Background:Thalamic abnormalities have been implicated in schizophrenia, but their early role remains unclear. This study investigated structural and functional alterations of thalamic subregions in drug-naïve first-episode schizophrenia (FES) patients and ultra-high-risk (UHR) individuals, and explored their associations with serum short-chain fatty acids (SCFAs).Methods:This cross-sectional study included 102 FES patients, 72 UHR individuals, and 69 healthy controls (HCs). Structural magnetic resonance imaging (MRI) data were available for all participants, and functional MRI and SCFA measurements were conducted in subgroups (functional MRI (fMRI): 76 FES, 63 UHR, 61 HC; SCFA: 59 FES, 51 UHR, 40 HC).Results:Thalamic volume was smaller in FES compared to HCs, with atrophy present specifically at the psychotic stage, particularly affecting the right thalamus and nuclei including the mediodorsal medial magnocellular (MDm), ventromedial (VM), and ventral posterolateral (VPL). Functional connectivity (FC) disruptions were observed in the left sensory thalamus with cortico-striatal-thalamic circuits and in the right occipital thalamus with fronto-temporal regions. Thalamic volumetric deficits correlated with negative and disorganized symptoms in both clinical groups. Serum SCFAs showed several significant associations: in the UHR group, FC between the right occipital thalamus and the left medial frontal cortex was negatively associated with acetic acid and total SCFA levels; in FES patients, centromedian nucleus volume was positively correlated with acetic acid; and in HCs, butyric acid was inversely correlated with the volume of the mediodorsal (MD) lateral nucleus.Conclusions:These findings highlight early thalamic subregional alterations in psychosis risk and co-occurring changes in gut-derived metabolites. Whether and how these changes are related remains a question for future research.

BACKGROUND:To evaluate magnetic resonance imaging (MRI)-visible perivascular spaces (PVSs) as an imaging marker of glymphatic function in neonatal hypoxic-ischemic encephalopathy (HIE) and to assess the diagnostic utility of a combined clinical-PVS model. METHODS:This retrospective study included 266 neonates with HIE, categorized as mild (n = 50) or moderate-to-severe (n = 216). PVS burden in the basal ganglia (BG) and white matter was assessed on T2-weighted imaging using visual grading and volumetric quantification. Logistic regression models (clinical vs combined clinical-PVS vs traditional MRI injury pattern model) were constructed, with performance evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis. RESULTS:A significant severity-dependent reduction in PVS metrics was observed in the BG. Neonates in the moderate-to-severe group exhibited significantly lower BG PVS fractions compared to the mild group (P < 0.001). Multivariate analysis identified BG PVS fraction and clinical indicators as independent predictors of severity. The combined clinical-PVS model achieved an AUC of 0.82, which was significantly higher than the AUC of 0.69 yielded by the traditional MRI injury pattern model (P < 0.05). CONCLUSIONS:HIE severity is characterized by a progressive reduction in BG PVS, suggesting a structural collapse of the glymphatic network in severe injury. The combined application of clinical markers and quantitative PVS metrics provides superior diagnostic accuracy compared to conventional MRI injury patterns. This objective approach complements clinical assessment and enhances risk stratification for neonates with HIE.

This study aimed to evaluate whether three-dimensional (3D) Black Bone imaging enhances diagnostic accuracy in detecting skull fractures in children with traumatic brain injury (TBI), using computed tomography (CT) as the reference standard. Data were collected between January 2022 and August 2024 from 50 pediatric TBI patients (27 boys, 23 girls; mean age: 4.76 ± 3.39 years) who underwent CT (gold standard) and conventional magnetic resonance imaging (MRI), with or without the Black Bone sequence, for fracture diagnosis. Patients were categorized by age (< 3 years, ≧3 years) and fracture type (linear, depressed, sutural disconnection, comminuted). The presence or absence of skull fracture was assessed using conventional MRI with and without Black Bone images. Conen’s kappa, McNemar’s test, area under curve (AUC), accuracy, sensitivity, negative predictive values (NPV) and positive predictive values (NPV) were employed to compare inter-reader agreement and fracture detection performance between image types. For all cases of skull fracture, conventional MRI with Black Bone images demonstrated an AUC of 0.947 (95

Objectives Accurate pediatric brain tumor classification from gene expression data remains challenging due to the extremely high-dimensional nature of transcriptomic datasets, where tens of thousands of features are available from only a limited number of samples. This imbalance increases the risk of overfitting, unstable feature selection, and unreliable performance estimation. Moreover, molecular heterogeneity among pediatric brain tumor subtypes makes robust prediction and biological interpretation difficult. This study aims to develop a stable and interpretable deep learning framework for pediatric brain tumor classification from transcriptomic profiles. Methods We propose a Stability-Aware Feature-Gated Progressive Network (SA-FGPN) that integrates leakage-controlled preprocessing, low-rank transcriptomic compression, progressive nonlinear dimensionality reduction, residual projection bottleneck learning, feature-interaction modeling, and stability-aware feature gating. The low-rank compressor reduces model complexity under the D ≫ N setting, while progressive representation learning and residual projections improve optimization stability. A sparsity- and consistency-regularized feature-gating mechanism is introduced to reduce dependence on unstable latent representations. For biological interpretation, fold-averaged Integrated Gradients are employed to identify important transcriptomic probes, followed by gene-stability analysis and functional enrichment analysis. Results Experiments were conducted on a pediatric brain tumor gene expression dataset containing 130 samples and 54,676 probe-level features. Under repeated stratified 5-fold cross-validation with 10 repetitions, SA-FGPN achieved an accuracy of 0.9847 ± 0.0142 , macro-F1 of 0.9728 ± 0.0201 , and macro-AUC of 0.9916 ± 0.0078 . Across 30 repeated random stratified splits, the maximum observed accuracy was 0.9897. SA-FGPN consistently outperformed classical machine learning methods and recent deep learning baselines, including autoencoder-, CNN-, CNN-BiLSTM-, and transformer-based approaches. Statistical significance testing, calibration analysis, permutation testing, ablation experiments, computational evaluation, and gene-stability analysis further demonstrated the robustness and reliability of the proposed framework. Conclusion SA-FGPN provides an effective, robust, and biologically interpretable solution for pediatric brain tumor classification from high-dimensional transcriptomic data. By integrating stability-aware feature selection with progressive representation learning, the proposed framework improves predictive performance while enabling the identification of biologically meaningful molecular signatures.

RATIONALE AND OBJECTIVES:To develop and validate an interpretable radiomics model based on pituitary MRI to predict growth hormone deficiency (GHD) in children with short stature. METHODS:This retrospective multicenter study enrolled 202 children (105 GHD, 97 idiopathic short stature [ISS]) as an internal cohort (7:3 ratio for training/testing cohorts) from institution I, and 138 children (61 GHD, 77 ISS) from institution II and institution III as an external validation cohort. Radiomics features were selected by SelectKBest and least absolute shrinkage and selection operator (LASSO), subsequently used to construct six machine learning models. Diagnostic performance of model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration curves. The interpretability of the model was assessed using Shapley additive explanations (SHAP). RESULTS:A total of 17 radiomics features were selected. Among all classifiers, support vector machine (SVM)-based radiomics model exhibited the highest diagnostic performance, with AUCs of 0.877 (95% CI: 0.813, 0.928), 0.878 (95% CI: 0.786, 0.951), and 0.885 (95% CI: 0.833, 0.937) in training, testing, and external validation cohorts, respectively. The SVM-integrated clinical-radiomics model yielded comparable efficacy, with AUCs of 0.874 (95% CI: 0.812, 0.928), 0.878 (95% CI: 0.786, 0.952), and 0.889 (95% CI: 0.830, 0.939) across the same cohorts. Both radiomics-based models significantly outperformed the clinical model (all p<0.001), while no statistically significant difference was observed between the radiomics and clinical-radiomics models (all p>0.05). The SHAP analysis identified three key radiomics features with significant differences between GHD and ISS groups (all p<0.001). CONCLUSIONS:The interpretable radiomics-driven SVM model effectively predicts GH levels, providing a clinically viable, non-invasive alternative to GH stimulation test in children with short stature.

BACKGROUND:Neonatal hypoxic-ischemic encephalopathy (HIE) diagnosis is confounded by heterogeneous neural injury and metabolic dysfunction. Multi-pool chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) uniquely quantifies amide proton transfer, nuclear Overhauser enhancement, and magnetization transfer signals, providing multi-parametric assessment of HIE pathophysiology. OBJECTIVE:To investigate whether multi-pool CEST MRI can serve as a molecular-specific biomarker for histopathological alterations in HIE and assess its efficacy in grading disease severity. MATERIALS AND METHODS:This prospective study included 20 neonates with HIE and 42 age-matched controls undergoing 3.0-T CEST MRI. Imaging data were spatially normalized to a neonatal atlas for region-specific analysis (caudate, putamen, thalamus, pallidum, amygdala, hippocampus). Group differences in CEST signals (amide proton transfer, nuclear Overhauser enhancement, magnetization transfer) were analyzed via Wilcoxon tests, with diagnostic performance evaluated through receiver operating characteristic analysis. RESULTS:Compared to controls, HIE neonates showed significant reductions in amide proton transfer (bilateral putamen, right hippocampus/pallidum/amygdala, left thalamus/caudate), nuclear Overhauser enhancement (left thalamus/caudate/putamen), and magnetization transfer signals (bilateral thalamus/pallidum/putamen, left caudate; all P<0.05). Subgroup analysis revealed progressive metabolic decline: moderate-to-severe HIE exhibited further amide proton transfer reduction in the right thalamus, nuclear Overhauser enhancement decreases in bilateral hippocampus, and magnetization transfer decreases in left hippocampus/thalamus compared to mild cases (all P<0.05). Notably, conventional amide proton transfer-weighted imaging showed no significant changes, as the reduction in amide proton transfer signal was offset by a concurrent decrease in the nuclear Overhauser enhancement, highlighting the superiority of multi-pool analysis. Left hippocampal nuclear Overhauser enhancement demonstrated exceptional severity discrimination (area under curve (AUC)=0.96), while a multi-region integrated model achieved perfect staging accuracy (AUC=1.00). CONCLUSION:Multi-pool CEST MRI effectively captures histopathological changes in neonatal HIE, with left hippocampal nuclear Overhauser enhancement emerging as a precise biomarker for severity stratification. The combined dynamics of amide proton transfer, nuclear Overhauser enhancement, and magnetization transfer signals provide noninvasive insights into metabolic-pathological correlations, highlighting its transformative potential for early diagnosis and targeted therapeutic monitoring.

To establish a combined model integrating imaging-based radiomics features and clinical parameters to predict the prognosis of hypoxic-ischemic encephalopathy (HIE) in full-term newborns one year after birth. A total of 180 full-term neonates diagnosed with HIE were retrospectively analyzed. Based on cognitive and motor function scores at 12 months post-birth, patients were categorized into two groups: Group B, representing those with a good prognosis (n = 84), and Group W, representing those with a poor prognosis (n = 96). The dataset was randomly divided into a training dateset (n = 126) and a testing dateset (n = 54). Clinical characteristics were first compared between the two groups. Subsequently, three predictive models were developed: a clinical model, a radiomics model, and a combined model integrating both clinical and radiomics features. The predictive performances of these models were evaluated using receiver operating characteristic (ROC) curve analysis, and their discriminative abilities were quantified by calculating the area under the curve (AUC). The Apgar scores at 1, 5, and 10 min after birth were significantly higher in Group B compared to Group W (P < 0.05). In the clinical model, the Apgar score at 10 min was identified as the strongest prognostic factor, yielding an AUC of 0.857 in the training datest and 0.737 in the testing datest. In the radiomics model, nine radiomics features were significantly associated with prognosis, achieving AUCs of 0.916 and 0.770 in the training and testing datests, respectively. In the combined model, seven radiomics features together with the 5-minute and 10-minute Apgar scores were identified as independent predictors of prognosis. This integrated model demonstrated superior predictive performance, with AUCs of 0.952 in the training datest and 0.823 in the testing datest. The combined model incorporating MR-based radiomics signatures and clinical parameters demonstrates high predictive accuracy for assessing the one-year prognosis of full-term neonates with HIE, suggesting a promising framework for early risk stratification and individualized management of affected infants.

The striatum, a core brain structure relevant for schizophrenia, exhibits heterogeneous volumetric changes in this illness. Due to this heterogeneity, its role in the risk of developing schizophrenia following exposure to environmental stress remains poorly understood. Using the putamen (a subnucleus of the striatum) as an indicator for convergent genetic risk of schizophrenia, 63 unaffected first-degree relatives of patients (22.08 ± 4.80 years) with schizophrenia (UFR-SZ) were stratified into two groups. Compared with healthy controls (HC; n = 59), voxel-based and brain-wide volumetric changes and their associations with stressful life events (SLE) were tested. These stratified associations were validated using two large population-based cohorts (the ABCD study; n = 1680, 11.92 ± 0.62 years; and UK Biobank, n = 20547, 55.38 ± 7.43 years). Transcriptomic analysis of brain tissues was used to identify the biological processes associated with the brain mediation effects on the SLE-psychosis relationship. The stratified UFR-SZ subgroup with smaller right putamen had a smaller volume in the left caudate when compared to HC; this caudate volume was associated with both a higher level of SLE and more psychotic symptoms. This caudate-SLE association was replicated in two independent large-scale cohorts, when individuals were stratified by both a higher polygenic burden for schizophrenia and smaller right putamen. In UFR-SZ, the caudate cluster mediated the relationship between SLE and more psychotic symptoms. This mediation was associated with the genes enriched in both glutamatergic synapses and response to oxidative stress. The stratified association between the striatum and stress highlights the differential vulnerability to stress, contributing to the complexity of the gene-by-environment etiology of schizophrenia.

This study aimed to determine whether adding pointwise encoding time reduction with radial acquisition (PETRA) images to conventional magnetic resonance (MR) imaging improves the depiction and characterization of traumatic fractures in pediatric patients. Twenty-nine pediatric subjects with fractures and a control group of twenty individuals without fractures were included. Two independent observers assessed conventional MR, PETRA, and combined MRI + PETRA images, documenting the presence of fractures, bone fragments, callus formation, displacement, size, and angles of fractures. Diagnostic accuracy was higher for combined conventional MR with PETRA images than for conventional MR or PETRA alone in detecting fractures (area under curve (AUC): 0.86 for conventional MR, 0.97 for PETRA, 1.00 for combined), callus formation (AUC: 0.67 conventional MR, 0.82 PETRA, 0.86 combined), and bone fragments (AUC: 0.89 conventional MR, 0.96 PETRA, 0.97 combined). PETRA images improved agreement in detecting fractures, especially in the ulna/radius (κ = 0.46 conventional MR, 1.00 PETRA, 1.00 combined) and fibula/talus (κ = 0.42 conventional MR, 1.00 PETRA, 1.00 combined), compared to CT. PETRA also enhanced agreement in characterizing callus formation, bone fragments, displacement, size and fracture angles (intraclass correlation > 0.88 for all comparisons), compared to CT. Addition of PETRA images revealed that the differences in the measurements of fragment displacement, size, and fracture angle compared to CT, were not statistically significant (all p > 0.05). Adding PETRA images to conventional MR enhances diagnostic accuracy and reliability in detecting fractures among pediatric patients compared to conventional MR alone. Not applicable.

Hepatocellular carcinoma (HCC) is an aggressive form of liver cancer. Gamma Knife is a minimally invasive treatment option for cancer, but it remains constrained by limitations such as tumor recurrence and metastatic progression. In contrast, photodynamic therapy (PDT) utilizes tumor-specific targeting while providing three clinical benefits: repeatable administration, immunomodulatory activity, and synergistic integration with systemic therapies. However, traditional PDT faces limitations in treating deep-seated tumors, such as HCC. Copper cysteamine (Cu-Cy) serves as a novel photosensitizer that can be activated directly by X-ray irradiation. Our initial studies have demonstrated that Cu-Cy can be activated by the gamma-ray emitted by the Gamma Knife, making it effective for PDT. Subsequent results showed that combining the Gamma Knife with Cu-Cy-mediated PDT can not only effectively suppress the proliferation and migration of HCC cells but also inhibit tumor growth without obvious side effects. This study substantiates the efficacy and safety of combining Gamma Knife treatment with Cu-Cy-mediated PDT for the management of HCC, while also introducing innovative theories and strategies in therapeutic approaches. Furthermore, our research offers a new perspective on the selection of excitation light sources for PDT, potentially advancing the clinical application and translational viability of Cu-Cy.

ObjectiveThis study aimed to develop highly precise radiomics and deep learning models to accurately detect acute lymphoblastic leukemia (ALL) using a T1WI image.MethodsA total of 604 brain magnetic resonance data of ALL group and normal children (NC) group. Two radiologists independently retrieved radiomics features after manually delineating the area of interest along the clivus at the median sagittal position of T1WI. According to the 9:1 ratio, all samples were randomly divided into the training cohort and the testing cohort. the support vector machine was then used to classify the radiomics model using the features that had a correlation coefficient of greater than 0.99 in the training cohort.the Efficientnet-B3 network model received the training set images to create a deep learning model. The sensitivity, specificity, and area under the ROC curve were calculated in order to evaluate the diagnostic efficacy of the different models after the validation of two aforementioned models in the testing cohort.ResultsThe deep learning model had a higher AUC value of 0.981 than the radiomics model's value of 0.962 in the testing cohort. Delong's test showed no statistical difference between the two models (P>0.05).The accuracy/sensitivity/specificity/negative predictive value/positive predictive value achieved 0.9180/0.9565/0.8947/0.9714/0.8462 for the radiomics model and 0.9344/0.8696/0.9737/0.9250/0.9524 for deep learning model.ConclusionThe deep learning and radiomics models showed high AUC values in the training and test cohorts. They also exhibited good diagnostic efficacy for predicting ALL.

Background This study aims to investigate microstructural abnormalities within and between hemispheres in preschool children with autism spectrum disorders (ASD) using diffusion basis spectrum imaging (DBSI). Methods A total of 35 ASD patients and 32 healthy controls (HC), matched for sex and age, underwent DBSI at 3T. We analyzed DBSI-derived indices of brain white matter using tract-based spatial statistics (TBSS) to compare ASD and HC groups. Support vector machine (SVM) classification was employed to evaluate the potential of positive DBSI parameters in distinguishing ASD patients. Additionally, correlation analyses were conducted to explore relationships between positive DBSI parameters and clinical scales. Results Patients in the ASD group exhibited significantly higher fiber ratios in the right brainstem tracts, increased radial diffusivity in the left superior longitudinal fasciculus, and reduced fractional anisotropy (FA) in various fiber tracts, including projection, commissural, and association fibers, compared to HC. Notably, the FA of the right cingulum correlated positively with the Gesell scale (r = 0.439, p = 0.008) and achieved a specificity of 90% in identifying ASD. Conclusion The DBSI findings suggest asynchronous myelination in the right hemisphere and cerebellum in preschool ASD, with the FA value of the right cingulate gyrus appearing to be a reliable marker for ASD and may serve as a potential diagnostic parameter for preschool ASD.

This study aims to investigate the value of basal ganglia and limbic/paralimbic networks alteration in identifying preschool children with ASD and normal controls using diffusion basis spectrum imaging (DBSI). DBSI data from 31 patients with ASD and 30 NC were collected in Hunan Children's Hospital. All data were imported into the post-processing server. The most discriminative features were extracted from the connection, global and nodal metrics separately using the two-sample t-test. To effectively integrate the multimodal information, we employed the multi-kernel learning support vector machine (MKL-SVM). In ASD group, the value of global efficiency, local efficiency, clustering coefficient and synchronization were lower than NC group, while modularity score, hierarchy, normalized clustering coefficient, normalized characteristic path length, small-world, characteristic path length and assortativity were higher. Significant weaker connections are mainly distributed in the limbic/paralimbic networks. The model combining consensus connection, global and nodal graph metrics features can achieve the best performance in identifying ASD patients, with an accuracy of 96.72%.The most specific brain regions connection weakening associated with preschool ASD are predominantly located in limbic/paralimbic networks, suggesting their involvement in abnormal brain development processes. The effective combination of connection, global and nodal metrics information by MKL-SVM can effectively distinguish patients with ASD.

Objective To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging.Methods We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification.Results The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance.Conclusion The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.

Apart from the typical respiratory symptoms, coronavirus disease 2019 (COVID-19) also affects the central nervous system, leading to central disorders such as encephalopathy and encephalitis. However, knowledge of pediatric COVID-19-associated encephalopathy is limited, particularly regarding specific subtypes of encephalopathy. This study aimed to assess the features of COVID-19-associated encephalopathy/encephalitis in children. We retrospectively analyzed a single cohort of 13 hospitalized children with COVID-19-associated encephalopathy. The primary outcome was the descriptive analysis of the clinical characteristics, magnetic resonance imaging and electroencephalography findings, treatment progression, and outcomes. Thirteen children among a total of 275 (5%) children with confirmed COVID-19 developed associated encephalopathy/encephalitis (median age, 35 months; range, 3-138 months). Autoimmune encephalitis was present in six patients, acute necrotizing encephalopathy in three, epilepsy in three, and central nervous system small-vessel vasculitis in one patient. Eight (62%) children presented with seizures. Six (46%) children exhibited elevated blood inflammatory indicators, cerebrospinal fluid inflammatory indicators, or both. Two (15%) critically ill children presented with multi-organ damage. The magnetic resonance imaging findings varied according to the type of encephalopathy/encephalitis. Electroencephalography revealed a slow background rhythm in all 13 children, often accompanied by epileptic discharges. Three (23%) children with acute necrotizing encephalopathy had poor prognoses despite immunotherapy and other treatments. Ten (77%) children demonstrated good functional recovery without relapse. This study highlights COVID-19 as a new trigger of encephalopathy/encephalitis in children. Autoimmune encephalitis is common, while acute necrotizing encephalopathy can induce poor outcomes. These findings provide valuable insights into the impact of COVID-19 on children's brains.

ObjectiveThe deep medullary veins (DMVs) can be evaluated using susceptibility-weighted imaging (SWI). This study aimed to apply radiomic analysis of the DMVs to evaluate brain injury in neonatal patients with hypoxic-ischemic encephalopathy (HIE) using SWI.MethodsThis study included brain magnetic resonance imaging of 190 infants with HIE and 89 controls. All neonates were born at full-term (37+ weeks gestation). To include the DMVs in the regions of interest, manual drawings were performed. A Rad-score was constructed using least absolute shrinkage and selection operator (LASSO) regression to identify the optimal radiomic features. Nomograms were constructed by combining the Rad-score with a clinically independent factor. Receiver operating characteristic curve analysis was applied to evaluate the performance of the different models. Clinical utility was evaluated using a decision curve analysis.ResultsThe combined nomogram model incorporating the Rad-score and clinical independent predictors, was better in predicting HIE (in the training cohort, the area under the curve was 0.97, and in the validation cohort, it was 0.95) and the neurologic outcomes after hypoxic-ischemic (in the training cohort, the area under the curve was 0.91, and in the validation cohort, it was 0.88).ConclusionBased on radiomic signatures and clinical indicators, we developed a combined nomogram model for evaluating neonatal brain injury associated with perinatal asphyxia.

Objective:To summarize the clinical and CT findings of cranial fasciitis in children and analyze the causes of misdiagnosis, in order to improve the diagnostic accuracy of the disease.Methods:The clinical and CT data of 11 patients of cranial fasciitis confirmed by surgical pathology in our hospital from January 2016 to September 2021 were retrospectively analyzed.Results:Of these 11 patients, 4 were male and 7 were female, aged from 5 to 107 months with an average age of 48.5 months.The clinical manifestations were as follows: painless mass in 9 cases, tenderness in 2 cases.In the short term, the mass increased significantly in 2 cases, gradually increased in 5 cases and did not increase in 4 cases.There were 9 cases of single lesion and 2 cases of multiple lesions, most of which were located in the frontoparietal occipital region.There were two types of CT findings:（1）6 patients presented with expansive and osteolytic bone destruction with soft tissue mass, of which, the lesions in 5 cases penetrated through the cranial plate involving dura, and residual bone fragments were observed in 4 cases.（2）5 patients presented with scalp mass with mild erosion or compression of the outer skull plate.The maximum diameters of the lesions in 11 patients ranged from 12 to 43mm.The soft tissue masses were usually isodensity with clear boundary on the plain CT,and mild to significantly enhanced on the contrast-enhanced CT.None of the 11 cases were diagnosed with cranial fasciitis by CT,and 7 of them were misdiagnosed as Langerhans cell histiocytosis.Conclusion:Cranial fasciitis is a rare benign hyperplastic lesion involving scalp and skull.The rate of imaging misdiagnosis is high.Comprehensive analysis of clinical features and imaging signs can improve the diagnostic accuracy.

[目的]探讨双能CT在体内尿路结石成分鉴别中的应用价值.[方法]选取2020年10月至2021年10月邵阳市中心医院收治的106例尿路结石患者,对其进行80 kV/140 kV双能量扫描及数据采集,分析尿路结石成分,以红外光谱作为金标准,绘制双能CT分析的受试者工作特征(ROC)曲线,分析双能CT诊断的敏感度、特异度、准确度.[结果]双能CT对106例患者的126枚尿路结石进行成分分析,其中尿酸盐结石30枚,非尿酸盐结石96枚.离体后红外光谱诊断尿酸盐结石32枚,非尿酸盐结石94枚.双能CT与红外光谱评估不同成分结石比较,差异无统计学意义(x2=0.228,P＞0.05).尿酸结石有效原子序数为7.06±0.68,与非尿酸结石的12.87 ±2.53比较,差异有统计学意义(t=-11.00,P＜0.001).双能CT分析结石成分的敏感度、特异度以及准确度分别为87.5％、97.9％及95.2％.[结论]双能CT能够较为准确地对尿路结石进行在体成分分析,有助于指导临床医师选择合适的治疗方式.

[目的]探讨儿童脾脏淋巴管畸形的影像学特征.[方法]回顾性分析本院2010年1月至2020年12月经病理学检查证实的9例脾脏淋巴管畸形患儿的临床资料,8例于本院行CT平扫及增强,1例在外院行CT平扫增强及本院M RI平扫增强.观察并分析脾脏淋巴管畸形的部位、分型、形态、边界、大小、有无钙化、分隔、密度/信号及强化方式等.[结果]3例位于脾脏包膜下,6例位于脾脏周边及深部;病理检查结果显示,4例为巨囊型,3例为微囊型,2例为混合型;3例呈分叶状,6例呈大小不等类圆形;3例边界模糊,6例边界尚清晰,其中1例单发病灶边缘小点条状钙化灶.7例CT平扫呈低密度影,1例呈稍高密度影,微囊型呈"蜂窝状"改变,6例CT增强示分隔及囊壁轻度强化,1例病变中心及边缘稍强化,1例实性部分呈明显不均匀强化,1例病变呈稍长T1稍长T2信号,增强实性部分呈渐进性强化.[结论]儿童脾脏淋巴管畸形呈单发或多发病变,其CT和MRI表现具有一定的特征性,绝大部分呈低密度影或囊性信号,少部分密度或信号不均匀,囊壁可见钙化,其分隔及囊壁轻度强化,实性部分可不均匀强化或渐进性强化,有助于脾脏淋巴管畸形的诊断.

目的 探讨儿童嗜酸性膀胱炎(EC)的CT影像特征及临床病理.方法 回顾性分析经临床病理确诊的10例EC患儿的临床资料及泌尿系CT影像表现.所有患儿均进行平扫、增强及延时扫描,重点观察膀胱壁形态、厚度、强化和延时特征表现.结果 8例患儿临床上表现为尿频、尿急,部分合并尿痛,2例表现为排尿困难.实验室检查显示大部分患儿的外周血嗜酸性粒细胞比值不同程度增高,病理提示黏膜、黏膜下层及肌层数量不一的嗜酸性粒细胞等炎性细胞浸润并可见水肿.影像上CT显示膀胱容积缩小,外周多发增粗血管,膀胱壁增厚呈进行性强化,局限性2例,弥漫性8例.膀胱内漂浮着完整的黏膜线,轻度强化,黏膜下宽窄不一的低密度水肿带.结论 儿童EC的主要CT表现为膀胱壁增厚、新生血管、黏膜完整及低密度水肿带,同时结合临床实验室及病史可作出初步诊断.