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BackgroundBrain MRIs acquired in clinical settings represent a valuable and underutilized scientific resource for investigating neurodevelopment. Utilization of these clinical scans has been limited because of their clinical acquisition and technical heterogeneity. These barriers have curtailed the interpretability and scientific value of retrospective studies of clinically acquired brain MRIs, compared to studies of prospectively acquired research quality brain MRIs.PurposeTo develop a scalable and rigorous approach to generate clinical brain growth chart models, to benchmark neuroanatomical differences in clinical MRIs, and to validate clinically-derived brain growth charts against those derived from large-scale research studies.Materials and MethodsWe curated a set of clinical MRIScans withLimitedImagingPathology (SLIP) – so-called “clinical controls” – from an urban pediatric healthcare system acquired between 2005 and 2020. The curation process included manual review of signed radiology reports, as well as automated and manual quality review of images without gross pathology. We measured global and regional volumetric imaging phenotypes in the SLIP sample using two alternative, advanced image processing pipelines, and quantitatively compared clinical brain growth charts to research brain growth charts derived from >123,000 MRIs.ResultsThe curated SLIP dataset included 372 patients scanned between the ages of 28 days post-birth and 22.2 years across nine 3T MRI scanners. Clinical brain growth charts were highly similar to growth charts derived from large-scale research datasets, in terms of the normative developmental trajectories predicted by the models. The clinical indication of the scans did not significantly bias the output of clinical brain charts. Tens of thousands of additional healthcare system scans meet inclusion criteria to be included in future brain growth charts.ConclusionBrain charts derived from clinical-controls are highly similar to brain charts from research-controls, suggesting that curated clinical scans could be used to supplement research datasets.Summary StatementBrain growth charts of pediatric clinical MRIs with limited imaging pathology (N=372) are highly correlated with charts from a large aggregated set of research controls (N>120,000).Key ResultsA cohort of brain MRI scans with limited reported imaging pathology (N=372, 186 female; ages 0.07 - 22.2 years, median = 10.2) were identified using signed radiology reports and processed using two segmentation pipelines. Growth charts generated from these scans are highly correlated with growth charts from a large aggregated set of research controls (r range 0.990 - 0.999). There was no evidence of bias due to the reason for each scan.

Hypoxic-ischemic encephalopathy (HIE) in infants can have long-term adverse neurodevelopmental effects and markedly reduce quality of life. Both the initial hypoperfusion and the subsequent rapid reperfusion can cause deleterious effects in brain tissue. Cerebral blood flow (CBF) assessment in newborns with HIE can help detect abnormalities in brain perfusion to guide therapy and prognosticate patient outcomes. The review will provide an overview of the pathophysiological implications of CBF derangements in neonatal HIE, current and emerging techniques for CBF quantification, and the potential to utilize CBF as a physiologic target in managing neonates with acute HIE. The alterations of CBF in infants during hypoxia-ischemia have been studied by using different neuroimaging techniques, including nitrous oxide and xenon clearance, transcranial Doppler ultrasonography, contrast-enhanced ultrasound, arterial spin labeling MRI, 18F-FDG positron emission tomography, near-infrared spectroscopy (NIRS), functional NIRS, and diffuse correlation spectroscopy. Consensus is lacking regarding the clinical significance of CBF estimations detected by these different modalities. Heterogeneity in the imaging modality used, regional versus global estimations of CBF, time for the scan, and variables impacting brain perfusion and cohort clinical characteristics should be considered when translating the findings described in the literature to routine practice and implementation of therapeutic interventions.· Hypoxic ischemic injury in infants can result in adverse long-term neurologic sequelae.. · Cerebral blood flow is a useful biomarker in neonatal hypoxic-ischemic injury.. · Imaging modality, variables affecting cerebral blood flow, and patient characteristics affect cerebral blood flow assessment..

Diffuse leptomeningeal glioneuronal tumor (DLGNT) occurs predominantly in children and is typically characterized by diffuse leptomeningeal lesions throughout the neuroaxis with focal segments of parenchymal involvement. Recent reports have identified cases without diffuse leptomeningeal involvement that retain classic glioneuronal features on histology. In this report, we present a case of a 4-year-old boy with a large cystic-solid intramedullary spinal cord lesion that on surgical biopsy revealed a biphasic astrocytic tumor with sparsely distributed eosinophilic granular bodies and Rosenthal fibers. Next-generation sequencing revealed a KIAA1549-BRAF fusion, 1p/19q codeletion, and lack of an IDH1 mutation. Methylation profiling demonstrated a calibrated class score of 0.98 for DLGNT and copy number loss of 1p. Despite the morphologic similarities to pilocytic astrocytoma and the lack of oligodendroglial/neuronal components or leptomeningeal dissemination, the molecular profile was definitive in classifying the tumor as DLGNT. This case highlights the importance of molecular and genetic testing in the characterization of pediatric central nervous system tumors.

Imaging plays an integral role in the management of brain tumors, including tumor diagnosis and classification, treatment planning, and posttreatment surveillance. Conventional magnetic resonance (MR) imaging with gadolinium-based contrast agents on current high field clinical MR systems provide excellent anatomic and morphologic imaging of brain tumors. Anatomic MR imaging can determine the location of intracranial masses, presence of edema, mass effect, calcification, cyst formation, hemorrhage, vascularization, and contrast enhancement. Extra-axial and intra-axial brain tumors can also be discriminated quite accurately by anatomic imaging. However, the assessment of tumor type, grade, and extension, and differentiation of tumors from tumor-like conditions can be limited, potentially affecting therapeutic decision making [1, 2].

Background Clinicians and machine classifiers reliably diagnose pilocytic astrocytoma (PA) on magnetic resonance imaging (MRI) but less accurately distinguish medulloblastoma (MB) from ependymoma (EP). One strategy is to first rule out the most identifiable diagnosis. Objective To hypothesize a sequential machine-learning classifier could improve diagnostic performance by mimicking a clinician's strategy of excluding PA before distinguishing MB from EP. Methods We extracted 1800 total Image Biomarker Standardization Initiative (IBSI)-based features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a 2-step sequential classifier - first ruling out PA, and next distinguishing MB from EP. For each step, we selected the best performing model from 6-candidate classifier using a reduced feature set, and measured performance on a holdout test set with the microaveraged F1 score. Results Optimal diagnostic performance was achieved using 2 decision steps, each with its own distinct imaging features and classifier method. A 3-way logistic regression classifier first distinguished PA from non-PA, with T2 uniformity and T1 contrast as the most relevant IBSI features (F1 score 0.8809). A 2-way neural net classifier next distinguished MB from EP, with T2 sphericity and T1 flatness as most relevant (F1 score 0.9189). The combined, sequential classifier was with F1 score 0.9179. Conclusion An MRI-based sequential machine-learning classifiers offer high-performance prediction of pediatric posterior fossa tumors across a large, multinational cohort. Optimization of this model with demographic, clinical, imaging, and molecular predictors could provide significant advantages for family counseling and surgical planning.

The management of patients with symptomatic rectal masses can be challenging and is further complicated in cases of advanced age, comorbidities, prior surgeries, and acute hemorrhage. In this report, we describe a patient who presented with massive hemorrhage from a 7 cm low rectal tumor with subsequent cardiac arrest. After return of spontaneous circulation, emergent pelvic angiography identified extensive tumor enhancement and blush off the anterior division of the right internal iliac artery from multiple parasitized vessels. The right internal iliac artery was embolized with multiple microcoils to decrease the tumor blood supply and slow the rate of bleeding. The patient was then taken directly from the angiography suite to radiation oncology for planning CT, and within several hours, underwent his first session of radiation. In conjunction with angioembolization, short-course radiation therapy can be an effective treatment modality for advanced bleeding rectal tumors not amenable to surgical resection.

Understanding the immune microenvironment in pediatric low-grade glioma (pLGG) patients may help in identification of the patients who benefit from anti-tumor immunotherapies. However, surgical resection is not feasible for many pLGG tumors in certain anatomical locations. Therefore, developing non-invasive tools that characterize the tumor microenvironment prior to therapeutic interventions could contribute to stratification and enrollment of the patients into relevant clinical trials. In this work, we derived radiomic signatures of immune profiles (radioimmunomics) based on machine learning (ML) analysis of readily available conventional MRI scans. Transcriptomic data for a cohort of 197 subjects was retrospectively collected from Open Pediatric Brain Tumor Atlas (OpenPBTA). The patients were categorized into three groups (Group1-3) based on their immunological profiles using consensus clustering algorithm. This analysis revealed greater immune cell infiltration in non-BRAF mutated pLGGs. Group1 showed more enrichment in M1 macrophages, and microenvironment and immune scores compared to Group2 and Group3. Elevated tumor inflammation score (TIS), as a predictor of clinical response to anti-PD-1 blockade, was observed in Group1 compared to Group2 (p= 1.4e-7) and Group3 (p= 0.0054). Radiomic features, including volumetric, morphologic, histogram, and texture descriptors, were extracted from the segmented tumor regions on multiparametric MRI (mpMRI) scans of 71 (of 197) patients. Multivariate ML models were trained to predict the three immunological groups based on radiomic features using cross-validated random forest classifier along with recursive feature elimination, which yielded AUC of 0.72 for this multi-class classification problem. Our findings indicate the presence of distinct immunological groups in pLGG tumors, with possibly more favorable response to immunotherapies in Group1 tumors. Furthermore, we developed radioimmunomic signatures based on pre-operative conventional mpMRI that can potentially stratify the patients based on their immune tumor microenvironment. Based on these initial promising results, we are exploring additional features to increase the accuracy of radioimmunomics model.

Volumetric measurements of whole tumor and its components on MRI scans, facilitated by automatic segmentation tools, are essential to reduce inter-observer variability in monitoring tumor progression and response assessment for pediatric brain tumors. Here, we present a fully automatic segmentation model based on deep learning that reliably delineates the tumor components recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group for evaluation of treatment response. Multi-parametric MRI (mpMRI) scans (T1-pre, T1-post, T2, and T2-FLAIR), acquired on multiple MRI scanners with different field strengths and vendors, for a cohort of 218 pediatric patients with a variety of histologically confirmed brain tumor subtypes were collected. The mpMRI scans were co-registered and manually segmented by experienced neuroradiologists in consensus to identify the tumor subregions including the enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED) regions. A convolutional neural network model based on DeepMedic architecture was trained using mpMRI scans as the inputs for segmentation of the whole tumor and subregions. The trained model showed excellent performance in segmentation of the whole tumor, as suggested by median dice of 0.90/0.85 for validation (n = 44)/independent test (n = 22) sets. ET and non-enhancing components (union of NET, CC, and ED) were segmented with median dice scores of 0.78/0.84 and 0.76/0.74 for validation/test sets, respectively. The automated and manual segmentations demonstrated strong agreement in estimating VASARI (Visually AcceSAble Rembrandt Images) MRI features with Pearson’s correlation coefficient R > 0.75 (p < 0.0001) for ET, NET, CC, and ED components. Our proposed automated segmentation method developed based on MRI scans acquired with different protocols, equipment, and from a variety of brain tumor subtypes, shows potential application for reliable and generalizable volumetric measurements which can be used for treatment response assessment in clinical trials.

The current era of advanced computing has allowed for the development and implementation of the field of radiomics. In pediatric neuro-oncology, radiomics has been applied in determination of tumor histology, identification of disseminated disease, prognostication, and molecular classification of tumors (ie, radiogenomics). The field also comes with many challenges, such as limitations in study sample sizes, class imbalance, generalizability of the methods, and data harmonization across imaging centers. The aim of this review paper is twofold: first, to summarize existing literature in radiomics of pediatric neuro-oncology; second, to distill the themes and challenges of the field and discuss future directions in both a clinical and technical context.

Advanced magnetic resonance neuroimaging techniques play an important adjunct role to conventional MRI sequences for better depiction and characterization of a variety of brain disorders. In this article we briefly review the basic principles and clinical utility of a select number of these techniques, including clinical functional MRI for presurgical planning, clinical diffusion tensor imaging and related techniques, dynamic susceptibility contrast perfusion imaging using gadolinium injection, and arterial spin labeling perfusion imaging. The article focuses on general principles of clinical MRI acquisition protocols, relevant factors affecting image quality, and a general framework for obtaining images for each of these techniques. We also present relevant advances for acquiring these types of imaging sequences in a clinical setting.

Diffuse leptomeningeal glioneuronal tumor (DLGNT) is characterized by widespread leptomeningeal lesions with focal parenchymal involvement. We present a child with a large intramedullary spinal cord lesion, whose biopsy revealed a biphasic astrocytic tumor consistent with pilocytic astrocytoma. Next-generation sequencing revealed a KIAA1549-BRAF fusion, 1p/19q co-deletion, and no IDH1 mutation. Methylation profiling demonstrated a calibrated class score of 0.98 for DLGNT. Despite morphologic similarities to pilocytic astrocytoma and lack of oligodendroglial/neuronal components or leptomeningeal dissemination, the molecular profile definitively classified the tumor as DLGNT. This case highlights the importance of molecular and genetic testing in diagnosing pediatric central nervous system tumors.

Torcular dural sinus malformations (tDSMs) are rare vascular malformations that present in fetuses and infants. Existing data on prognostic imaging features, as well as the associated morbidity and mortality, are limited and variable. We therefore reviewed cases of tDSMs diagnosed on fetal magnetic resonance imaging (MRI) at our referral center to identify pre- and postnatal MRI imaging features associated with long-term outcomes.We searched our imaging database for fetal and postnatal MRI reports of tDSM cases. The electronic medical record was then reviewed for pre- and postnatal clinical data, including follow-up imaging. Neurological outcomes were characterized using the previously reported scale based on the Bicêtre Score. Imaging features association with outcome scores were compared using the Fisher exact test.Sixteen cases of tDMS diagnosed by fetal MRI with postnatal clinical follow-up were identified, 11 of whom underwent postnatal MRI. The majority of cases of tDSM (73%) decreased in size or resolved on postnatal follow-up study without treatment. Restricted diffusion and parenchymal hemorrhage on fetal MRI were the only imaging features identified significantly associated with unfavorable neurological outcome or death, present in two patients with poor outcomes (two of two) and only one with a normal outcome (one of 14) (P = 0.025).Findings of tDSM on fetal MRI most often regress and/or resolve with normal or mild neurological outcomes, with the most significant predictor of poor outcome being the presence of parenchymal injury on fetal MRI. In addition, a subset will present with venolymphatic malformations.

Moyamoya is a progressive cerebrovascular disorder that leads to stenosis of the arteries in the distal internal carotid, proximal middle cerebral and proximal anterior cerebral arteries of the circle of Willis. Typically a network of collaterals form to bypass the stenosis and maintain cerebral blood flow. As moyamoya progresses it affects the anterior circulation more commonly than posterior circulation, and cerebral blood flow becomes increasingly reliant on external carotid supply. Children with moyamoya are at increased risk for ischemic symptoms including stroke and transient ischemic attacks (TIA). In addition, cognitive decline may occur over time, even in the absence of clinical stroke. Standard of care for stroke prevention in children with symptomatic moyamoya is revascularization surgery. Treatment of children with asymptomatic moyamoya with revascularization surgery however remains more controversial. Therefore, biomarkers are needed to assist with not only diagnosis but also with determining ischemic risk and identifying best surgical candidates. In this review we will discuss the current knowledge as well as gaps in research in relation to pediatric moyamoya biomarkers including neurologic presentation, cognitive, neuroimaging, genetic and biologic biomarkers of disease severity and ischemic risk.