Aberrant white matter microstructure detected by automatic fiber quantification in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease.

BACKGROUND AND OBJECTIVES: Myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD) is an idiopathic inflammatory demyelinating disorder in children, for which the precise damage patterns of the white matter (WM) fibers remain unclear. Herein, we utilized diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ) to identify patterns of fiber damage and to investigate the clinical significance of MOGAD-affected fiber tracts. METHODS: A total of 28 children with MOGAD and 31 healthy controls were included in this study. The AFQ approach was employed to track WM fiber with 100 equidistant nodes defined along each tract for statistical analysis of DTI metrics in both the entire and nodal manner. The feature selection method was used to further screen significantly aberrant DTI metrics of the affected fiber tracts or segments for eight common machine learning (ML) to evaluate their potential in identifying MOGAD. These metrics were then correlated with clinical scales to assess their potential as imaging biomarkers. RESULTS: In the entire manner, significantly reduced fractional anisotropy (FA) was shown in the left anterior thalamic radiation, arcuate fasciculus, and the posterior and anterior forceps of corpus callosum in MOGAD (all p < 0.05). In the nodal manner, significant DTI metrics alterations were widely observed across 37 segments in 10 fiber tracts (all p < 0.05), mainly characterized by decreased FA and increased radial diffusivity (RD). Among them, 14 DTI metrics in seven fiber tracts were selected as important features to establish ML models, and satisfactory discrimination of MOGAD was obtained in all models (all AUC > 0.85), with the best performance in the logistic regression model (AUC = 0.952). For those features, the FA of left cingulum cingulate and the RD of right inferior frontal-occipital fasciculus were negatively and positively correlated with the expanded disability status scale (r = -0.54, p = 0.014; r = 0.43, p = 0.03), respectively. CONCLUSION: Pediatric MOGAD exhibits extensive WM fiber tract aberration detected by AFQ. Certain fiber tracts exhibit specific patterns of DTI metrics that hold promising potential as biomarkers.

Classification of Myelin Oligodendrocyte Glycoprotein Antibody-Related Disease and Its Mimicking Acute Demyelinating Syndromes in Children Using MRI-Based Radiomics: From Lesion to Subject.

RATIONALE AND OBJECTIVES: To develop MRI-based radiomics models from the lesion level to the subject level and assess their value for differentiating myelin oligodendrocyte glycoprotein antibody-related disease (MOGAD) from non-MOGAD acute demyelinating syndromes in pediatrics. MATERIALS AND METHODS: 66 MOGAD and 66 non-MOGAD children were assigned to the training set (36/35), internal test set (14/16), and external test set (16/15), respectively. At the lesion level, five single-sequence models were developed alongside a fusion model (combining these five sequences). The radiomics features of each lesion were quantified as the lesion-level radscore (LRS) using the best-performing model. Subsequently, a lesion-typing function was employed to classify lesions into two types (MOGAD-like or non-MOGAD-like), and the average LRS of the predominant type lesions in each subject was considered as the subject-level radscore (SRS). Based on SRS, a subject-level model was established and compared to both clinical models and radiologists' assessments. RESULTS: At the lesion level, the fusion model outperformed the five single-sequence models in distinguishing MOGAD and non-MOGAD lesions (0.867 and 0.810 of area under the curve [AUC] in internal and external testing, respectively). At the subject level, the SRS model showed superior performance (0.844 and 0.846 of AUC in internal and external testing, respectively) compared to clinical models and radiologists' assessments for distinguishing MOGAD and non-MOGAD. CONCLUSION: MRI-based radiomics models have potential clinical value for identifying MOGAD from non-MOGAD. The fusion model and SRS model can distinguish between MOGAD and non-MOGAD at the lesion level and subject level, respectively, providing a differential diagnosis method for these two diseases.

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters.

Purpose: To construct a machine learning model based on radiomics of multiparametric magnetic resonance imaging (MRI) combined with clinical parameters for predicting Sonic Hedgehog (SHH) and Group 4 (G4) molecular subtypes of pediatric medulloblastoma (MB). Methods: The preoperative MRI images and clinical data of 95 patients with MB were retrospectively analyzed, including 47 cases of SHH subtype and 48 cases of G4 subtype. Radiomic features were extracted from T1-weighted imaging (T1), contrast-enhanced T1 weighted imaging (T1c), T2-weighted imaging (T2), T2 fluid-attenuated inversion recovery imaging (T2FLAIR), and apparent diffusion coefficient (ADC) maps, using variance thresholding, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The optimal features were filtered using LASSO regression, and a logistic regression (LR) algorithm was used to build a machine learning model. The receiver operator characteristic (ROC) curve was plotted to evaluate the prediction accuracy, and verified by its calibration, decision and nomogram. The Delong test was used to compare the differences between different models. Results: A total of 17 optimal features, with non-redundancy and high correlation, were selected from 7,045 radiomics features, and used to build an LR model. The model showed a classification accuracy with an under the curve (AUC) of 0.960 (95% CI: 0.871-1.000) in the training cohort and 0.751 (95% CI: 0.587-0.915) in the testing cohort, respectively. The location of the tumor, pathological type, and hydrocephalus status of the two subtypes of patients differed significantly (p < 0.05). When combining radiomics features and clinical parameters to construct the combined prediction model, the AUC improved to 0.965 (95% CI: 0.898-1.000) in the training cohort and 0.849 (95% CI: 0.695-1.000) in the testing cohort, respectively. There was a significant difference in the prediction accuracy, as measured by AUC, between the testing cohorts of the two prediction models, which was confirmed by Delong's test (p = 0.0144). Decision curves and nomogram further validate that the combined model can achieve net benefits in clinical work. Conclusion: The combined prediction model, constructed based on radiomics of multiparametric MRI and clinical parameters can potentially provide a non-invasive clinical approach to predict SHH and G4 molecular subtypes of MB preoperatively.

Corpus Callosum Volumes in Children with Autism Spectrum Disorders: Sex-Associated Differences.

This study aimed to analyze the relationship between sex and corpus callosum (CC) volume in children with autism spectrum disorders (ASD) aged 2-4 years. This prospective study included 50 children with ASD and 50 typically developing (TD) children aged 2-4 years. Midsagittal slices of the CCs of the participants were divided into five subregions using FreeSurfer software. The PMCC, AMCC and TCC volumes were significantly higher in ASD participants than in TD participants, and results were significant in females with ASD rather than in males with ASD (all P < 0.05). In toddlers with ASD, the CC volumes were increased and more pronounced in females than in males. This could be due to overgrowth of axons or/and axonal pruning disorders.

Fisher discriminant model based on LASSO logistic regression for computed tomography imaging diagnosis of pelvic rhabdomyosarcoma in children.

Computed tomography (CT) has been widely used for the diagnosis of pelvic rhabdomyosarcoma (RMS) in children. However, it is difficult to differentiate pelvic RMS from other pelvic malignancies. This study aimed to analyze and select CT features by using least absolute shrinkage and selection operator (LASSO) logistic regression and established a Fisher discriminant analysis (FDA) model for the quantitative diagnosis of pediatric pelvic RMS. A total of 121 pediatric patients who were diagnosed with pelvic neoplasms were included in this study. The patients were assigned to an RMS group (n = 36) and a non-RMS group (n = 85) according to the pathological results. LASSO logistic regression was used to select characteristic features, and an FDA model was constructed for quantitative diagnosis. Leave-one-out cross-validation and receiver operating characteristic (ROC) curve analysis were used to evaluate the diagnostic ability of the FDA model. Six characteristic variables were selected by LASSO logistic regression, all of which were CT morphological features. Using these CT features, the following diagnostic models were established: (RMS group)[Formula: see text]; (Non-RMS group)[Formula: see text], where [Formula: see text], [Formula: see text], … and [Formula: see text] are lower than normal muscle density (1 = yes; 0 = no), multinodular fusion (1 = yes; 0 = no), enhancement at surrounding blood vessels (1 = yes; 0 = no), heterogeneous progressive centripetal enhancement (1 = yes; 0 = no), ring enhancement (1 = yes; 0 = no), and hemorrhage (1 = yes; 0 = no), respectively. The calculated area under the ROC curve (AUC) of the model was 0.992 (0.982-1.000), with a sensitivity of 94.4%, a specificity of 96.5%, and an accuracy of 95.9%. The calculated sensitivity, specificity and accuracy values were consistent with those from cross-validation. An FDA model based on the CT morphological features of pelvic RMS was established and could provide an easy and efficient method for the diagnosis and differential diagnosis of pelvic RMS in children.

Sex differences of language abilities of preschool children with autism spectrum disorder and their anatomical correlation with Broca and Wernicke areas.

Objective: People with autism spectrum disorder (ASD) often have language difficulties. This study focuses on whether there are sex differences in language ability in children with ASD and aims to analyze whether such differences may arise from developmental imbalances in the anatomical structures of Broca and Wernicke areas. Methods: The language development quotient (DQ) scores of Gesell Developmental Scale (GDS) and the scores of language communication of Childhood Autism Rating Scale (CARS) were used to judge the language ability, and the FREESURFER software extracted the anatomical structures of Broca and Wernicke areas on 3DT1 sequences. We analyzed the correlation between the anatomical structure of Broca/Wernicke areas and language abilities assessments. Results: The study initially included 44 cases of ASD, with 36 males (81.8 %) and 8 females (18.2%), and the age range was 24-72 months. Males have better language abilities than females. Specifically, the GDS verbal DQ of males was significantly higher than that of females (56.50 ± 18.02 vs. 29.23 ± 6.67, p < 0.001). Broca thickness-L was positively correlated with verbal DQ scores in GDS (r = 0.382, p = 0.011) and lower than grade 2 and 3 on the CARS verbal communication grade 4 (5.76 ± 0.17 vs. 6.21 ± 0.30 and 6.11 ± 0.35), with statistically significant differences between groups (p < 0.05). Conclusion: There were sex differences in the language abilities of preschoolers with ASD, which may be due to an imbalance development of certain structures in Broca and Wernicke areas, especially Broca area.

Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.

Background: Early screening and treatment of esophageal cancer (EC) is particularly important for the survival and prognosis of patients. However, early EC is difficult to diagnose by a routine endoscopic examination. Therefore, convolutional neural network (CNN)-based artificial intelligence (AI) has become a very promising method in the diagnosis of early EC using endoscopic images. The aim of this study was to evaluate the diagnostic performance of CNN-based AI for detecting early EC based on endoscopic images. Methods: A comprehensive search was performed to identify relevant English articles concerning CNN-based AI in the diagnosis of early EC based on endoscopic images (from the date of database establishment to April 2022). The pooled sensitivity (SEN), pooled specificity (SPE), positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR) with 95% confidence interval (CI), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) for the accuracy of CNN-based AI in the diagnosis of early EC based on endoscopic images were calculated. We used the I2 test to assess heterogeneity and investigated the source of heterogeneity by performing meta-regression analysis. Publication bias was assessed using Deeks' funnel plot asymmetry test. Results: Seven studies met the eligibility criteria. The SEN and SPE were 0.90 (95% confidence interval [CI]: 0.82-0.94) and 0.91 (95% CI: 0.79-0.96), respectively. The LR+ of the malignant ultrasonic features was 9.8 (95% CI: 3.8-24.8) and the LR- was 0.11 (95% CI: 0.06-0.21), revealing that CNN-based AI exhibited an excellent ability to confirm or exclude early EC on endoscopic images. Additionally, SROC curves showed that the AUC of the CNN-based AI in the diagnosis of early EC based on endoscopic images was 0.95 (95% CI: 0.93-0.97), demonstrating that CNN-based AI has good diagnostic value for early EC based on endoscopic images. Conclusions: Based on our meta-analysis, CNN-based AI is an excellent diagnostic tool with high sensitivity, specificity, and AUC in the diagnosis of early EC based on endoscopic images.

Functional Brain Networks in Preschool Children With Autism Spectrum Disorders.

Objective: The present study aims to investigate the functional brain network characteristics of preschool children with autism spectrum disorder (ASD) through functional connectivity (FC) calculations using resting-state functional MRI (rs-fMRI) and graph theory analysis to better understand the pathogenesis of ASD and provide imaging evidence for the early assessment of this condition. Methods: A prospective study of preschool children including 32 with ASD (ASD group) and 22 healthy controls (HC)group was conducted in which all subjects underwent rs-fMRI scans, and then the differences in FC between the two groups was calculated, followed by graph-theoretic analysis to obtain the FC properties of the network. Results: In the calculation of FC, compared with the children in the HC group, significant increases or decreases in subnetwork connectivity was found in the ASD group. There were 25 groups of subnetworks with enhanced FC, of which the medial prefrontal and posterior cingulate gyrus and angular gyrus were all important components of the default mode network (DMN). There were 11 groups of subnetworks with weakened FC, including the hippocampus, parahippocampal gyrus, superior frontal gyrus, inferior temporal gyrus, precuneus, amygdala, and perirhinal cortex, with the hippocampus and parahippocampal gyrus predominating. In the network properties determined by graph theory, the clustering coefficient and local efficiency of the functional network was increased in the ASD group; specifically, compared with those in the HC group, nodes in the left subinsular frontal gyrus and the right middle temporal gyrus had increased efficiency, and nodes in the left perisylvian cortex, the left lingual gyrus, and the right hippocampus had decreased efficiency. Conclusion: Alterations in functional brain networks are evident in preschool children with ASD and can be detected with sleep rs-fMRI, which is important for understanding the pathogenesis of ASD and assessing this condition early.

Preoperative MRI Evaluation of Hand Vessels in Children With Congenital Syndactyly Malformation by a Contrast-Enhanced Three-Dimensional Water-Selective Cartilage Scan.

Background: It is crucial to preoperatively assess the arteries of the hands in congenital syndactyly malformation (CSM) patients because this information can affect the therapeutic outcome and prognosis. Objective: To investigate the value of a contrast-enhanced three-dimensional water-selective cartilage scan for the preoperative evaluation of CSM in children. Materials and Methods: Contrast-enhanced three-dimensional water-selective cartilage scan 3.0 T magnetic resonance imaging (MRI) performed in 16 clinically diagnosed CSM patients with 17 affected hands. The arteries of the hands were displayed with a focus on the bifurcation position of the common palmar digital arteries (CPDAs) and the maturity of the proper palmar digital arteries (PPDAs). The MRI results were interpreted by consensus between two experienced pediatric radiologists with 10 years of MRI experience each. The MRI findings were compared with the operation results. Results: Of 51 CPDAs in the 17 affected hands, MRI showed that 30 had an abnormal bifurcation position and 20 had a normal position, and of the 102 PPDAs, 14 were shown to have an abnormal maturity and 85 a normal state, which were confirmed by surgery. The accuracy, sensitivity and specificity for determining the bifurcation position of the CPDAs based on MR maximum intensity projection reconstructed images were 98.04% (50/51), 96.77% (30/31) and 100% (20/20), respectively. The maturity of the PPDAs was judged by MR maximum intensity projection reconstructed images with an accuracy, sensitivity and specificity of 97.06% (99/102), 82.35% (14/17) and 100% (85/85), respectively. Conclusion: Contrast-enhanced three-dimensional water-selective cartilage scan has excellent performance in displaying the bifurcation position of the CPDAs and the maturity of the PPDAs and is of high value for the preoperative evaluation of CSM in children.

Tuber Brain Proportion Determines Epilepsy Onset in Children With Tuberous Sclerosis Complex.

BACKGROUND: Tuberous sclerosis complex (TSC) is a rare autosomal dominant disorder characterized by epilepsy and structural abnormalities of the brain. Little research has been done to explore the relationship between the tuber brain proportion (TBP) and epilepsy. We investigated several quantitative cerebral lesions including TBP on magnetic resonance imaging (MRI) and their impact on the onset age, seizure mode, and antiseizure treatment effectiveness of epilepsy in children with TSC. METHODS: We reviewed the clinical characteristics and MRI information of 44 children with TSC who had experienced epileptic seizures. Supratentorial tubers were quantitatively manually measured to calculate the TBP. The numbers of cortical/subcortical cyst-like tubers, diffuse lesions, subependymal nodules, and subependymal giant cell astrocytomas were also evaluated. RESULTS: Twelve children (27.3%) had experienced infantile spasms, thirteen children (29.5%) had early-onset epilepsy, and twenty-seven patients (64.3%) had a significant reduction in the frequency of seizures after antiseizure treatments. The median TBP was 9.2%, and diffuse lesions (range: 0-2) and cortical cyst-like lesions (range: 0-17) were seen in seven and seventeen children, respectively. The values of TBP (P < 0.001), diffuse lesions (P < 0.001), and cortical cyst-like tubers (P < 0.001) were all associated with early-onset epilepsy. The values of TBP (P = 0.004) and cortical cyst-like tuber (P < 0.001) were associated with the occurrence of infantile spasms. The values of TBP (P = 0.01), diffuse lesions (P = 0.04), and cortical cyst-like tubers (P = 0.004) were negatively associated with the effectiveness of antiseizure treatments. There was no significant correlation between subcortical cyst-like tuber, subependymal nodule, subependymal giant cell astrocytoma, and epilepsy severity. CONCLUSIONS: Increasing abnormality of the cerebral hemispheres, as shown by quantitative MRI analysis including TBP, cortical cyst-like tubers, and diffuse lesions, is associated with measures of more severe epilepsy due to TSC. The values of TBP demonstrate strong significance for early-onset epilepsy.

Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes with an MT-TL1 m.3243A>G point mutation: Neuroradiological features and their implications for underlying pathogenesis.

Objective: Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) is one of the most common inherited mitochondrial disorders. Due to the high clinical and genetic heterogeneity of MELAS, it is still a major challenge for clinicians to accurately diagnose the disease at an early stage. Herein, we evaluated the neuroimaging findings of MELAS with an m.3243A>G mutation in MT-TL1 and analyzed the possible underlying pathogenesis of stroke-like episodes. Materials and methods: Fifty-nine imaging studies in 24 patients who had a confirmed genetic diagnosis of m.3243A>G (MT-TL1; tRNA Leu) associated with MELAS were reviewed in our case series. The anatomic location, morphological features, signal/intensity characteristics and temporal evolution of lesions were analyzed on magnetic resonance imaging (MRI), and computed tomography (CT) images. The supplying vessels and metabolite content of the lesions were also evaluated by using MR angiography (MRA)/CT angiography (CTA), and MR spectroscopy (MRS), respectively. Results: The lesions were most commonly located in the posterior brain, with 37 (37/59, 63%) in the occipital lobe, 32 (32/59, 54%) in the parietal lobe, and 30 (30/59, 51%) in the temporal lobe. The signal characteristics of the lesions varied and evolved over time. Bilateral basal ganglia calcifications were found in 6 of 9 (67%) patients who underwent CT. Cerebral and cerebellar atrophy were found in 38/59 (64%) and 40/59 (68%) patients, respectively. Lesion polymorphism was found in 37/59 (63%) studies. MRS showed elevated lactate doublet peaks in 9/10 (90%) cases. MRA or CTA revealed that the lesion-related arteries were slightly dilated compared with those of the contralateral side in 4 of 6 (67%) cases. Conclusion: The imaging features of MELAS vary depending on the disease stage. Polymorphic lesions in a single imaging examination should be considered a diagnostic clue for MELAS. Stroke-like episodes may be involved in a complex pathogenetic process, including mitochondrial angiopathy, mitochondrial cytopathy, and neuronal excitotoxicity.

Low-dose Computed Tomography (CT) for the Diagnosis of Congenital Heart Disease in Children: A Meta-analysis.

BACKGROUND: Low dose CT has become a promising examination method for the diagnosis of Congenital heart disease (CHD) in children because it has a low radiation dose, but it has not been widely accepted as an alternative to standard-dose CT in clinical applications due to concerns about image quality. Therefore, we suggest that the diagnostic accuracy, image quality, and radiation dose of low-dose CT for CHD in children should be fully explored through a metaanalysis of existing studies. METHODS: A comprehensive search was performed to identify relevant English and Chinese articles (from inception to May 2019). All selected studies concerned the diagnosis of CHD in children using low-dose CT. The accuracy of low-dose CT was determined by calculating pooled estimates of sensitivity, specificity, diagnostic odds ratio, and likelihood ratio. Pooling was conducted using a bivariate generalized linear mixed model. Forest plots and summary receiver operating characteristic (SROC) curves were generated. RESULTS: Ten studies, accounting for 577 patients, met the eligibility criteria. The pooled sensitivity and specificity were 0.95 (95% confidence interval (CI) 0.92-0.97) and 1.00 (95% CI 1.00- 1.00), respectively. The pooled diagnostic odds ratio, positive likelihood ratio, and negative likelihood ratio of low-dose CT were 12705.53 (95% CI 5065.00-31871.73), 671.29 (95% CI 264.77- 1701.97), and 0.05 (95% CI 0.03-0.08), respectively. Additionally, the area under the SROC curve was 1.00 (95% CI 0.99-1.00), suggesting that low-dose CT is an excellent diagnostic tool for CHD in children. CONCLUSION: Low-dose CT, especially with a prospective ECG-triggering mode, provides excellent imaging quality and high diagnostic accuracy for CHD in children.

Black Hole Sign on Noncontrast Computed Tomography in Predicting Hematoma Expansion in Patients with Intracerebral Hemorrhage: A Meta-analysis.

BACKGROUND: Black hole sign represents a novel imaging marker for predicting hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH). Several previous studies have reported the accuracy of black hole sign in predicting HE, but the accuracy was variable. We performed a meta-analysis to systematically assess the accuracy of black hole sign in predicting HE in patients with ICH. METHODS: A systematic search was performed to identify relevant English and Chinese articles (from inception to January 2019). All studies on the accuracy of black hole sign in predicting HE in patients with ICH were included. Pooled sensitivity, specificity, and positive and negative likelihood ratios were calculated. Pooling was conducted using the bivariate generalized linear mixed model. Forest plots and a summary receiver operator characteristic plot were generated. We used I² to test heterogeneity and investigated the source of heterogeneity by meta-regression. Publication bias was assessed by Deeks' funnel plot asymmetry test. RESULTS: A total of 6 studies with 1876 patients were included in this meta-analysis. The pooled sensitivity, specificity, and positive and negative likelihood ratios of black hole sign for predicting HE were 0.30, 0.93, 4.00 and 0.75, respectively. The area under the curve (AUC) was 0.83. The studies had substantial heterogeneity (I²=89.00%, 95% CI 78.00-100.00). Low risk of publication bias was detected. CONCLUSION: Black hole sign is a useful imaging marker with high specificity in predicting hematoma expansion in patients with intracerebral hemorrhage.

A Sleeping rs-fMRI Study of Preschool Children with Autism Spectrum Disorders.

OBJECTIVES: The brain functional network of autism spectrum disorders (ASDs) in the earlier stages of life has been almost unknown due to difficulties in obtaining a resting-state functional magnetic resonance imaging (rs-fMRI). This study aimed to perform rs-MRI under a sedated sleep state and reveal possible alterations in the brain functional network. METHODS: Rs-fMRI was performed in a group of preschool children (aged 2-6 years, 53 with ASD, 63 as controls) under a sedated sleeping state. Based on graph theoretical analysis, global and local topological metrics were calculated to investigate alterations in brain functional networks. Besides, correlation analyses were conducted between the abnormal attribute values and the Childhood Autism Rating Scale (CARS) scores. RESULTS: The graph theoretical analysis showed that the nodal degree of the right medial frontal gyrus and the nodal efficiency of the right lingual gyrus in the ASD group were higher than those in the control group (P<0.05). There was a statistically significant positive correlation (R=0.318, P<0.05) between the right midfrontal gyrus nodal degree values and CARS scores in the ASD patients. CONCLUSION: Alterations of some nodal attributes in the brain network occurred in preschool autistic children which could serve as potential imaging biomarkers for evaluating ASD in earlier stages.

Systematic Review and Meta-analysis of the Malignant Ultrasound Features of Triple-Negative Breast Cancer.

OBJECTIVES: The malignant ultrasound (US) features of breast cancer are known to include an irregular shape, a noncircumscribed margin, an echogenic halo, a nonparallel orientation, posterior acoustic attenuation, microcalcification, and others. However, these US features are uncertain and controversial for the diagnosis of triple-negative breast cancer (TNBC). This study aimed to analyze the diagnostic value of malignant US features for TNBC by a systematic review and meta-analysis, analyze the US characteristics of TNBC, and provide US evidence for clinical diagnosis. METHODS: A comprehensive search was performed to identify relevant English articles concerning the US diagnosis of TNBC (from the date of database establishment to November 2019). The pooled sensitivity, pooled specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio with 95% confidence interval, summary receiver operating characteristic curve, and area under the curve for the different malignant US features were calculated. RESULTS: Ten studies (620 patients) met the eligibility criteria. The sensitivity (range, 0.14-0.68) and specificity (range, 0.19-0.66) of the malignant US features were not high. Summary receiver operating characteristic curves showed that the area under the curve (range, 0.25-0.47) of the malignant US features was low, demonstrating that these features have poor diagnostic value for TNBC. The positive likelihood ratio (range, 0.4-to 0.9) of the malignant US features was low, and the negative likelihood ratio (range, 1.09-2.02) was not low, revealing that these features had a poor ability to confirm or exclude TNBC. CONCLUSIONS: Triple-negative breast cancer lacks the typical malignant US features of breast cancer and has its own US features.

Corrigendum: Enhanced Topological Network Efficiency in Preschool Autism Spectrum Disorder: A Diffusion Tensor Imaging Study.

[This corrects the article DOI: 10.3389/fpsyt.2018.00278.].

Enhanced Topological Network Efficiency in Preschool Autism Spectrum Disorder: A Diffusion Tensor Imaging Study.

Background: The functional mechanism behind autism spectrum disorder (ASD) is not clear, but it is related to a brain connectivity disorder. Previous studies have found that functional brain connectivity of ASD is linked to both increased connections and weakened connections, and the inconsistencies in functional brain connectivity may be related to age. The functional connectivity in adolescents and adults with ASD is generally less than in age-matched controls; functional connectivity in younger children with the disorder appears to be higher. As the basis of the functional network, the structural network is less studied. This study intends to further study the pathogenesis of ASD by analyzing the white matter network of ASD preschool children. Materials and Methods: In this study, Diffusion Tensor Imaging (DTI) was used to scan preschool children (aged 2-6 years, 39 children with ASD, 19 children as controls), and graph theory was used for analysis. Result: Enhanced topological network efficiency was found in the preschool children with ASD. A higher nodal efficiency was found in the left precuneus, thalamus, and bilateral superior parietal cortex, and the nodal efficiency of the left precuneus was positively associated with the severity of ASD. Conclusion: Our research shows the white matter network efficiency of preschoolers with ASD. It supports the theory of excessive early brain growth in ASD, and it shows left brain lateralization. It opens the way for new research perspectives of children with ASD.

Diffusion Tensor Imaging for Evaluating Biliary Atresia in Infants and Neonates.

BACKGROUND: Preliminary studies have shown that diffusion tensor imaging (DTI) is helpful in evaluating liver disorders. However, there is no published literature on the use of DTI in the diagnosis of biliary atresia (BA). This study aimed to investigate the diagnostic value of the liver average apparent diffusion coefficient (ADC) and fractional anisotropy (FA) measured using DTI for BA in neonates and infants. METHODS: Fifty-nine patients with infant jaundice were included in this study. DTI was performed with b factors of 0 and 1000 s/mm2. Liver fibrosis in the BA group was determined and graded (F0, F1, F2, F3, F4) based on the pathological findings. Statistical analyses were performed to determine the diagnostic accuracy of DTI for BA. RESULTS: The ADC value was significantly lower in the BA group [(1.262±0.127)×10-3 mm2/s] than in the non-BA group [(1.430±0.149)×10-3 mm2/s, (P<0.001)]. The area under the receiver operating characteristic curve was 0.805±0.058 (P<0.001) for ADC. With a cut-off value of 1.317×10-3 mm2/s, ADC achieved a sensitivity of 75% and a specificity of 81.5% for the differential diagnosis of BA and non-BA. In the BA group, the ADC value was significantly correlated with fibrotic stage. Further analysis showed that the ADC value of stage F0 was significantly higher than that of stages F1, F2, F3 and F4, whereas there were no significant differences among stages F1, F2, F3 and F4. CONCLUSION: Hepatic ADC measured with DTI can be used as an adjunct to other noninvasive imaging methods in the differential diagnosis of BA and non-BA. ADC was helpful in detecting liver fibrosis but not in differentiating the fibrotic grades.

In Vivo magnetic resonance imaging of xenografted tumors using FTH1 reporter gene expression controlled by a tet-on switch.

As a promising magnetic resonance imaging (MRI) reporter, ferritin has been used to track cells in vivo; however, its continuous overexpression can be cytotoxic, which restricts its application. In this study, we aimed to develop a switch to turn this genetic reporter "on" or "off" while monitoring cell grafts via MRI. To accomplish this, we genetically modified the ferritin heavy chain (FTH1) with a Tet-On switch and assessed the expression of FTH1 in transduced neuroblastoma cells (SK-N-SH) in vitro and in xenografted tumors in vivo. We found that FTH1 expression induced by doxycycline (Dox) in SK-N-SH-FTH1 cells depended on treatment dose and duration. We successfully detected T2-weighted MRI contrast in cell grafts after switching "on" the reporter gene using Dox, and this contrast disappeared when we switched it "off". The genetic reporter FTH1 can thus be switched "on" or "off" throughout longitudinal monitoring of cell grafts, limiting expression to when MRI contrast is needed. The controllable imaging system we have developed minimizes risks from constitutive reporter gene overexpression and facilitates tumor cell monitoring in vitro and in vivo.

Osteosclerotic metaphyseal dysplasia with extensive interstitial pulmonary lesions: a case report and literature review.

Osteosclerotic metaphyseal dysplasia (OMD) is a very rare sclerosing bone disorder. To date, four cases have been documented in three reports. Here, we present the case of a 12-year-old girl with a history of recurrent respiratory infections, hypotonia, developmental delay, genu valgum, and hepatosplenomegaly. Radiographs revealed profound, ivory-white sclerosis of the metaphyses and epiphyses of the long bones in both the upper and lower extremities. Sclerosis also affected the ends or margins of the flat bones, including the mandible, clavicles, scapulae, ribs, iliac crests, ischia, pubic bones, talus, calcaneus, and some vertebrae, to varying degrees. Based on the clinical, radiographic, and laboratory findings, a diagnosis of OMD was made. Our patient is the fifth case of OMD reported in the international literature and shares clinical and radiological similarities with four other reported cases of OMD. However, the extensive interstitial pulmonary lesions observed on computed tomography images in the present case have not been previously documented. This pulmonary disorder, which may be associated with OMD, should be evaluated in subsequently encountered cases.