Safety of Gadoxetate Disodium for Hepatobiliary MRI in Children and Adolescents.

Background Despite a proven role in the characterization of liver lesions, use of the gadolinium-based contrast agent (GBCA) gadoxetate disodium at MRI is limited in children due to a lack of comparative safety data. Purpose To evaluate the safety of the GBCA gadoxetate disodium (a linear ionic hepatobiliary contrast agent [HBA]) in children and adolescents, compared with extracellular contrast agents (ECA). Materials and Methods A retrospective analysis was conducted in children and adolescents aged 18 years or younger who underwent HBA-enhanced MRI at one of three tertiary hospitals from January 2010 to December 2022. The incidence of GBCA-associated acute adverse events was compared between MRI examinations with a HBA and those with ECA. Severity was categorized according to American College of Radiology guidelines (mild, moderate, or severe). (a) Propensity score matching using multivariable logistic regression models and (b) inverse probability of treatment weighting analysis based on nine covariates (age, sex, asthma, allergic rhinitis, chronic urticaria or atopy, food allergy, drug allergy, premedication, and history of GBCA-associated adverse events) were used for confounder adjustment. Results A total of 1629 MRI examinations (ECA, n = 1256; HBA, n = 373) in 1079 patients were included (mean age, 8.6 years ± 6.5; 566 girls). The per-examination incidence of GBCA-associated acute adverse events showed no evidence of a difference, with rates of 0.9% (11 of 1256 examinations) for ECA and 1.3% (five of 373 examinations) for HBA (odds ratio [OR], 1.55 [95% CI: 0.54, 4.46]; P = .42). Acute adverse events were all mild with ECA, whereas with HBA, they were mild for four patients and moderate for one patient. There was no evidence of a difference in the incidence of acute adverse events, even in propensity score matching (OR, 1.33 [95% CI: 0.30, 5.96]; P = .71) and inverse probability of treatment weighting analysis (OR, 0.84 [95% CI: 0.25, 2.86]; P = .78). Conclusion Gadoxetate disodium showed no difference in acute adverse events compared with ECA in children and adolescents, with further large-scale pediatric studies required to confirm its safety. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Otero in this issue.

Characteristics of chronic enteropathy associated with SLCO2A1 gene (CEAS) in children, a unique type of monogenic very early-onset inflammatory bowel disease.

BACKGROUND:  Chronic enteropathy associated with SLCO2A1 gene (CEAS) is a unique type of inflammatory bowel disease. CEAS is monogenic disease and is thought to develop from childhood, but studies on pediatric CEAS are scarce. We analyzed characteristics of pediatric CEAS. METHODS: Eleven patients diagnosed with CEAS at Seoul National University Children's Hospital were identified and analyzed. Clinical data of patients were collected. Sanger sequencing of SLCO2A1 was performed on all patients. RESULTS: Patients were diagnosed at a median age of 16.0 years (IQR 11.0 ~ 20.0), and the median age at symptoms onset was only 4.0 years (IQR 2.5 ~ 6.0). Growth delay was observed at the time of diagnosis. Patients showed multiple ulcers or strictures in the small intestine, while the esophagus and colon were unaffected in any patients. Almost half of the patients underwent small intestine resection. The major laboratory features of pediatric CEAS include iron deficiency anemia (IDA), hypoalbuminemia, and near-normal levels of C-reactive protein (CRP). Two novel mutations of SLCO2A1 were identified. The most prevalent symptoms were abdominal pain and pale face. None of the immunomodulatory drugs showed a significant effect on CEAS. CONCLUSIONS: Pediatric CEAS typically develop from very young age, suggesting it as one type of monogenic very early onset inflammatory bowel disease. CEAS can cause growth delay in children but there is no effective treatment currently. We recommend screening for SLCO2A1 mutations to pediatric patients with chronic IDA from a young age and small intestine ulcers without elevation of CRP levels.

Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography.

BACKGROUND: Low-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality. OBJECTIVE: To evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children. MATERIALS AND METHODS: This single-center retrospective study included 35 pediatric patients (mean age 9.2 years, range 1-17 years) who underwent sequential abdominal CT scans-one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)-within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, the Friedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups. RESULTS: The standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05). CONCLUSION: Low-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts.

Shear wave elastography and dispersion imaging for hepatic veno-occlusive disease prediction after pediatric hematopoietic stem cell transplantation: a feasibility study.

BACKGROUND: Non-invasive imaging modalities are warranted for diagnosing and monitoring veno-occlusive disease because early diagnosis and treatment improve the prognosis. OBJECTIVE: To evaluate the usefulness of liver shear wave elastography (SWE) and shear wave dispersion (SWD) imaging in diagnosing and monitoring veno-occlusive disease in pediatric patients. MATERIALS AND METHODS: We conducted a prospective cohort study at a single tertiary hospital from March 2021 to April 2022. The study protocol included four ultrasound (US) sessions: a baseline US and three follow-up US after hematopoietic stem cell transplantation. Clinical criteria, including the European Society for Blood and Marrow Transplantation criteria, were used to diagnose veno-occlusive disease. We compared clinical factors and US parameters between the veno-occlusive disease and non-veno-occlusive disease groups. The diagnostic performance of US parameters for veno-occlusive disease was assessed by plotting receiver operating characteristic (ROC) curves. We describe temporal changes in US parameters before and after veno-occlusive disease diagnosis. RESULTS: Among the 38 participants (mean age 10.7 years), eight developed veno-occlusive disease occurring 17.0 ± 5.2 days after hematopoietic stem cell transplantation. Liver stiffness, as measured by SWE (15.0 ± 6.2 kPa vs. 5.8 ± 1.8 kPa; P<0.001), and viscosity, as assessed with SWD (17.7 ± 3.1 m/s/kHz vs. 14.3 ± 2.8 m/s/kHz; P=0.015), were significantly higher in the veno-occlusive disease group compared to the non-veno-occlusive disease group at the time of diagnosis. Liver stiffness demonstrated the highest area under the ROC (AUROC) curves at 0.960, with an optimal predictive value of >6.5 kPa, resulting in sensitivity and specificity of 100% and 83.3%, respectively. Viscosity demonstrated an AUROC of 0.783, with an optimal cutoff value of 13.9 m/s/kHz for predicting veno-occlusive disease, with a sensitivity of 100% and specificity of 53.3%, respectively. Liver stiffness increased with disease severity and decreased during post-treatment follow-up. CONCLUSION: SWE may be a promising technique for early diagnosis and severity prediction of veno-occlusive disease. Furthermore, liver viscosity assessed by SWD may serve as an additional marker of veno-occlusive disease.

Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI.

INTRODUCTION: Deep learning-based MRI reconstruction has recently been introduced to improve image quality. This study aimed to evaluate the performance of deep learning reconstruction in pediatric brain MRI. METHODS: A total of 107 consecutive children who underwent 3.0 T brain MRI were included in this study. T2-weighted brain MRI was reconstructed using the three different reconstruction modes: deep learning reconstruction, conventional reconstruction with an intensity filter, and original T2 image without a filter. Two pediatric radiologists independently evaluated the following image quality parameters of three reconstructed images on a 5-point scale: overall image quality, image noisiness, sharpness of gray-white matter differentiation, truncation artifact, motion artifact, cerebrospinal fluid and vascular pulsation artifacts, and lesion conspicuity. The subjective image quality parameters were compared among the three reconstruction modes. Quantitative analysis of the signal uniformity using the coefficient of variation was performed for each reconstruction. RESULTS: The overall image quality, noisiness, and gray-white matter sharpness were significantly better with deep learning reconstruction than with conventional or original reconstruction (all P < 0.001). Deep learning reconstruction had significantly fewer truncation artifacts than the other two reconstructions (all P < 0.001). Motion and pulsation artifacts showed no significant differences among the three reconstruction modes. For 36 lesions in 107 patients, lesion conspicuity was better with deep learning reconstruction than original reconstruction. Deep learning reconstruction showed lower signal variation compared to conventional and original reconstructions. CONCLUSION: Deep learning reconstruction can reduce noise and truncation artifacts and improve lesion conspicuity and overall image quality in pediatric T2-weighted brain MRI.

Efficacy and safety of mucous fistula refeeding in preterm infants: an exploratory randomized controlled trial.

BACKGROUND: This study aimed to evaluate whether mucous fistula refeeding (MFR) is safe and beneficial for the growth and intestinal adaptation of preterm infants with enterostomies. METHODS: This exploratory randomized controlled trial enrolled infants born before 35 weeks' gestation with enterostomy. If the stomal output was ≥ 40 mL/kg/day, infants were assigned to the high-output MFR group and received MFR. If the stoma output was < 40 mL/kg/day, infants were randomized to the normal-output MFR group or the control group. Growth, serum citrulline levels, and bowel diameter in loopograms were compared. The safety of MFR was evaluated. RESULTS: Twenty infants were included. The growth rate increased considerably, and the colon diameter was significantly larger after MFR. However, the citrulline levels did not significantly differ between the normal-output MFR and the control group. One case of bowel perforation occurred during the manual reduction for stoma prolapse. Although the association with MFR was unclear, two cases of culture-proven sepsis during MFR were noted. CONCLUSIONS: MFR benefits the growth and intestinal adaptation of preterm infants with enterostomy and can be safely implemented with a standardized protocol. However, infectious complications need to be investigated further. TRIAL REGISTRATION: clinicaltrials.gov NCT02812095, retrospectively registered on June 6, 2016.

Factors influencing outcomes of esophageal balloon dilatation for anastomosis site stenosis after esophageal atresia surgery.

BACKGROUND: Esophageal balloon dilatation is an effective treatment for anastomotic strictures, but the factors affecting the outcome of dilatation remain unclear. OBJECTIVE: To investigate the predictive factors of esophageal balloon dilatation outcome in children with anastomotic stricture after esophageal atresia repair. MATERIALS AND METHODS: We retrospectively reviewed children with esophageal atresia who underwent esophageal balloon dilatation for postoperative strictures between August 2007 and February 2021. We investigated each child's age, weight and height; type of esophageal atresia surgery; shape, length and level of stricture; esophageal balloon dilatation balloon size; application of mitomycin; number of inflation sessions; and number of esophageal balloon dilatation sessions. The outcome of each esophageal balloon dilatation session was determined as improvement in stricture diameter between pre- and post-esophageal balloon dilatation esophagography. We used uni- and multivariate analyses with generalized estimating equations to evaluate outcome predictors. RESULTS: Overall, 69 children (mean age, 2.3 years; 45 boys) underwent 227 esophageal balloon dilatations. In the univariate analysis, the positive effect of esophageal balloon dilatation decreased with increased age, weight, height, balloon size and number of esophageal balloon dilatation sessions. Additionally, the positive effect was decreased in cervical-level strictures and with the application of mitomycin during esophageal balloon dilatation. In the multivariate analysis, independent prognostic factors of the positive esophageal balloon dilatation effect were age (incidence rate ratio [IRR]: -0.01; 95% confidence interval [CI]: -0.01, -0.002), shape of stricture (IRR: -0.54; 95% CI: -0.91, -0.18) and number of esophageal balloon dilatation sessions (IRR, -0.10; 95% CI: -0.14, -0.18). CONCLUSION: Repeated esophageal balloon dilatation, older age and eccentric stricture shape are associated with poor response to esophageal balloon dilatation in children with anastomotic strictures after esophageal atresia repair.

Ultra-low-dose computed tomography with deep learning reconstruction for craniosynostosis at radiation doses comparable to skull radiographs: a pilot study.

BACKGROUND: Craniofacial computed tomography (CT) is the diagnostic investigation of choice for craniosynostosis, but high radiation dose remains a concern. OBJECTIVE: To evaluate the image quality and diagnostic performance of an ultra-low-dose craniofacial CT protocol with deep learning reconstruction for diagnosis of craniosynostosis. MATERIALS AND METHODS: All children who underwent initial craniofacial CT for suspected craniosynostosis between September 2021 and September 2022 were included in the study. The ultra-low-dose craniofacial CT protocol using 70 kVp, model-based iterative reconstruction and deep learning reconstruction techniques was compared with a routine-dose craniofacial CT protocol. Quantitative analysis of the signal-to-noise ratio and noise was performed. The 3-dimensional (D) volume-rendered images were independently evaluated by two radiologists with regard to surface coarseness, step-off artifacts and overall image quality on a 5-point scale. Sutural patency was assessed for each of six sutures. Radiation dose was compared between the two protocols. RESULTS: Among 29 patients (15 routine-dose CT and 14 ultra-low-dose CT), 23 patients had craniosynostosis. The 3-D volume-rendered images of ultra-low-dose CT without deep learning showed decreased image quality compared to routine-dose CT. The 3-D volume-rendered images of ultra-low-dose CT with deep learning reconstruction showed higher noise level, higher surface coarseness but decreased step-off artifacts, comparable signal-to-noise ratio and overall similar image quality compared to the routine-dose CT images. Diagnostic performance for detecting craniosynostosis at the suture level showed no significant difference between ultra-low-dose CT without deep learning reconstruction, ultra-low-dose CT with deep learning reconstruction and routine-dose CT. The estimated effective radiation dose for the ultra-low-dose CT was 0.05 mSv (range, 0.03-0.06 mSv), a 95% reduction in dose over the routine-dose CT at 1.15 mSv (range, 0.54-1.74 mSv). This radiation dose is comparable to 4-view skull radiography (0.05-0.1 mSv) and lower than previously reported effective dose for craniosynostosis protocols (0.08-3.36 mSv). CONCLUSION: In this pilot study, an ultra-low-dose CT protocol using radiation doses at a level similar to skull radiographs showed preserved diagnostic performance for craniosynostosis, but decreased image quality compared to the routine-dose CT protocol. However, by combining the ultra-low-dose CT protocol with deep learning reconstruction, image quality was improved to a level comparable to the routine-dose CT protocol, without sacrificing diagnostic performance for craniosynostosis.

Factors contributing to uncertainty in paediatric abdominal ultrasound reports in the paediatric emergency department.

BACKGROUND: Abdominal pain, which is a common cause of children presenting to the paediatric emergency department (PED), is often evaluated by ultrasonography (US). However, uncertainty in US reports may necessitate additional imaging. OBJECTIVE: In this study, we evaluated factors contributing to uncertainty in paediatric abdominal US reports in the PED. MATERIALS AND METHODS: This retrospective cohort study included children younger than 18 years of age who underwent abdominal US in the PED of the study hospital between January 2017 and December 2019. After exclusion, the researchers manually reviewed and classified all US reports as 'certain' or 'uncertain'. Univariate and multivariate logistic regression analyses were performed to identify the factors contributing to uncertain reports. RESULTS: In total, 1006 patients were included in the final analysis., 796 patients were tagged as having certain reports, and 210 as having uncertain reports. Children with uncertain reports had a significantly higher rate of undergoing an additional computed tomography (CT) scan (31.0% vs. 2.5%, p < 0.001) and a longer PED median length of stay (321.0 (Interquartile range (IQR); 211.3-441.5) minutes vs. 284.5 (IQR; 191.8-439.5) minutes, p = 0.042). After logistic regression, US performed by a radiology resident (odds ratio, 5.01; 95% confidence interval, 3.63-7.15) was the most significant factor contributing to uncertainty in paediatric abdominal US reports followed by obesity and age. CONCLUSION: Several factors contribute to uncertainty in paediatric abdominal US reports. Uncertain radiological reports increase the likelihood of additional CT scans. Measures to improve the clarity of radiological reports must be considered to improve the quality of care for children visiting the PED.

Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI.

Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient movement due to the relatively long data acquisition time. This could cause severe degradation of image quality and therefore affect the overall diagnosis. In this paper, we develop an efficient retrospective 2D deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in 3D brain MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns the missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving the spatial image details and improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. The proposed network is optimized by minimizing the loss of structural similarity (SSIM) using the synthesized motion-corrupted images from 83 real motion-free subjects. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans. The overall image quality of the motion-corrected images via the proposed motion correction network significantly improves SSIM from 71.66% to 95.03% and declines the mean square error from 99.25 to 29.76. These results indicate the high similarity of the brain's anatomical structure in the corrected images compared to the motion-free data. The motion-corrected results of both the simulated and real motion data showed the potential of the proposed motion correction network to be feasible and applicable in clinical practices.

Allergic-like Hypersensitivity Reactions to Gadolinium-based Contrast Agents: An 8-year Cohort Study of 154 539 Patients.

Background With the widespread use of gadolinium-based contrast agents (GBCAs), the incidence of allergic-like hypersensitivity reactions (HSRs) to GBCAs is increasing. Research on the incidence and risk factors for HSRs to GBCAs is needed for their safe use. Purpose To determine the incidence of acute and delayed reactions to GBCAs and to discuss the risk factors and strategies for the prevention of HSRs to GBCAs. Materials and Methods All cases of HSRs to contrast media that occurred at the Seoul National University Hospital from July 1, 2012, to June 30, 2020, were assessed. Information including age, sex, GBCA type, onset, and severity of HSRs was retrospectively analyzed. Results Among the 331070 cases of GBCA exposure in 154539 patients, 1304 cases of HSRs (0.4%) were reported. Acute HSRs accounted for 1178 cases (0.4%), while 126 cases (0.04%) were delayed HSRs. While both premedication (odds ratio [OR] = 0.7, P = .041) and changing the type of GBCA (OR = 0.2, P < .001) showed preventative effects in patients with a history of acute HSRs, only premedication (OR = 0.2, P = .016) significantly reduced the incidence of HSRs in patients with a history of delayed reactions. The risk of an HSR to GBCA was higher in those with a history of an HSR to iodinated contrast media (OR = 4.6, P < .001). Conclusion The rate of hypersensitivity reactions (HSRs) to gadolinium-based contrast agents (GBCAs) was 0.4%. The absence of premedication, repeated exposures to the culprit GBCA, and a history of HSRs to iodinated contrast media and GBCAs were risk factors for HSRs to GBCAs. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kallmes and McDonald in this issue.

Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network.

OBJECTIVES: To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children. METHODS: For model development, we utilized transfer learning with a pre-trained model based on adult patients. We used CT images of 31 pediatric patients under 19 years of age (mean age, 9.6 years) who underwent PET-CT from institution #1 for transfer learning. Two radiologists manually labeled the skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs, and central nervous system in each CT slice and used these as references. For external validation, we collected 14 pediatric PET/CT scans from institution #2 (mean age, 9.1 years). The Dice similarity coefficients (DSCs), sensitivities, and precision were compared between the algorithms before and after transfer learning. In addition, we evaluated segmentation performance according to sex, age (≤ 8 vs. > 8 years), and body mass index (BMI, ≤ 20 vs. > 20 kg/m2). RESULTS: The algorithm after transfer learning showed better performance than the algorithm before transfer learning for all body compositions (p < 0.001). The average DSC, sensitivity, and precision of each algorithm before and after transfer learning were 98.23% and 99.28%, 98.16% and 99.28%, and 98.29% and 99.28%, respectively. The segmentation performance of the algorithm was generally not affected by age, sex, or BMI, except for precision in the body muscle compartment. CONCLUSION: The developed model with transfer learning enabled accurate and fully automated segmentation of multiple tissues on whole-body CT scans in children. KEY POINTS: • We utilized transfer learning with a pre-trained segmentation algorithm for adult to develop an algorithm for automated segmentation of pediatric whole-body CT. • This algorithm showed excellent performance and was not affected by sex, age, or body mass index, except for precision in body muscle.

Development and evaluation of deep-learning measurement of leg length discrepancy: bilateral iliac crest height difference measurement.

BACKGROUND: Leg length discrepancy (LLD) is a common problem that can cause long-term musculoskeletal problems. However, measuring LLD on radiography is time-consuming and labor intensive, despite being a simple task. OBJECTIVE: To develop and evaluate a deep-learning algorithm for measurement of LLD on radiographs. MATERIALS AND METHODS: In this Health Insurance Portability and Accountability Act (HIPAA)-compliant retrospective study, radiographs were obtained to develop a deep-learning algorithm. The algorithm developed with two U-Net models measures LLD using the difference between the bilateral iliac crest heights. For performance evaluation of the algorithm, 300 different radiographs were collected and LLD was measured by two radiologists, the algorithm alone and the model-assisting method. Statistical analysis was performed to compare the measurement differences with the measurement results of an experienced radiologist considered as the ground truth. The time spent on each measurement was then compared. RESULTS: Of the 300 cases, the deep-learning model successfully delineated both iliac crests in 284. All human measurements, the deep-learning model and the model-assisting method, showed a significant correlation with ground truth measurements, while Pearson correlation coefficients and interclass correlations (ICCs) decreased in the order listed. (Pearson correlation coefficients ranged from 0.880 to 0.996 and ICCs ranged from 0.914 to 0.997.) The mean absolute errors of the human measurement, deep-learning-assisting model and deep-learning-alone model were 0.7 ± 0.6 mm, 1.1 ± 1.1 mm and 2.3 ± 5.2 mm, respectively. The reading time was 7 h and 12 min on average for human reading, while the deep-learning measurement took 7 min and 26 s. The radiologist took 74 min to complete measurements in the deep-learning mode. CONCLUSION: A deep-learning U-Net model measuring the iliac crest height difference was possible on teleroentgenograms in children. LLD measurements assisted by the deep-learning algorithm saved time and labor while producing comparable results with human measurements.

Thyroid nodules in childhood-onset Hashimoto's thyroiditis: Frequency, risk factors, follow-up course and genetic alterations of thyroid cancer.

OBJECTIVE: We evaluated the frequency, risk factors and the follow-up outcomes of thyroid nodules, and genetic alterations in thyroid cancer, in youth with childhood-onset Hashimoto thyroiditis (HT) residing in an iodine-sufficient country. DESIGN: A retrospective cohort study. PATIENTS AND MEASUREMENTS: A total of 213 patients (194 females, mean age 10.6 years at the time of HT diagnosis) were ultrasonographically evaluated. Thyroid nodules were categorized using the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TI-RADS). RESULTS: Thyroid nodules were detected in 40 (18.8%) patients over a median follow-up period of 3.4 years, usually after the onset of puberty. A family history of thyroid disease (hazard ratio 2.1, p = .031) was predictive of thyroid nodule detection. Papillary thyroid carcinoma (PTC) was diagnosed in 9 (4.2% of all and 22.5% of nodule-positive patients). The malignant nodules had a higher K-TIRADS or ACR-TI-RADS risk level compared with benign nodules (p < .01 for both). Genetic alterations were revealed in 7 (BRAFV600E in 6 and RET-ERC1 fusion in 1) of the eight available tumour tissue samples. None showed evidence of disease over a median follow-up period of 3.4 years. CONCLUSIONS: The nodule detection rate was 18.8%, with a 22.5% risk of malignancy among the detected nodules in childhood-onset HT patients, showing increased risk in those with a family history. Additional large-scale studies are required to evaluate the usefulness of K-TIRADS or ACR-TI-RADS risk level for the differentiation of paediatric thyroid nodules.

Cochlear duct length and cochlear distance on preoperative CT: imaging markers for estimating insertion depth angle of cochlear implant electrode.

OBJECTIVES: Preoperative estimation of the insertion depth angle of cochlear implant (CI) electrodes is essential for surgical planning. The purpose of this study was to determine the cochlear size using preoperative CT and to investigate the correlation between cochlear size and insertion depth angle in morphologically normal cochlea. METHODS: Thirty-five children who underwent CI were included in this study. Cochlear duct length (CDL) and the diameter of the cochlear basal turn (distance A/B) on preoperative CT and the insertion depth angle of the CI electrode on postoperative radiographs were independently measured by two readers. Correlation between cochlear size and insertion depth angle was evaluated. Interobserver agreement was calculated using the intraclass correlation coefficient (ICC). RESULTS: The mean CDL, distance A, and distance B of 70 ears were 36.20 ± 1.57 mm, 8.67 ± 0.42 mm, and 5.73 ± 0.32 mm, respectively. The mean insertion depth angle was 431.45 ± 38.42°. Interobserver agreements of CDL, distance A/B, and insertion depth angle were fair to excellent (ICC 0.864, 0.862, 0.529, and 0.958, respectively). Distance A (r = - 0.7643) and distance B (r = - 0.7118) showed a negative correlation with insertion depth angle, respectively (p < 0.0001). However, the correlation between CDL and insertion depth angle was not statistically significant (r = - 0.2333, p > 0.05). CONCLUSIONS: The CDL and cochlear distance can be reliably obtained from preoperative CT. Distance A can be used as a predictive marker for estimating insertion depth angle during CI surgery.

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique.

OBJECTIVES: To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT). METHODS: From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2-19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired t tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed. RESULTS: The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT. CONCLUSION: Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT. KEY POINTS: • An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load. • The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT. • This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol.

Diagnosis of Hippocampal Sclerosis in Children: Comparison of Automated Brain MRI Volumetry and Readers of Varying Experience.

BACKGROUND. Hippocampal sclerosis (HS) is a leading cause of medically refractory temporal lobe epilepsy in children. The diagnosis is clinically important because most patients with HS have good postsurgical outcomes. OBJECTIVE. This study aimed to compare the performance of a fully automated brain MRI volumetric tool and readers of varying experience in the diagnosis of pediatric HS. METHODS. This retrospective study included 22 children with HS diagnosed between January 2009 and January 2020 who underwent surgery and an age- and sex-matched control group of 44 patients with normal MRI findings and extratemporal epilepsy diagnosed between January 2009 and January 2020. Regional brain MRI volumes were calculated from a high-resolution 3D T1-weighted sequence using an automated volumetric tool. Four readers (two pediatric radiologists [experienced] and two radiology residents [inexperienced]) visually assessed each MRI examination to score the likelihood of HS. One inexperienced reader repeated the evaluations using the volumetric tool. The area under the ROC curve (AUROC), sensitivity, and specificity for HS were computed for the volumetric tool and the readers. Diagnostic performances were compared using McNemar tests. RESULTS. In the HS group, the hippocampal volume (affected vs unaffected, 3.54 vs 4.59 cm3) and temporal lobe volume (affected vs unaffected, 5.66 vs 6.89 cm3) on the affected side were significantly lower than on the unaffected side (p < .001) using the volu-metric tool. AUROCs of the volumetric tool were 0.813-0.842 in patients with left HS and 0.857-0.980 in patients with right HS (sensitivity, 81.8-90.9%; specificity, 70.5-95.5%). No significant difference (p = .63 to > .99) was observed between the performance of the volumetric tool and the performance of the two experienced readers as well as one inexperienced reader (AUROCs for these three readers, 0.968-0.999; sensitivity, 86.4-90.9%; specificity, 100.0%). The volumetric tool had better performance (p < .001) than the other inexperienced reader (AUROC, 0.806; sensitivity, 81.8%; specificity, 47.7%). With subsequent use of the tool, this inexperienced reader showed a nonsignificant increase (p = .10) in AUROC (0.912) as well as in sensitivity (86.4%) and specificity (84.1%). CONCLUSION. A fully automated volumetric brain MRI tool outperformed one of two inexperienced readers and performed as well as two experienced readers in identifying and lateralizing HS in pediatric patients. The tool improved the performance of an inexperienced reader. CLINICAL IMPACT. A fully automated volumetric tool facilitates diagnosis of HS in pediatric patients, especially for an inexperienced reader.

Ultrasonographic cystometry for neurogenic bladder using elastography.

AIM: Ultrasound shear wave elastography (SWE) has been used to measure elasticity (Young's modulus: YM) in solid organs. It was reported to show a better correlation with intravesical pressure (Pves) than with compliance, supporting its potential use in noninvasive cystometry. Contrariwise, conceptually, YM should be more correlated with compliance than with Pves. To optimize the potential use of YM as a noninvasive urodynamic study, the relationship between YM, Pves, and compliance was reassessed in this study. METHOD: YM was serially measured using SWE along with bladder filling. To overcome problems inherent to current compliance measurements, modified dynamic compliance was developed from cystometry by a locally weighted scatter plot smoothing algorithm. Then it was matched with YM from SWE. YM was also correlated with Pves. Furthermore, to understand the nature of YM, which was measured by ultrasound, the bladder wall's modulus, which was the mathematical assessment of YM derived from cystometric data, was also calculated and compared. RESULTS: Thirty-two neurogenic bladder patients were included in this study. YM correlated with Pves (r = .72, p < .0001) better than with modified dynamic compliance (r = -0.43, p < .0001). The correlation of YM with Pves was even higher than that with the calculated bladder wall's modulus (r = .52, p < .0001). CONCLUSION: YM measured by SWE associates with Pves better than with compliance, confirming the results of previous studies. SWE reflects the integration of both the holding capability of the bladder wall and urine rather than either of one, implying its potential utilization in noninvasive cystometry.

The usefulness of noninvasive liver stiffness assessment using shear-wave elastography for predicting liver fibrosis in children.

BACKGROUND: Pediatric patients with liver disease require noninvasive monitoring to evaluate the risk of fibrosis progression. This study aimed to identify the significant factors affecting liver stiffness values using two-dimensional shear-wave elastography (2D-SWE), and determine whether liver stiffness can predict the fibrosis stage of various childhood liver diseases. METHODS: This study included 30 children (22 boys and 8 girls; mean age, 5.1 ± 6.1 years; range, 7 days-17.9 years) who had undergone biochemical evaluation, 2D-SWE examination, histopathologic analysis of fibrosis grade (F0 to F3), assessment of necroinflammatory activity, and steatosis grading between August 2016 and March 2020. The liver stiffness from 2D-SWE was compared between fibrosis stages using Kruskal-Wallis analysis. Factors that significantly affected liver stiffness were evaluated using univariate and multivariate linear regression analyses. The diagnostic performance was determined from the area under the receiver operating curve (AUC) values of 2D-SWE liver stiffness. RESULTS: Liver stiffness at the F0-1, F2, and F3 stages were 7.9, 13.2, and 21.7 kPa, respectively (P < 0.001). Both fibrosis stage and necroinflammatory grade were significantly associated with liver stiffness (P < 0.001 and P = 0.021, respectively). However, in patients with alanine aminotransferase (ALT) levels below 200 IU/L, the only factor affecting liver stiffness was fibrosis stage (P = 0.030). The liver stiffness value could distinguish significant fibrosis (≥ F2) with an AUC of 0.950 (cutoff value, 11.3 kPa) and severe fibrosis (F3 stage) with an AUC of 0.924 (cutoff value, 18.1 kPa). The 2D-SWE values for differentiating significant fibrosis were 10.5 kPa (≥ F2) and 18.1 kPa (F3) in patients with ALT levels below 200 IU/L. CONCLUSION: The liver stiffness values on 2D-SWE can be affected by both fibrosis and necroinflammatory grade and can provide excellent diagnostic performance in evaluating the fibrosis stage in various pediatric liver diseases. However, clinicians should be mindful of potential confounders, such as necroinflammatory activity or transaminase level, when performing 2D-SWE measurements for liver fibrosis staging.

Factors associated with neurodevelopment in preterm infants with systematic inflammation.

BACKGROUND: Several studies have suggested that adverse neurodevelopment could be induced by systemic inflammation in preterm infants. We aimed to investigate whether preterm infants with systemic inflammation would have impaired neurodevelopment and which biomarkers and neurophysiologic studies during inflammation are associated with poor neurodevelopment. METHODS: This prospective cohort study enrolled infants born before 30 weeks of gestation or with birth weight < 1250 g. Infants were grouped according to the presence of systemic inflammation: Control (no inflammation, n = 49), I (systemic inflammation, n = 45). Blood and cerebrospinal fluid samples for markers of brain injury and inflammation were collected and amplitude-integrated electroencephalography (aEEG) was performed within 4 h of septic workup. We evaluated aEEG at 35 weeks postmenstrual age (PMA), head circumference at 36 weeks PMA, and brain MRI at discharge. The Bayley Scales of Infant and Toddler Development III (Bayley-III) was performed at a corrected age (CA) of 18 months. RESULTS: The I group had more white matter injuries (2 vs. 26.7%, Control vs. I, respectively) at the time of discharge, lower brain functional maturation (9.5 vs. 8), and smaller head size (z-score - 1.45 vs. -2.12) at near-term age and poorer neurodevelopment at a CA of 18 months than the control (p < 0.05). Among the I group, the proportion of immature neutrophils (I/T ratios) and IL-1 beta levels in the CSF were associated with aEEG measures at the day of symptom onset (D0). Seizure spike on aEEG at D0 was significantly correlated with motor and social-emotional domains of Bayley-III (p < 0.05). The I/T ratio and CRP and TNF-α levels of blood at D0, white matter injury on MRI at discharge, head circumference and seizure spikes on aEEG at near-term age were associated with Bayley-III scores at a CA of 18 months. CONCLUSIONS: Systemic inflammation induced by clinical infection and NEC are associated with neurodevelopmental impairment in preterm infants. The seizure spike on aEEG, elevated I/T ratio, CRP, and plasma TNF-alpha during inflammatory episodes are associated with poor neurodevelopment.