017. Relative cerebral blood flow (rCBF) and qualitative perfusion scores for arterial transit time (ATT) effects were evaluated in the middle cerebral artery (MCA) territory on ASL perfusion MRI. The rCBF and relative time-topeak (rTTP) values were also measured using dynamic susceptibility contrast (DSC) perfusion MRI. Each perfusion change on ASL and DSC perfusion MRI was analyzed using the paired t test. We analyzed the correlation between perfusion changes on ASL and DSC images using Spearman’s correlation coefficient. @*Results@#The ASL rCBF values improved at both the ganglionic and supraganglionic levels of the MCA territory after surgery (p = 0.040 and p = 0.003, respectively). The ATT perfusion scores also improved at both levels (p < 0.001 and p < 0.001, respectively). The rCBF and rTTP values on DSC MRI showed significant improvement at both levels of the MCA territory of the operated side (all p < 0.05). There was no significant correlation between the improvements in rCBF values on the two perfusion images (r = 0.195, p = 0.303); however, there was a correlation between the change in perfusion scores on ASL and rTTP on DSC MRI (r = 0.701, p < 0.001). @*Conclusion@#Recognizing the effects of ATT on ASL perfusion MRI may help monitor cerebral perfusion changes and complement quantitative rCBF assessment using ASL perfusion MRI in patients with moyamoya disease after indirect revascularization.
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Purpose@#To identify characteristic magnetic resonance imaging (MRI) features to differentiate between Krabbe disease and metachromatic leukodystrophy (MLD) in young children. @*Materials and Methods@#We collected all confirmed cases of Krabbe disease and MLD between October 2004 and September 2020 at Seoul National University Children's Hospital. Patients with initial MRI available were included. Their initial MRIs were retrospectively reviewed for the following: 1) presence of white matter signal abnormality involving the periventricular and deep white matter, subcortical white matter, internal capsule, brainstem, and cerebellum; 2) presence of volume decrease and signal alteration in the corpus callosum and thalamus; 3) presence of the tigroid sign; 4) presence of optic nerve hypertrophy; and 5) presence of enhancement or diffusion restriction. @*Results@#Eleven children with Krabbe disease and 12 children with MLD were included in this study. There was no significant difference in age or symptoms at onset.Periventricular and deep white matter signal alterations sparing the subcortical white matter were present in almost all patients of the two groups. More patients with Krabbe disease had T2 hyperintensities in the internal capsule and brainstem than patients with MLDs. In contrast, more patients with MLD had T2 hyperintensities in the splenium and genu of the corpus callosum. No patient with Krabbe disease showed T2 hyperintensity in the corpus callosal genu. A decrease in volume in the corpus callosum and thalamus was more frequently observed in patients with Krabbe disease than in those with MLD. Other MRI findings including the tigroid sign and optic nerve hypertrophy were not significantly different between the two groups. @*Conclusion@#Signal abnormalities in the internal capsule and brainstem, decreased thalamic volume, decreased splenial volume accompanied by signal changes, and absence of signal changes in the callosal genu portion were MRI findings suggestive of Krabbe disease rather than MLD based on initial MRI. Other MRI findings such as the tigroid sign could not help differentiate between these two diseases.
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Objective@#To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. @*Materials and Methods@#Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. @*Results@#The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). @*Conclusion@#The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.