Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Primary Intracranial High Grade Congenital Glial Lesions (PIHGCL): A systematic review of case reports from 1985 to 2025
BACKGROUND: Among congenital intracranial space-occupying lesions, teratomas, gliomas, and neuroblastomas are more frequently reported. However, primary congenital glioblastoma (cGBM) and congenital anaplastic astrocytoma (cAA) are underrecognized entities. Their detection remains challenging due to nonspecific prenatal findings and limited awareness among pediatricians and obstetric care providers, contributing to delayed diagnosis and worsened outcomes. OBJECTIVE: To summarize the clinical presentations, diagnostic imaging characteristics, reported cases, histopathological features, and neuro-oncological management strategies of primary intracranial high grade congenital glial lesions (PIHGCL). MATERIALS AND METHODS: A comprehensive literature search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted in 2025 over PubMed and Google Scholar using keywords and Boolean operators, retrieving 35 studies (44 patients) with PIHGCL published 1985 to 2025. Quality assessment was done using Joanna Briggs Institute (JBI) tool. The data was analyzed descriptively as well as analytically to determine survival outcomes in treatment groups. RESULTS: A total of 28 studies (37 patients) discussed cGBM, and 7 studies (7 patients) described the occurrence of cAA. Most of the patients were infants i.e., 65.9% (29), presenting with macrocephaly in 27.7% (10) patients. Most of the lesions were intracerebral, 72.7% (32), lateralized equally to both sides, i.e., 46.5% (20). Surgical excision alone was done in 29.5% (13) patients, surgical excision with adjuvant chemotherapy was given to 43.2% (19) patients, chemotherapy alone in 6.8% (3) patients whereas 20.5% (9) underwent only conservative treatment. Kaplan Meir-analysis revealed a significant difference in survival in patients who received adjuvant treatment along with surgical excision in contrast to other treatment groups (χ2 = 7.83, df = 3, p = 0.05). CONCLUSION: PIHGCL presents during fetal or neonatal period with clinicopathological features distinct from adult and pediatric glioblastomas with distinct genetic etiology. Extent of resection is not a reliable marker of treatment outcomes in PIHGCL due to anatomical and physiological variations at the age of presentations. Adjuvant chemotherapy along with surgical resection results in improved patient survival.
11 June 2026
Read appraisal →Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Structural equation modeling of findings in pediatric tele-neuroradiology: a 2-year nationwide study of turnaround time predictors and clinical decision support
BACKGROUND: Pediatric neuroradiology faces significant workforce shortages, with teleradiology emerging as a vital solution. However, studies investigating findings and their operational impact from teleradiology centers remain limited. OBJECTIVES: To develop and validate structural equation models for identifying predictors of turnaround time and formulate recommendations for workflow optimization in pediatric tele-neuroradiology services. DESIGN: A retrospective cohort study following STROBE guidelines. SETTINGS: 107 hospitals across 17 states in the United States (US) via a teleradiology platform providing interpretation services through US board-certified radiologists. PATIENTS AND METHODS: We analyzed 9985 pediatric neuroradiology scans from 7958 patients (January 2023-December 2024). We utilized confirmatory factor analysis to validate findings structures, followed by structural equation modeling to predict turnaround times. Binary logistic regression models were developed with area-under-the-curve (AUC) estimation for performance assessment. Bootstrap validation with 5000 samples ensured model stability. MAIN OUTCOME MEASURES: Primary outcome was turnaround time. Secondary outcomes included requirements for multiple imaging studies, follow-up recommendations, and consultations. SAMPLE SIZE: A total of 9985 studies providing over 99% statistical power for detecting significant relationships. RESULTS: Factor analysis demonstrated a two-factor structure (trauma: α = 0.742, structural: α = 0.685). The structural model explained 7.8% of turnaround time variance, with computed tomography (CT) modality (β = -0.164), trauma score (β = 0.125), and structural score (β = 0.142) as significant predictors. Among immediate neurosurgical emergencies (n = 180, 1.8%), 89.4% achieved turnaround time within the 60-min benchmark for time-sensitive consultations. Prediction models demonstrated excellent discrimination: traumatic findings (AUC = 0.91), structural findings (AUC = 0.92), critical findings (AUC = 0.95), and a dedicated neurosurgical emergency model (AUC = 0.94, NPV = 0.996). A severity classification system showed strong validation against imaging needs (AUC = 0.76) and consultations (AUC = 0.89). CONCLUSIONS: Our study establishes a validated SEM framework for pediatric tele-neuroradiology with excellent predictive performance (AUC = 0.91-0.95). Among immediate neurosurgical emergencies (n = 180, 1.8%), 89.4% met the 60-min benchmark, and a dedicated emergency prediction model achieved AUC = 0.94. However, translation to improved neurosurgical care delivery and patient outcomes remains unvalidated, representing the next investigational priorities. LIMITATIONS: Retrospective design limits causal inference; a single platform may limit generalizability; the CT majority (96.8%) limits magnetic resonance imaging conclusions. Critically, post-diagnostic clinical outcomes, including neurosurgical consultations, interventions performed, and patient outcomes, were not tracked, precluding conclusions about whether documented operational efficiency translated to improved neurosurgical care delivery.
2 May 2026
Read appraisal →Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
The role of age and Galassi type in management and surgical outcomes of pediatric middle fossa arachnoid cysts: a systematic review and meta-analysis
PURPOSE: Arachnoid cysts are found in approximately 2.6% of the pediatric population, mostly asymptomatic, incidental findings. However, those with mass effect symptoms from cyst growth may require surgery. Because of the variability in progression, we investigated the interaction of patient age with middle fossa arachnoid cyst (MFAC) clinical outcomes. METHODS: A systematic review using PubMed, Google Scholar, and Web of Science electronic databases was completed according to the PRISMA guidelines to identify articles detailing pediatric MFACs. RESULTS: Sixty articles meeting the inclusion criteria, totaling 1024 patients, were included. Signs of elevated ICP (194/873, prevalence: 22.2%, 95% CI: 19.5-25.0%) and primary headaches (263/873, prevalence: 30.1%, 95% CI: 27.1-33.2%) were the most common presenting symptoms. In the evaluation of the hemispheric location of MFACs, younger ages at presentation were more likely to present with bilateral MFACs (OR: 0.71, 95% CI: 0.52-0.98, p = 0.034). Most MFACs in the retrospective studies had left laterality (421/677, prevalence: 62.2%, 95% CI: 58.6-65.1%) and were singular (791/817, prevalence:96.8%, 95% CI: 95.6-98.2%). A higher proportion of younger patients required a second operation (OR: 0.82, 95% CI: 0.72-0.93, p = 0.002) and cerebrospinal fluid shunting (OR: 0.88, 95% CI: 0.79-0.97, p = 0.011) across the case reports. There was no significant difference in complications when comparing older children (5-18 years) to young children (≤ 4 years) (p = 0.737) or Galassi type (p = 0.736), but Galassi type 1 was associated with management through observation (p = 0.019). CONCLUSION: Younger patients presented with a higher rate of bilateral MFACs and required more invasive treatment methods and repeat operations more frequently, suggesting that infants and young children with MFACs may require greater surveillance during and after management to minimize complications.
14 Apr 2026
Read appraisal →Developmental cognitive neuroscience
Deep learning in fetal, infant, and toddler neuroimaging research.
Artificial intelligence (AI) is increasingly being integrated into everyday tasks and work environments. However, its adoption in medical image analysis has progressed more slowly due to high clinical stakes, limited availability of labeled data, and substantial variability in imaging protocols and population. These challenges are further pronounced in the field of fetal, infant, and toddler (FIT) neuroimaging, where datasets are especially scarce and subject to large amounts of anatomical variability. However, deep learning (DL), a specific method within machine learning, which is itself a subfield of AI, has emerged as a powerful framework to adapt to the challenges of medical image analysis. This review is written for the broad FIT research community, including clinicians, neuroscientists, and develop mental scientists who may not have formal training in AI. To make the material accessible, we provide a concise overview of DL concepts before reviewing a selected, and non-exhaustive, list of applications of DL in FIT neuroimaging, including structural image analysis, enhancement of data acquisition, modeling of cognitive and perceptual processes, and automated video tagging. In closing, we discuss best practices for data curation, ongoing challenges, and opportunities for future research.
4 Apr 2026
Read appraisal →Pediatric radiology
Data mining in pediatric radiology in the era of artificial intelligence
Data mining is the systematic process of extracting useful knowledge from large multimodal datasets and is increasingly enabled by artificial intelligence (AI) methods. Pediatric radiology is a natural field for data mining because multimodal data sources, including images, reports, metadata, and electronic health records, together capture rich information on anatomy, disease, treatment, and outcomes. In the current era, the boundaries between data mining and AI are increasingly blurred. AI assists in key steps of the mining workflow through automated labeling, information extraction, and representation learning, while data mining provides the high-quality curated datasets that underpin model performance, generalizability, and safety. This review, therefore, examines both domains together, emphasizing their interdependence in the pediatric context. We describe core concepts and workflows of data mining in pediatric radiology, including data collection, linkage, annotation, analysis, validation, and governance, and outline how modern AI tools such as deep learning, large language models, multimodal fusion, and federated learning support advanced pattern discovery across limited and heterogeneous pediatric datasets. We summarize current and emerging clinical applications across diagnosis, prognosis, radiation dose monitoring, operational analytics, reporting safety nets, and continual learning. We then discuss current challenges related to data quality and standardization, ethics, regulation, workflow integration, resource disparities, sustainability, and explainability. Finally, we highlight future perspectives, including synthetic data generation, foundation models, structured reporting, and pediatric-focused ethical frameworks that aim to enable safe, transparent, and equitable integration of AI-driven data mining to improve outcomes in children.
4 Apr 2026
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