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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

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.To develop and evaluate a deep-learning algorithm for measurement of LLD on radiographs.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.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.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.

To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children.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/mThe algorithm after transfer learning showed better performance than the algorithm before transfer learning for all body compositions (p0.001). 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.The developed model with transfer learning enabled accurate and fully automated segmentation of multiple tissues on whole-body CT scans in children.• 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.

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.

Background/aim Previous reports have indicated that increased expression of Jagged-1 (JAG1) may predict chemotherapy response and poor prognosis for patients with recurrent or metastatic colorectal cancer (CRC). This study aimed to investigate the clinical impact of JAG1 expression level in patients with CRC, including recurrence, especially in those diagnosed with lymph node-positive stage III CRC who underwent complete resection and appropriate adjuvant chemotherapy. Patients and methods All patients were enrolled through a retrospective chart review, and only those for whom the clinical course and all clinical information were adequately determined according to the inclusion criteria were selected for retrospective review through medical records. Immunohistochemical staining of JAG1 was performed using paraffin-embedded tissue. JAG1 expression was determined by scoring for staining intensity and percentage of positively stained cells; the final JAG1 score was determined as the sum of both scores. Results Sixteen patients who experienced relapse and 15 without (for over 3 years) were selected. The protein expression level of JAG1 showed a tendency for being lower in the group without recurrence, although not statistically significantly (p=0.083); however, the mean JAG1 expression score was significantly lower in the group without recurrence (1.53 vs. 3.19; p=0.004). The patients were divided into two groups with low and high JAG1 expression. The results showed that high JAG1 expression was significantly associated with recurrence of stage III CRC (p=0.029). Conclusion The expression of JAG1 may be a potential novel biomarker for predicting CRC recurrence.

Purpose This study aimed to assess the feasibility of contrast-enhanced ultrasound (CEUS) for the diagnosis of acute pyelonephritis (APN) in pediatric patients with febrile urinary tract infection (UTI). Materials and methods Between March 2019 and January 2021, study participants with suspected UTI were assessed for APN using ultrasound. Parenchymal echogenicity changes, renal pelvis dilatation, and the presence of a focal suspected lesion were assessed using conventional grayscale ultrasound. The presence and location of a decreased perfusion area were evaluated using color Doppler ultrasound (CDUS) and CEUS. Agreement between each ultrasound examination and a 99mTc‒dimercaptosuccinic acid (DMSA) scan was assessed using the κ value, and the most visible period of the lesion was evaluated using CEUS. Results This study enrolled 21 participants (median age, 8.0 months; range, 2.0–61.0 months) with isolated urinary tract pathogens. Five increased parenchymal echotextures (11.9%) and 14 renal pelvic dilatations (33.3%) were confirmed, but no focal lesions were detected on the grayscale images. CDUS and CEUS showed decreased local perfusion suggestive of APN in two and five kidneys, respectively. DMSA scan showed substantial agreement with CEUS findings (κ = 0.80, P = 0.010), but other grayscale and CDUS findings did not agree with DMSA scan results (P > 0.05). All lesions were best observed in the late parenchymal phase on CEUS. Conclusion CEUS can reveal renal perfusion defects in pediatric patients with suspected APN without radiation exposure or sedation; therefore, CEUS may be a feasible and valuable diagnostic technique.

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 P 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.

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.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.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.Deep learning reconstruction can reduce noise and truncation artifacts and improve lesion conspicuity and overall image quality in pediatric T2-weighted brain MRI.

Esophageal balloon dilatation is an effective treatment for anastomotic strictures, but the factors affecting the outcome of dilatation remain unclear.To investigate the predictive factors of esophageal balloon dilatation outcome in children with anastomotic stricture after esophageal atresia repair.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.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).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.

Appropriate adjustment of various microclimatic and nutrient conditions can improve crop productivity under greenhouse cultivation. Here, we performed non-targeted profiling to investigate metabolite variation in tomato fruits following different supplementary LED lighting (SL) (red: R; blue: B; and a combination of red and blue light: RB, for 1 h/day and 6 h/day per condition) and mineral nutrient supply (N, K, Mg). The different SL treatments affected metabolite variation in tomato fruits more significantly than mineral nutrients. Importantly, regulating the SL period induced a more distinct metabolite composition in tomato fruits than that induced by varying the SL sources. Tomato fruits cultivated under RB for 6 h/day had a relatively higher content of sugar derivatives, especially sucrose, thus influencing the gustatory characteristics of the fruit. In contrast, fruit from tomatoes cultivated under RB for 1 h/day showed a distinctive increase in the abundance of amino acids, organic acids, and several secondary metabolites, adding to its nutritional quality. Intriguingly, the different mineral supplements elicited discriminant metabolic variation in tomato fruits cultivated under reduced levels of mineral supply; however, the effects were insignificant under enhanced levels of mineral supply. Specifically, reduced level in nitrogen supply resulted in a lower abundance of amino acids, whereas reduced level in potassium supply increased metabolite levels including amino acids, sugars, and fatty acids in tomato fruits. In the present study, we employed a non-targeted metabolomics approach to unravel the effects of microclimatic parameters and mineral nutrients on fruit quality parameters of tomato plants, by which regulating the period of SL and reducing the potassium concentration were suggested to improve different nutritional qualities.

OBJECTIVES This study aimed to evaluate the usefulness of deep learning-based image conversion to improve the reproducibility of computed tomography (CT) radiomics features. MATERIALS AND METHODS This study was conducted using an abdominal phantom with liver nodules. We developed an image conversion algorithm using a residual feature aggregation network to reproduce radiomics features with CT images under various CT protocols and reconstruction kernels. External validation was performed using images from different scanners, consisting of 8 different protocols. To evaluate the variability of radiomics features, regions of interest (ROIs) were drawn by targeting the liver parenchyma, vessels, paraspinal area, and liver nodules. We extracted 18 first-order, 68 second-order, and 688 wavelet radiomics features. Measurement variability was assessed using the concordance correlation coefficient (CCC), compared with the ground-truth image. RESULTS In the ROI-based analysis, there was an 83.3% improvement of CCC (80/96; 4 ROIs with 3 categories of radiomics features and 8 protocols) in synthetic images compared with the original images. Among them, the 56 CCC pairs showed a significant increase after image synthesis. In the radiomics feature-based analysis, 62.0% (3838 of 6192; 774 radiomics features with 8 protocols) features showed increased CCC after image synthesis, and a significant increase was noted in 26.9% (1663 of 6192) features. In particular, the first-order feature (79.9%, 115/144) showed better improvement in terms of the reproducibility of radiomics feature than the second-order (59.9%, 326/544) or wavelet feature (61.7%, 3397/5504). CONCLUSIONS Our study demonstrated that a deep learning model for image conversion can improve the reproducibility of radiomics features across various CT protocols, reconstruction kernels, and CT scanners.

To investigate the feasibility and safety of transradial cerebral angiography (TRCA) in comparison to transfemoral cerebral angiography (TFCA) in the pediatric population.We retrospectively reviewed pediatric patients who underwent TRCA between January 2019 and July 2020. Patients with TFCA experience were examined. Pre- and post-procedural Doppler ultrasonography was performed to evaluate TRCA complications. To evaluate differences in radiation exposure between TRCA and TFCA, we investigated the radiation dose, dose area product (DAP), fluoroscopy time, and examination time.Thirty-five patients (mean age, 13.8 years; 22 male) underwent TRCA, with 18 (mean age, 13.0 years; 10 male) experiencing TFCA. TRCA was successful in all cases without technical failure. Radiation exposure including radiation dose and DAP were significantly higher in the TRCA group (474.1 mGy and 8299.6 μGy mAlthough TRCA is a technically feasible and safe method in pediatric patients, high radiation exposure to TFCA must be considered. Therefore, it should be considered as an alternative method in those with unfavorable clinical situations for performing TFCA.Level 4, Case Series.

This study aimed to evaluate a deep learning model for generating synthetic contrast-enhanced CT (sCECT) from non-contrast chest CT (NCCT). A deep learning model was applied to generate sCECT from NCCT. We collected three separate data sets, the development set (n = 25) for model training and tuning, test set 1 (n = 25) for technical evaluation, and test set 2 (n = 12) for clinical utility evaluation. In test set 1, image similarity metrics were calculated. In test set 2, the lesion contrast-to-noise ratio of the mediastinal lymph nodes was measured, and an observer study was conducted to compare lesion conspicuity. Comparisons were performed using the paired t-test or Wilcoxon signed-rank test. In test set 1, sCECT showed a lower mean absolute error (41.72 vs 48.74; P P P P P P ≤ .001). Synthetic CECT generated from NCCT improves the depiction of mediastinal lymph nodes.

Background: The guideline for managing pediatric thyroid nodules has not been established. We investigated the appropriateness of the Korean Society of Thyroid Radiology (KSThR) guidelines and the ...

To evaluate the intraobserver and interobserver reliability of gallbladder polyp measurements using transabdominal US and the factors that affect reliability. 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. 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 (

This study compared the accuracy of a new MALDI-TOF mass spectrometry system, ASTA MicroIDSys system, with that of MALDI Biotyper system for the identification of reference and clinical bacterial and yeast strains. The identification accuracy of the 2 systems was compared using a total of 406 strains comprising 142 aerobic and 180 anaerobic bacterial strains and 84 yeast strains. The genus and species identification rates were 98.0% and 89.4% using MicroIDSys and 96.1% and 89.4% using Biotyper, respectively. The species identification rates of MicroIDSys and Biotyper for aerobic bacteria were 93.0% and 97.2%, respectively, and those for anaerobic bacteria were 85.6% and 81.7%, respectively. The accuracy of yeast identification at the species level was 91.7% using MicroIDSys and 92.9% using Biotyper. These findings indicate that MicroIDSys could be useful for the accurate identification of bacteria and yeast in clinical microbiology laboratories.