The utility of Multicentre Epilepsy Lesion Detection (MELD) algorithm in identifying epileptic activity and predicting seizure freedom in MRI lesion-negative paediatric patients.

AIM: Paediatric patients with drug-resistant focal epilepsy (DRFE) who have no clear focal lesion identified on conventional structural magnetic resonance imaging (MRI) are a particularly challenging cohort to treat and form an increasing part of epilepsy surgery programs. A recently developed deep-learning-based MRI lesion detection algorithm, the Multicentre Lesion Detection (MELD) algorithm, has been shown to aid detection of focal cortical dysplasia (FCD). We applied this algorithm retrospectively to a cohort of MRI-negative children with refractory focal epilepsy who underwent stereoelectroencephalography (SEEG) to determine its accuracy in identifying unseen epileptic lesions, seizure onset zones and clinical outcomes. METHODS: We retrospectively applied the MELD algorithm to a consecutive series of MRI-negative patients who underwent SEEG at our tertiary Paediatric Epilepsy Surgery centre. We assessed the extent to which the identified MELD cluster or lesion area corresponded with the clinical seizure hypothesis, the epileptic network, and the positron emission tomography (PET) focal hypometabolic area. In those who underwent resective surgery, we analysed whether the region of MELD abnormality corresponded with the surgical target and to what extent this was associated with seizure freedom. RESULTS: We identified 37 SEEG studies in 28 MRI-negative children in whom we could run the MELD algorithm. Of these, 14 (50 %) children had clusters identified on MELD. Nine (32 %) children had clusters concordant with seizure hypothesis, 6 (21 %) had clusters concordant with PET imaging, and 5 (18 %) children had at least one cluster concordant with SEEG electrode placement. Overall, 4 MELD clusters in 4 separate children correctly predicted either seizure onset zone or irritative zone based on SEEG stimulation data. Sixteen children (57 %) went on to have resective or lesional surgery. Of these, only one patient (4 %) had a MELD cluster which co-localised with the resection cavity and this child had an Engel 1 A outcome. CONCLUSIONS: In our paediatric cohort of MRI-negative patients with drug-resistant focal epilepsy, the MELD algorithm identified abnormal clusters or lesions in half of cases, and identified one radiologically occult focal cortical dysplasia. Machine-learning-based lesion detection is a promising area of research with the potential to improve seizure outcomes in this challenging cohort of radiologically occult FCD cases. However, its application should be approached with caution, especially with regards to its specificity in detecting FCD lesions, and there is still work to be done before it adds to diagnostic utility.

International Consensus Statement on the Radiological Screening of Contact Children in the Context of Suspected Child Physical Abuse.

Importance: Physical abuse is a common but preventable cause of long-term childhood morbidity and mortality. Despite the strong association between abuse in an index child and abuse in contact children, there is no guidance outlining how to screen the latter, significantly more vulnerable group, for abusive injuries. Consequently, the radiological assessment of contact children is often omitted, or variably performed, allowing occult injuries to go undetected and increasing the risk of further abuse. Objective: To report an evidence-based and consensus-derived set of best practices for the radiological screening of contact children in the context of suspected child physical abuse. Evidence Review: This consensus statement is supported by a systematic review of the literature and the clinical opinion of an internationally recognized group of 26 experts. The modified Delphi consensus process comprised 3 meetings of the International Consensus Group on Contact Screening in Suspected Child Physical Abuse held between February and June 2021. Findings: Contacts are defined as the asymptomatic siblings, cohabiting children, or children under the same care as an index child with suspected child physical abuse. All contact children should undergo a thorough physical examination and a history elicited prior to imaging. Contact children younger than 12 months should have neuroimaging, the preferred modality for which is magnetic resonance imaging, and skeletal survey. Contact children aged 12 to 24 months should undergo skeletal survey. No routine imaging is indicated in asymptomatic children older than 24 months. Follow-up skeletal survey with limited views should be performed if abnormal or equivocal at presentation. Contacts with positive findings should be investigated as an index child. Conclusions and Relevance: This Special Communication reports consensus recommendations for the radiological screening of contact children in the context of suspected child physical abuse, establishing a recognized baseline for the stringent evaluation of these at-risk children and providing clinicians with a more resilient platform from which to advocate for them.

Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study.

BACKGROUND: Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice. OBJECTIVE: We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors. MATERIALS AND METHODS: Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBVuncorr) and corrected (rCBVcorr) relative cerebral blood volume, and the leakage parameter, K2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types. RESULTS: Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBVuncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. -0.14±5.55; P<0.01). Average median rCBV increased following leakage correction (2.54±1.63 vs. 1.68±1.36; P=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBVuncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference. CONCLUSION: Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children's brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors.

Rate and severity of radiological features of physical abuse in children during the first UK-wide COVID-19 enforced national lockdown.

Rate and severity of radiological features of physical abuse in children during the first UK-wide COVID-19 enforced national lockdown. OBJECTIVE: To assess the number, type and outcome of radiological investigations for children presenting to hospital with suspected physical abuse (SPA; including abusive head trauma) during the first national COVID-19 enforced lockdown compared with the prelockdown period. DESIGN: Multicentre, retrospective, observational, interrupted time series analysis. SETTING: Eight secondary/tertiary paediatric centres between January 2018 and July 2020 inclusive. PARTICIPANTS: 1587 hospital assessed children undergoing radiographic skeletal surveys (SkS) and head CT imaging performed for SPA/child protection concerns. MAIN OUTCOME MEASURES: Incidence and severity of fractures identified on SkS; head injury (composed of incidence rates and ratios of skull fracture, intracranial haemorrhage (ICH) and hypoxic ischaemic injury (HII)) on head CT imaging; and ratio of antemortem and postmortem SkS. RESULTS: 1587 SkS were performed: 1282 (81%) antemortem, 762 (48%) male, and positive findings in 582 (37%). Median patient age was 6 months. There were 1.7 fractures/child prelockdown versus 1.1 fractures/child during lockdown. There was no difference between positive/negative SkS rates, the absolute ratio of antemortem/postmortem SkS or absolute numbers of head injury occurring between January 2018 and February 2020 and the lockdown period April-July 2020. Likewise, prelockdown incidence and rates of skull fracture 30/244 (12%), ICH 28/220 (13%) and HIE 10/205 (5%) were similar to lockdown, 142/1304 (11%), 171/1152 (15%) and 68/1089 (6%), respectively. CONCLUSION: The first UK COVID-19 lockdown did not lead to an increase in either the number of antemortem or postmortem radiological investigations performed for SPA, or the number or severity of fractures and intracranial injuries identified by these investigations.

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours.

1 H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation.

Fifteen-minute consultation: Fractures in non-ambulant children with cerebral palsy.

OBJECTIVE: To describe a safeguarding decision pathway for the assessment of osteopenic fractures in non-ambulant children with cerebral palsy. METHOD: Literature review and consensus practice of a child safeguarding team, including clinicians and social workers. CONCLUSION: Low-energy fractures of the lower limb in non-ambulant children with cerebral palsy are relatively common and explained by the presence of reduced bone strength, in the absence of any other unexplained injuries or safeguarding concerns.

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.