Development and Validation of a Biparametric MRI Deep Learning Radiomics Model with Clinical Characteristics for Predicting Perineural Invasion in Patients with Prostate Cancer.

RATIONALE AND OBJECTIVES: Perineural invasion (PNI) is an important prognostic biomarker for prostate cancer (PCa). This study aimed to develop and validate a predictive model integrating biparametric MRI-based deep learning radiomics and clinical characteristics for the non-invasive prediction of PNI in patients with PCa. MATERIALS AND METHODS: In this prospective study, 557 PCa patients who underwent preoperative MRI and radical prostatectomy were recruited and randomly divided into the training and the validation cohorts at a ratio of 7:3. Clinical model for predicting PNI was constructed by univariate and multivariate regression analyses on various clinical indicators, followed by logistic regression. Radiomics and deep learning methods were used to develop different MRI-based radiomics and deep learning models. Subsequently, the clinical, radiomics, and deep learning signatures were combined to develop the integrated deep learning-radiomics-clinical model (DLRC). The performance of the models was assessed by plotting the receiver operating characteristic (ROC) curves and precision-recall (PR) curves, as well as calculating the area under the ROC and PR curves (ROC-AUC and PR-AUC). The calibration curve and decision curve were used to evaluate the model's goodness of fit and clinical benefit. RESULTS: The DLRC model demonstrated the highest performance in both the training and the validation cohorts, with ROC-AUCs of 0.914 and 0.848, respectively, and PR-AUCs of 0.948 and 0.926, respectively. The DLRC model showed good calibration and clinical benefit in both cohorts. CONCLUSION: The DLRC model, which integrated clinical, radiomics, and deep learning signatures, can serve as a robust tool for predicting PNI in patients with PCa, thus aiding in developing effective treatment strategies.

Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia.

AIM: To develop a decision-support tool for predicting extubation failure (EF) in neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms. METHODS: A dataset of 284 BPD neonates on mechanical ventilation was used to develop predictive models via machine-learning algorithms, including extreme gradient boosting (XGBoost), random forest, support vector machine, naïve Bayes, logistic regression, and k-nearest neighbor. The top three models were assessed by the area under the receiver operating characteristic curve (AUC), and their performance was tested by decision curve analysis (DCA). Confusion matrix was used to show the high performance of the best model. The importance matrix plot and SHapley Additive exPlanations values were calculated to evaluate the feature importance and visualize the results. The nomogram and clinical impact curves were used to validate the final model. RESULTS: According to the AUC values and DCA results, the XGboost model performed best (AUC = 0.873, sensitivity = 0.896, specificity = 0.838). The nomogram and clinical impact curve verified that the XGBoost model possessed a significant predictive value. The following were predictive factors for EF: pO2, hemoglobin, mechanical ventilation (MV) rate, pH, Apgar score at 5 min, FiO2, C-reactive protein, Apgar score at 1 min, red blood cell count, PIP, gestational age, highest FiO2 at the first 24 h, heart rate, birth weight, pCO2. Further, pO2, hemoglobin, and MV rate were the three most important factors for predicting EF. CONCLUSIONS: The present study indicated that the XGBoost model was significant in predicting EF in BPD neonates with mechanical ventilation, which is helpful in determining the right extubation time among neonates with BPD to reduce the occurrence of complications.

Expression of lncRNAs in children with pancreaticobiliary maljunction: functional analysis and potential biomarkers.

Introduction: Pancreaticobiliary maljunction (PBM) leads to higher rates of complications, including cholangitis, pancreatitis, and malignancies. The aim of the present study was to investigate the expression profile of long non-coding RNAs (lncRNAs) and their potential role as biomarkers in children with pancreaticobiliary maljunction. Material and methods: The differential expression of lncRNAs and messenger RNA (mRNAs) from pediatric patients with pancreaticobiliary maljunction and control subjects was analyzed using a commercial microarray and later validated with qRT-PCR. The potential biological functions of differentially expressed genes were explored based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment. The ability of potential lncRNA biomarkers to predict pancreaticobiliary maljunction was assessed based on the area under the receiver operating characteristic curve (AUC). Results: There were 2915 mRNAs and 173 lncRNAs upregulated, and 2121 mRNAs and 316 lncRNAs downregulated in PBM cases compared to controls. The enriched Gene Ontology categories associated with differentially expressed mRNAs were extracellular matrix, extracellular region, and kinetochore. The most enriched Kyoto Encyclopedia pathway was protein digestion and absorption, which was associated with cancer and PI3K-Akt signaling. Analysis of cis- and trans-target genes predicted that a single lncRNA was able to regulate several mRNAs. The qRT-PCR results for NR_110876, NR_132344, XR_946886, and XR_002956345 were consistent with the microarray results, and the difference was statistically significant for NR_132344, XR_946886, and XR_002956345 (p < 0.05). AUC was significant only for XR_946886 (0.837, p < 0.001). Conclusions: Our results implicate lncRNAs in common bile duct pathogenesis in PBM, and they identify XR_946886 as a potential biomarker for the disease.

Development of a machine learning model and nomogram to predict seizures in children with COVID-19: a two-center study.

OBJECTIVE: This study aimed to use machine learning to evaluate the risk factors of seizures and develop a model and nomogram to predict seizures in children with coronavirus disease 2019 (COVID-19). MATERIAL AND METHODS: A total of 519 children with COVID-19 were assessed to develop predictive models using machine learning algorithms, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR). The performance of the models was assessed using area under the receiver operating characteristic curve (AUC) values. Importance matrix plot and SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and to show the visualization results. The nomogram and clinical impact curve were used to validate the final model. RESULTS: Two hundred and seventeen children with COVID-19 had seizures. According to the AUC, the RF model performed the best. Based on the SHAP values, the top three most important variables in the RF model were neutrophil percentage, cough and fever duration. The nomogram and clinical impact curve also verified that the RF model possessed significant predictive value. CONCLUSIONS: Our research indicates that the RF model demonstrates excellent performance in predicting seizures, and our novel nomogram can facilitate clinical decision-making and potentially offer benefit for clinicians to prevent and treat seizures in children with COVID-19.

Development of a CT-based radiomics-clinical model to diagnose acute pancreatitis on nonobvious findings on CT in children with pancreaticobiliary maljunction.

OBJECTIVES: Since neither abdominal pain nor pancreatic enzyme elevation is specific for acute pancreatitis (AP), the diagnosis of AP in patients with pancreaticobiliary maljunction (PBM) may be challenging when the pancreas appears normal or nonobvious on CT. This study aimed to develop a quantitative radiomics-based nomogram of pancreatic CT for identifying AP in children with PBM who have nonobvious findings on CT. METHODS: PBM patients with a diagnosis of AP evaluated at the Children's Hospital of Soochow University from June 2015 to October 2022 were retrospectively reviewed. The radiological features and clinical factors associated with AP were evaluated. Based on the selected variables, multivariate logistic regression was used to construct clinical, radiomics, and combined models. RESULTS: Two clinical parameters and 6 radiomics characteristics were chosen based on their significant association with AP, as demonstrated in the training (area under curve [AUC]: 0.767, 0.892) and validation (AUC: 0.757, 0.836) datasets. The radiomics-clinical nomogram demonstrated superior performance in both the training (AUC, 0.938) and validation (AUC, 0.864) datasets, exhibiting satisfactory calibration (P > .05). CONCLUSIONS: Our radiomics-based nomogram is an accurate, noninvasive diagnostic technique that can identify AP in children with PBM even when CT presentation is not obvious. ADVANCES IN KNOWLEDGE: This study extracted imaging features of nonobvious pancreatitis. Then it developed and evaluated a combined model with these features.

Serum tRNA-derived fragments as potential biomarkers in children with acute intussusception.

Introduction: Pediatric intussusception is one of the most common causes of bowel obstruction in the pediatric population. Affected children have one section of the intestine sliding into the adjacent section. Intestinal ischemia-reperfusion injury (I/R) can occur during pediatric intussusception, and any delay in diagnosis or treatment can lead to loss of intestinal viability that requires bowel resection. The aim of the present study was to investigate whether transfer ribonucleic acid (tRNA)-derived fragments (tRFs) can serve as candidate biomarkers for pediatric intussusception. Material and methods: Using high-throughput sequencing technology, we identified differentially expressed tRFs, and ultimately selected three tRFs to establish a signature as a predictive biomarker of pediatric intussusception. Selection of these three upregulated genes was verified using quantitative reverse-transcription polymerase chain reaction (qRT-PCR). We conducted receiver operator characteristic (ROC) curve analysis to evaluate the predictive accuracy of the selected genes for pediatric intussusception. Results: We detected 732 tRFs and tRNA-derived stress-induced RNA (tiRNAs), 1705 microRNAs (miRNAs), 52 differentially expressed miRNAs, and 34 differentially expressed tRFs and tiRNAs between patients and controls. Compared with controls, we found 33 upregulated miRNAs, 24 upregulated tRFs and tiRNAs, 19 downregulated miRNAs, and 10 downregulated tRFs and tiRNAs in children with intussusception. Using qPCR, the expression trends of tRF-Leu-TAA-006, tRF-Gln-TTG-033 and tRF-Lys-TTT-028 were consistent with the sequencing results. AUCs of tRF-Leu-TAA-006, tRF-Gln-TTG-033 and tRF-Lys-TTT-028 were 0.984, 0.970 and 0.837, respectively. Conclusions: Circulating tRF-Leu-TAA-006, tRF-Gln-TTG-033 and tRF-Lys-TTT-028 expression might be a novel potential biomarker for diagnosis of pediatric intussusception.

Clinical presentations and outcomes of pancreaticobiliary maljunction in different pediatric age groups.

BACKGROUND: Pancreaticobiliary maljunction (PBM) is a congenital defect, with risk of developing various pancreaticobiliary and hepatic complications. The presentations of PBM in children and adults are believed to be different, but studies on PBM children of different age groups are limited. This study was to evaluate clinicopathologic characteristics and outcomes in PBM children of different ages. METHODS: A total of 166 pediatric patients with PBM were reviewed retrospectively. Clinicopathological, imaging, laboratory, surgical, and follow-up data were collected and analyzed. The patients were divided into three age groups, namely, group A (< 1 year, n = 31), group B (1-3 years, n = 63), and group C (> 3 years, n = 72). RESULTS: The major clinical manifestation was jaundice in group A and abdominal pain and vomiting in groups B and C. Acute pancreatitis was more often seen in group C than group A. The length of common channel was significantly longer in group C than group A, while the maximum diameter of common bile duct in group C was smaller than that in group A. Cholangitis and cholecystitis were more commonly performed in groups B and C, while hepatic fibrosis in group A. Whether preoperatively or postoperatively, group C was more likely to have elevated serum amylase, while groups A and B were more likely to present with abnormal liver function indicators, including the increase of aspartate transaminase, alanine transaminase, and gamma-glutamyl transpeptidase. CONCLUSION: Presentation of PBM varies among different pediatric age groups, thus suggesting that targeted management should be carried out according to these differences.

Identification and validation of radiomic features from computed tomography for preoperative classification of neuroblastic tumors in children.

BACKGROUND: To identify radiomic features that can predict the pathological type of neuroblastic tumor in children. METHODS: Data on neuroblastic tumors in 104 children were retrospectively analyzed. There were 14 cases of ganglioneuroma, 24 cases of ganglioneuroblastoma, and 65 cases of neuroblastoma. Stratified sampling was used to randomly allocate the cases into the training and validation sets in a ratio of 3:1. The maximum relevance-minimum redundancy algorithm was used to identify the top 10 of two clinical features and 851 radiomic features in portal venous-phase contrast-enhanced computed tomography images. Least absolute shrinkage and selection operator regression was used to classify tumors in two binary steps: first as ganglioneuroma compared to the other two types, then as ganglioneuroblastoma compared to neuroblastoma. RESULTS: Based on 10 clinical-radiomic features, the classifier identified ganglioneuroma compared to the other two tumor types in the validation dataset with sensitivity of 100.0%, specificity of 81.8%, and an area under the receiver operating characteristic curve (AUC) of 0.875. The classifier identified ganglioneuroblastoma versus neuroblastoma with a sensitivity of 83.3%, a specificity of 87.5%, and an AUC of 0.854. The overall accuracy of the classifier across all three types of tumors was 80.8%. CONCLUSION: Radiomic features can help predict the pathological type of neuroblastic tumors in children.

Using machine learning models to predict the surgical risk of children with pancreaticobiliary maljunction and biliary dilatation.

PURPOSE: To develop machine learning (ML) models to predict the surgical risk of children with pancreaticobiliary maljunction (PBM) and biliary dilatation. METHODS: The subjects of this study were 157 pediatric patients who underwent surgery for PBM with biliary dilatation between January, 2015 and August, 2022. Using preoperative data, four ML models were developed, including logistic regression (LR), random forest (RF), support vector machine classifier (SVC), and extreme gradient boosting (XGBoost). The performance of each model was assessed via the area under the receiver operator characteristic curve (AUC). Model interpretations were generated by Shapley Additive Explanations. A nomogram was used to validate the best-performing model. RESULTS: Sixty-eight patients (43.3%) were classified as the high-risk surgery group. The XGBoost model (AUC = 0.822) outperformed the LR (AUC = 0.798), RF (AUC = 0.802) and SVC (AUC = 0.804) models. In all four models, enhancement of the choledochal cystic wall and an abnormal position of the right hepatic artery were the two most important features. Moreover, the diameter of the choledochal cyst, bile duct variation, and serum amylase were selected as key predictive factors by all four models. CONCLUSIONS: Using preoperative data, the ML models, especially XGBoost, have the potential to predict the surgical risk of children with PBM and biliary dilatation. The nomogram may provide surgeons early warning to avoid intraoperative iatrogenic injury.

Development of a simplified model and nomogram in preoperative diagnosis of pediatric chronic cholangitis with pancreaticobiliary maljunction using clinical variables and MRI radiomics.

OBJECTIVE: The aim of this study was to develop a model that combines clinically relevant features with radiomics signature based on magnetic-resonance imaging (MRI) for diagnosis of chronic cholangitis in pancreaticobiliary maljunction (PBM) children. METHODS: A total of 144 subjects from two institutions confirmed PBM were included in this study. Clinical characteristics and MRI features were evaluated to build a clinical model. Radiomics features were extracted from the region of interest manually delineated on T2-weighted imaging. A radiomics signature was developed by the selected radiomics features using the least absolute shrinkage and selection operator and then a radiomics score (Rad-score) was calculated. We constructed a combined model incorporating clinical factors and Rad-score by multivariate logistic regression analysis. The combined model was visualized as a radiomics nomogram to achieve model visualization and provide clinical utility. Receiver operating curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance. RESULTS: Jaundice, protein plug, and ascites were selected as key clinical variables. Eight radiomics features were combined to construct the radiomics signature. The combined model showed superior predictive performance compared with the clinical model alone (AUC in the training cohort: 0.891 vs. 0.767, the validation cohort: 0.858 vs. 0.731), and the difference was significant (p = 0.002, 0.028) in the both cohorts. DCA confirmed the clinical utility of the radiomics nomogram. CONCLUSION: The proposed model that combines key clinical variables and radiomics signature is helpful in the diagnosis of chronic cholangitis in PBM children.

Prediction of post-operative acute pancreatitis in children with pancreaticobiliary maljunction using machine learning model.

PURPOSE: This study aimed to develop a prediction model to identify risk factors for post-operative acute pancreatitis (POAP) in children with pancreaticobiliary maljunction (PBM) by pre-operative analysis of patient variables. METHODS: Logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGBoost) models were established using the prospectively collected databases of patients with PBM undergoing surgery which was reviewed in the period comprised between August 2015 and August 2022, at the Children's Hospital of Soochow University. Primarily, the area beneath the receiver-operating curves (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. The model was finally validated using the nomogram and clinical impact curve. RESULTS: In total, 111 children with PBM met the inclusion criteria, and 21 children suffered POAP. In the validation dataset, LR models showed the highest performance. The risk nomogram and clinical effect curve demonstrated that the LR model was highly predictive. CONCLUSION: The prediction model based on the LR with a nomogram could be used to predict the risk of POAP in patients with PBM. Protein plugs, age, white blood cell count, and common bile duct diameter were the most relevant contributing factors to the models.

Using machine learning models to predict acute pancreatitis in children with pancreaticobiliary maljunction.

PURPOSE: To develop a model to identify risk factors and predictors of acute pancreatitis in children with pancreaticobiliary maljunction (PBM). METHODS: We screened consecutive PBM patients treated at two centers between January, 2015 and July, 2021. For machine learning, the cohort was divided randomly at a 6:4 ratio to a training dataset and a validation dataset. Three parallel models were developed using logistic regression (LR), a support vector machine (SVM), and extreme gradient boosting (XGBoost), respectively. Model performance was judged primarily based on the area under the receiver operating curves (AUC). RESULTS: A total of 99 patients were included in the analysis, 17 of whom suffered acute pancreatitis and 82 did not. The XGBoost (AUC = 0.814) and SVM (AUC = 0.813) models produced similar performance in the validation dataset; both outperformed the LR model (AUC = 0.805). Based on the SHapley Additive exPlanation values, the most important variable in both the XGBoost and SVM models were age, protein plugs, and white blood cell count. CONCLUSIONS: Machine learning models, especially XGBoost and SVM, could be used to predict acute pancreatitis in children with PBM. The most important contributing factor to the models were age, protein plugs, and white blood cell count.

Development of an MRI-Based Radiomics-Clinical Model to Diagnose Liver Fibrosis Secondary to Pancreaticobiliary Maljunction in Children.

BACKGROUND: Preoperative diagnosis of liver fibrosis in children with pancreaticobiliary maljunction (PBM) is needed to guide clinical decision-making and improve patient prognosis. PURPOSE: To develop and validate an MR-based radiomics-clinical nomogram for identifying liver fibrosis in children with PBM. STUDY TYPE: Retrospective. POPULATION: A total of 136 patients with PBM from two centers (center A: 111 patients; center B: 25 patients). Cases from center A were randomly divided into training (74 patients) and internal validation (37 patients) sets. Cases from center B were assigned to the external validation set. Liver fibrosis was determined by histopathological examination. FIELD STRENGTH/SEQUENCE: A 3.0 T (two vendors)/T1-weighted imaging and T2-weighted imaging. ASSESSMENT: Clinical factors associated with liver fibrosis were evaluated. A total of 3562 radiomics features were extracted from segmented liver parenchyma. Maximum relevance minimum redundancy and least absolute shrinkage and selection operator were recruited to screen radiomics features. Based on the selected variables, multivariate logistic regression was used to construct the clinical model, radiomics model, and combined model. The combined model was visualized as a nomogram to show the impact of the radiomics signature and key clinical factors on the individual risk of developing liver fibrosis. STATISTICAL TESTS: Mann-Whitney U and chi-squared tests were used to compare clinical factors. P < 0.05 was considered statistically significant in the final models. RESULTS: Two clinical factors and four radiomics features were selected as they were associated with liver fibrosis in the training (AUC, 0.723, 0.927), internal validation (AUC, 0.718, 0.885), and external validation (AUC, 0.737, 0.865) sets. The radiomics-clinical nomogram yielded the best performance in the training (AUC, 0.977), internal validation (AUC, 0.921), and external validation (AUC, 0.878) sets, with good calibration (P > 0.05). DATA CONCLUSION: Our radiomic-based nomogram is a noninvasive, accurate, and preoperative diagnostic tool that is able to detect liver fibrosis in PBM children. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Predictors of Bronchopulmonary Dysplasia in 625 Neonates with Respiratory Distress Syndrome.

OBJECTIVE: This study was designed to investigate the predictors of bronchopulmonary dysplasia in neonates with respiratory distress syndrome. METHODS: This was a single-center retrospective cohort study conducted between 1 January 2015 and 31 December 2020. A total of 625 neonates with respiratory distress syndrome (RDS) were enrolled. Demographic data, clinical presentations, complications and related treatment information were collected and analyzed. We used bivariate and multivariate logistic-regression analyses to determine significant predictors of bronchopulmonary dysplasia (BPD) in RDS neonates. RESULTS: In these 625 neonates, 102 (16.3%) of them developed BPD. Bivariate analysis and multivariate logistic-regression analyses revealed that birthweight, gestational age under 32 weeks, duration of oxygen therapy over 10 days, asphyxia, patent ductus arteriosus, transfusion of red blood cells (packed red blood cells) and surfactant use were significantly associated with the development of BPD. CONCLUSION: Birthweight, gestational age <32 weeks, total duration of oxygen therapy >10 days, asphyxia, patent ductus arteriosus, need for red blood cell infusion, and the use of pulmonary surfactant were important predictors of BPD in neonates with RDS.

Combination of UNet++ and ResNeSt to classify chronic inflammation of the choledochal cystic wall in patients with pancreaticobiliary maljunction.

OBJECTIVES: The aim of this study was to establish an automatic classification model for chronic inflammation of the choledoch wall using deep learning with CT images in patients with pancreaticobiliary maljunction (PBM). METHODS: CT images were obtained from 76 PBM patients, including 61 cases assigned to the training set and 15 cases assigned to the testing set. The region of interest (ROI) containing the choledochal lesion was extracted and segmented using the UNet++ network. The degree of severity of inflammation in the choledochal wall was initially classified using the ResNeSt network. The final classification result was determined per decision rules. Grad-CAM was used to explain the association between the classification basis of the network and clinical diagnosis. RESULTS: Segmentation of the lesion on the common bile duct wall was roughly obtained with the UNet++ segmentation model and the average value of Dice coefficient of the segmentation model in the testing set was 0.839 ± 0.150, which was verified through fivefold cross-validation. Inflammation was initially classified with ResNeSt18, which resulted in accuracy = 0.756, sensitivity = 0.611, specificity = 0.852, precision = 0.733, and area under curve (AUC) = 0.711. The final classification sensitivity was 0.8. Grad-CAM revealed similar distribution of inflammation of the choledochal wall and verified the inflammation classification. CONCLUSIONS: By combining the UNet++ network and the ResNeSt network, we achieved automatic classification of chronic inflammation of the choledoch in PBM patients and verified the robustness through cross-validation performed five times. This study provided an important basis for classification of inflammation severity of the choledoch in PBM patients. ADVANCES IN KNOWLEDGE: We combined the UNet++ network and the ResNeSt network to achieve automatic classification of chronic inflammation of the choledoch in PBM. These results provided an important basis for classification of choledochal inflammation in PBM and for surgical therapy.

Systematic screen of potential circular RNA biomarkers of Hirschsprung's disease.

BACKGROUND: Hirschsprung's disease (HSCR) is a developmental disorder of the enteric nervous system in which enteric ganglia are missing along a portion of the intestine. Aberrant expression of several circular RNAs (circRNAs) has been identified in the disease, but the full range of dysregulated circRNAs and their potential roles in its pathogenesis remain unclear. We used microarray profiling to systematically screen for circRNAs that were differentially expressed in HSCR, and we comprehensively analyzed the potential circRNA-miRNA-mRNA regulatory network to identify molecular mechanisms involved in the disorder. METHODS: We identified circRNAs that were differentially expressed between diseased tissue and paired normal intestinal tissues from patients with HSCR. The most strongly upregulated circRNAs were then validated by quantitative reverse-transcription-PCR (RT-PCR). We also constructed a circRNA-miRNA-mRNA interaction network to determine functional interactions between miRNAs and mRNAs. RESULTS: We identified 17 circRNAs that were upregulated and 10 that were downregulated in HSCR tissue compared with normal tissues. The five circRNAs that showed the greatest upregulation were verified by RT-PCR: hsa_circRNA_092493, hsa_circRNA_101965, hsa_circRNA_103118, hsa_circRNA_103279, and hsa_circRNA_104214. These five circRNAs were successfully adopted to diagnose HSCR based on receiver operating characteristic curves, and they were used to generate a circRNA-miRNA-mRNA network. The network revealed a potential function of the circRNAs as molecular sponges targeting miRNAs and mRNAs in HSCR. CONCLUSIONS: This first-ever systematic dissection of the circRNA profile in HSCR may provide useful insights into improving diagnosis and therapy.

Machine learning-based quantitative analysis of barium enema and clinical features for early diagnosis of short-segment Hirschsprung disease in neonate.

OBJECTIVE: To develop a mathematical model based on a combination of clinical and radiologic features (barium enema) for early diagnosis of short-segment Hirschsprung disease (SHSCR) in neonate. METHODS: The analysis included 54 neonates with biopsy-confirmed SHSCR (the cases) and 59 neonates undergoing barium enema for abdominal symptoms but no Hirschsprung disease (the control). Colon shape features extracted from barium enema images and clinical features were used to develop diagnostic models using support vector machine (SVM) and L2-regularized logistic regression (LR). The training cohort included 32 cases and 37 controls; testing cohort consisted 22 cases and 22 controls. Results were compared to interpretation by 2 radiologists. RESULTS: In the analysis by radiologists, 87 out of 113 cases were correctly classified. Six SHSCR cases were mis-classified into the non-HSCR group. In the remaining 20 cases, radiologists were unable to make a decision. Both the SVM and LR classifiers contained five clinical features and four shape features. The performance of the two classifiers was similar. The best model had 86.36% accuracy, 81.82% sensitivity, and 90.91% specificity. The AUC was 0.9132 for the best-performing SVM classifier and 0.9318 for the best-performing LR classifier. CONCLUSION: A combination of clinical features and colon shape features extracted from barium enemas can be used to improve early diagnosis of SHSCR in neonate.

miR-143 is implicated in growth plate injury by targeting IHH in precartilaginous stem cells.

Precartilaginous stem cells (PCSCs) are able to initiate chondrocyte and bone development. The present study aimed to investigate the role of miR-143 and the underlying mechanisms involved in PCSC proliferation. In a rat growth plate injury model, tissue from the injury site was collected and the expression of miR-143 and its potential targets was determined. PCSCs were isolated from the rabbits' distal epiphyseal growth plate. Cell viability, DNA synthesis, and apoptosis were determined with MTT, BrdU, and flow cytometric analysis, respectively. Real time PCR and western blot were performed to detect the mRNA and protein expression of the indicated genes. Indian hedgehog (IHH) was identified as a target gene for miR-143 with luciferase reporter assay. Decreased expression of miR-143 and increased expression of IHH gene were observed in the growth plate after injury. miR-143 mimics decreased cell viability and DNA synthesis and promoted apoptosis of PCSCs. Conversely, siRNA-mediated inhibition of miR-143 led to increased growth and suppressed apoptosis of PCSCs. Transfection of miR-143 decreased luciferase activity of wild-type IHH but had no effect when the 3'-UTR of IHH was mutated. Furthermore, the effect of miR-143 overexpression was neutralized by overexpression of IHH. Our study showed that miR-143 is involved in growth plate behavior and regulates PCSC growth by targeting IHH, suggesting that miR-143 may serve as a novel target for PCSC-related diseases.

Different characteristics of infants diagnosed with congenital choledochal malformation prenatally or postnatally.

The general condition, clinical and pathological characteristics, and treatment regimens of patients prenatally and postnatally diagnosed with congenital choledochal malformation (CM) were analyzed in order to investigate the clinical significance of early diagnosis, treatment, and intervention in CM. We retrospectively analyzed 33 children who were admitted to the Children's Hospital of Soochow University between 1 March 2010 and 31 May 2019, and their diagnosis of CM was confirmed by radiological, surgical and pathological findings. All the patients were under 36 months of age. The patients were divided into prenatally diagnosed and postnatally diagnosed groups. There were 16 and 17 CM patients in the prenatally and postnatally diagnosed groups, respectively, with a preponderance of females in both groups. Compared with the prenatally diagnosed group, the postnatally diagnosed group had a higher incidence of abdominal pain and vomiting (p < 0.05) and higher AST, GGT, and TB levels (p < 0.05). Although postoperative histopathological examination showed inflammation in both groups, congestion in the cyst walls and fibrous tissue hyperplasia were more significant in the postnatally diagnosed group (p < 0.05). In addition, operation time, length of time required to resume a normal diet after surgery, and total length of hospitalization differed between the 2 groups (p < 0.05), with the prenatally diagnosed group having a relatively longer operation time and taking longer to resume a normal diet after surgery. However, the total length of hospitalization in the prenatally diagnosed group was shorter than that in the postnatally diagnosed group. Compared with prenatally diagnosed CM patients, more symptoms, greater severity of symptoms, and more time to recovery after surgery were observed in postnatally diagnosed CM patients.

Preoperative one-stop magnetic resonance imaging evaluation of the pancreaticobiliary junction and hepatic arteries in children with pancreaticobiliary maljunction: a prospective cohort study.

PURPOSE: Pancreaticobiliary maljunction (PBM) is routinely assessed by intraoperative cholangiography (IOC), whereas accompanying abnormalities in the hepatic artery are assessed by preoperative contrast multi-slice computed tomography (MSCT). We evaluated the efficiency of performing one-stop preoperative magnetic resonance imaging (MRI) for delineating the anatomy of the pancreaticobiliary junction and the hepatic artery. METHODS: The subjects of this prospective analysis were children who underwent Roux-en-Y surgery for PBM in our institution during a recent 3-year period. Preoperative one-stop MRI was conducted using 3.0-T MRI. The efficiency of one-stop MRI was compared with that of IOC for assessing the bile duct, and with contrast MSCT for assessing the blood vessels. RESULTS: Sixty-five children underwent one-stop preoperative MRI, which had a 100% concordance rate, versus IOC for assessing the bile duct type. Protein plugs or cholelithiasis were identified by IOC in 8 children and by one-stop MRI in 45 children (P = 0.0233). Cholangitis was not identified by IOC in any children but it was identified by one-stop MRI in 29 children. MSCT was also performed in 46 children and revealed a variant hepatic artery in 9 and cholangitis in 21. One-stop MRI had a 100% concordance rate versus MSCT. CONCLUSION: Preoperative one-stop MRI accurately delineates the bile duct anatomy as well as the hepatic artery, cholangitis, and protein plugs in children with PBM.