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.

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.

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.

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.

Performance assessment of ASTA MicroIDSys, a new matrix assisted laser desorption ionization-time of flight mass spectrometry system, for identification of viridans group streptococci.

The performance of the ASTA MicroIDSys system (ASTA), a new matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) system, was evaluated for the identification of viridans group streptococci (VGS) and compared with the results obtained with the Bruker Biotyper system (Bruker Daltonics). A total of 106 Streptococcus reference strains belonging to 24 species from the bacterial strain bank was analyzed using the two MALDI-TOF MS systems. Of the 106 reference strains tested, ASTA MicroIDSys and Bruker Biotyper correctly identified 84.9% and 81.1% at the species level, 100% and 97.2% at the group level and 100% and 98.1% at the genus level, respectively. The difference between the two systems was not statistically significant (P = 0.289). Out of 24 species, 13 species were accurately identified to the species level with 100% accurate identification rates with both systems. The accurate identification rates at the species level of ASTA MicroIDSys and Bruker Biotyper were 100% and 87.5% for the S. anginosus group; 78.4% and 73.5% for the S. mitis group; 91.7% and 91.7% for the S. mutans group; and 100% and 100% for the S. salivarius group, respectively. The ASTA MicroIDSys showed an identification performance equivalent to that of the Bruker Biotyper for VGS. Therefore, it would be useful for the identification of VGS strains in clinical microbiology laboratories.

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.

Evaluation of frequency-selective non-linear blending technique on brain CT in postoperative children with Moyamoya disease.

OBJECTIVE: To evaluate whether a frequency-selective non-linear blending (BC) technique can improve tissue contrast and infarct detection on non-enhanced brain CT (NECT) in postoperative Moyamoya (MMD) patients. MATERIALS AND METHODS: From January 2010 to December 2017, 33 children (13boys and 20girls; mean age 9.1±3.4 years) with MMD postoperatively underwent NECT followed by diffusion MRI. We compared the contrast-to-noise ratio (CNR) between gray matter (GM) and white matter (WM) in NECT and BC images and the CNR between the infarct lesion and adjacent normal-appearing brain in NECT and BC images using a paired t-test. We assessed image noise, GM-WM differentiation, artifacts, and overall quality using a Wilcoxon signed rank test. A McNemar two-tailed test was conducted to compare the diagnostic accuracy of infarct detection. RESULTS: The CNR between GM and WM and the CNR of the infarct was better in BC images than in NECT images (3.9±1.0 vs. 1.8±0.6, P<0.001 and 3.6±0.3 vs. 1.9±0.2, P<0.001), with no difference in overall image quality observed. The sensitivity and specificity of infarct detection were 55.0% and 76.9% using NECT, and 70.0% and 69.2% using BC technique. The diagnostic accuracy of NECT and BC technique was 63.6% (21/33) and 69.7% (23/33), respectively. CONCLUSION: This study showed that the BC technique improved CNR and maintained image quality. This technique may also be used to identify ischemic brain changes in postoperative MMD patients by improving the CNR of the infarct lesion.

Intraobserver and interobserver reliability in sonographic size measurements of gallbladder polyps.

OBJECTIVES: To evaluate the intraobserver and interobserver reliability of gallbladder polyp measurements using transabdominal US and the factors that affect reliability. METHODS: From November 2017 to February 2018, two radiologists measured the maximum diameter of 91 gallbladder polyps using transabdominal US. Intraobserver and interobserver agreement were determined using 95% Bland-Altman limits of agreement and intraclass correlation coefficients (ICCs). The effects of image settings, polyp location, and polyp size were evaluated by comparing ICCs using z tests. RESULTS: The intraobserver agreement rates were 0.960 (95% confidence interval [CI], 0.939-0.973) for observer 1 and 0.962 (95% CI, 0.943-0.975) for observer 2. The ICCs between the two observers were 0.963 (95% CI, 0.926-0.979) for the first measurement and 0.973 (95% CI, 0.950-0.984) for the second measurement. The 95% limits of agreement on repeated measurements were 22.3-25.2% of the mean, and those between the two observers were 25.5-34.2% of the mean. ICCs for large polyps (≥ 5 mm) were significantly higher than those for small polyps (< 5 mm). There were no significant differences in the ICCs between image settings and polyp location. CONCLUSIONS: Polyp size measurements using transabdominal US are highly repeatable and reproducible. Polyp size significantly affects the reliability of measurement. Diameter changes of approximately less than 25% may fall within the measurement error; this should be considered while interpreting the change in size during follow-up US, especially for small polyps. KEY POINTS: • Gallbladder polyp size measurement using transabdominal US is highly repeatable and reproducible. • Diameter changes of approximately less than 25% should be interpreted carefully, especially in small polyps.

[Pediatric Hip Disorders]. / 소아 ê³ ê´€ì ˆ 질환.

Developmental dysplasia of the hip is a condition characterized by hip joint instability due to acetabular dysplasia in infancy, necessitating precise ultrasound examination. Legg-Calvé-Perthes disease is caused by a temporary disruption in blood flow to the femoral head during childhood, progressing through avascular, fragmentation, re-ossification, and residual stages. Slipped capital femoral epiphysis is a condition where the femoral head shifts medially along the epiphyseal line during adolescence due to stress, such as weight-bearing. Differentiating between transient hip synovitis and septic arthritis may require joint fluid aspiration. Osteomyelitis can be associated with soft tissue edema and osteolysis. When multiple lesions are present, it is essential to distinguish between Langerhans cell histiocytosis and metastatic neuroblastoma. This review will introduce imaging techniques and typical findings for these conditions.

Feasibility of a deep learning artificial intelligence model for the diagnosis of pediatric ileocolic intussusception with grayscale ultrasonography.

PURPOSE: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. METHODS: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. RESULTS: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. CONCLUSION: The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Novel synthesis of leucoside by enzymatic hydrolysis of tea seed extract.

BACKGROUND: The application of tea seed extract (TSE) has been widely investigated owing to its biological activities. In this paper, two flavonol triglycosides found in TSE, camelliaside A (CamA) and camelliaside B (CamB), were subjected to hydrolysis in the presence of three commercial enzyme complexes of the Pectinex® series, 5XL, XXL and Ultra SP-L (Ultra). RESULTS: XXL and 5XL induced stepwise deglycosylation of CamA and CamB to yield kaempferol diglycoside (nicotiflorin), kaempferol monoglycoside (astragalin) and kaempferol, while Ultra produced an additional new compound (1) that had not been observed in earlier studies. Upon hydrolysis of isolated CamA and CamB, compound (1) was obtained only from CamB. Both the molecular ion peak in liquid chromatography/mass spectrometry and the ¹H and ¹³C nuclear magnetic resonance spectra of (1) isolated by Ultra-induced hydrolysis of TSE indicated that (1) was kaempferol 3-O-ß-xylopyranosyl (1 → 2)-ß-glucopyranoside (leucoside), formed by selective hydrolysis of the rhamnosyl moiety of CamB. CONCLUSION: Pure leucoside can be prepared by enzymatic partial hydrolysis of TSE. This is the first study to address the synthesis of pure leucoside from a natural source.

Primary renal lymphoma presenting as renal failure: A case report and review of literature from 1989.

BACKGROUND: Primary renal lymphoma (PRL) is extremely rare with an incidence of 0.7% among extranodal lymphomas. Occult renal lymphoma, which mimics medical renal disease and bilateral renal involvement, presents a diagnostic challenge to nephrologists and radiologists as the clinical and radiological findings are mostly non-specific or inconclusive. Acute kidney injury (AKI) is not an uncommon finding in renal infiltration due to malignant lymphoma. However, only 14% of cases are detected before death, and the low diagnostic rate may be due to the non-specific clinical manifestations of renal involvement, with only 0.5% of these cases presenting with AKI. Moreover, PRL is difficult to diagnose based on clinical, biochemical, and radiologic features, especially, in the case of bilateral diffuse involvement. CASE SUMMARY: Herein, we report a 74-year-old woman with primary diffuse large B-cell lymphoma who presented with AKI diagnosed by ultrasound-guided needle biopsy. We also report the clinicopathologic findings of 121 PRL cases reported since 1989, by conducting a literature review of published cases. CONCLUSION: A timely renal biopsy provides the most expedient means of establishing the diagnosis. Thus, early identification of the disease by the clinician facilitates early diagnosis toward effective treatment.

Expression of Three Clones of PD-L1 in Lung Cancer: A Single-center Experience.

BACKGROUND/AIM: Programmed cell death ligand 1 (PD-L1) is an immune checkpoint protein involved in immune evasion of malignant tumors. Confirmation of PD-L1 expression in non-small cell lung cancer (NSCLC) is necessary for the determination of immunotherapy using immune checkpoint inhibitors (ICIs). PDL-1 expression is currently analyzed by immunohistochemistry and is the only available biomarker that can guide the treatment of NSCLC using ICIs. The present study was conducted to compare the expression of three different commercial clones of PD-L1 in order for immunohistochemistry (IHC) for these clones to become more reliable for surgical pathologists. MATERIALS AND METHODS: This study examined the expression of PD-L1 in 76 cases of resected lung cancer using IHC. Three clones were examined: SP263, SP142, and 22C3PharmDx, which are commercially approved for quantifying PD-L1 expression in lung cancer. RESULTS: Of the 76 patients whose samples were evaluated for PD-L1 using the IHC 22C3pharmDx assay, 19 (25.0%) had a tumor proportion score (TPS) of ≥50% and 41 (53.9%) had a PD-L1 TPS of ≥1%. Furthermore, using the SP263, 48.7% had a TPS of ≥1% and 18.4% of >50%. The SP142 assay was used to evaluate tumor cells (TCs) and immune cells (ICs). Twenty (26.3%) cases were positive for TCs and 25 (32.9%) were reactive for ICs. CONCLUSION: These three commercial PD-L1 clones are comparable for detecting primary targets for anti-tumor immunotherapies. Careful evaluation by a pathologist is necessary to minimize misinterpretation errors.