Intracranial Gunshot Wounds: An Assessment of Patient Characteristics on Surgical Outcomes.

Background/objective: Intracranial gunshot wounds (GSW) are often fatal, with most patients dying before intervention can occur. Surgical management, when indicated, results in decreased mortality. We sought to assess the neurosurgical outcomes and economic costs of intracranial GSW.

MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study.

Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified.

Attention-guided deep learning for gestational age prediction using fetal brain MRI.

Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R score of 0.

MRI Correlates of Ototoxicity in the Auditory Pathway in Children Treated for Medulloblastoma.

Objective: To assess diffusion and perfusion changes of the auditory pathway in pediatric medulloblastoma patients exposed to ototoxic therapies.

Study Design: Retrospective cohort study.

Setting: A single academic tertiary children's hospital.

Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study.

Background And Purpose: Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor detection and tumor pathology classification.

Erratum: Author Correction: PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.

[This corrects the article DOI: 10.1038/s41746-020-0266-y.].

PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.

Pulmonary embolism (PE) is a life-threatening clinical problem and computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosis. Prompt diagnosis and immediate treatment are critical to avoid high morbidity and mortality rates, yet PE remains among the diagnoses most frequently missed or delayed. In this study, we developed a deep learning model-PENet, to automatically detect PE on volumetric CTPA scans as an end-to-end solution for this purpose.

Altered cerebral perfusion in children with Langerhans cell histiocytosis after chemotherapy.

Background And Purpose: Children with Langerhans cell histiocytosis (LCH) may develop a wide array of neurological symptoms, but associated cerebral physiologic changes are poorly understood. We examined cerebral hemodynamic properties of pediatric LCH using arterial spin-labeling (ASL) perfusion magnetic resonance imaging (MRI).

Materials And Methods: A retrospective study was performed in 23 children with biopsy-proven LCH.

Diffusion tensor magnetic resonance imaging of the optic nerves in pediatric hydrocephalus.

Objective: While conventional imaging can readily identify ventricular enlargement in hydrocephalus, structural changes that underlie microscopic tissue injury might be more difficult to capture. MRI-based diffusion tensor imaging (DTI) uses properties of water motion to uncover changes in the tissue microenvironment. The authors hypothesized that DTI can identify alterations in optic nerve microstructure in children with hydrocephalus.

Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs.

Purpose: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs.

Materials And Methods: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set.

Arterial spin labeling perfusion changes of the frontal lobes in children with posterior fossa syndrome.

Objective: Posterior fossa syndrome (PFS) is a common complication following the resection of posterior fossa tumors in children. The pathophysiology of PFS remains incompletely elucidated; however, the wide-ranging symptoms of PFS suggest the possibility of widespread cortical dysfunction. In this study, the authors utilized arterial spin labeling (ASL), an MR perfusion modality that provides quantitative measurements of cerebral blood flow without the use of intravenous contrast, to assess cortical blood flow in patients with PFS.

Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model.

Importance: Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic.

Objective: To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxel-by-voxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance.

Age-Dependent White Matter Characteristics of the Cerebellar Peduncles from Infancy Through Adolescence.

Cerebellum-cerebrum connections are essential for many motor and cognitive functions and cerebellar disorders are prevalent in childhood. The middle (MCP), inferior (ICP), and superior cerebellar peduncles (SCP) are the major white matter pathways that permit communication between the cerebellum and the cerebrum. Knowledge about the microstructural properties of these cerebellar peduncles across childhood is limited.

MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma.

Background And Purpose: Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma.

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.

Background: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses.

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

Background: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets.

Sex Differences in Cognitive Decline in Subjects with High Likelihood of Mild Cognitive Impairment due to Alzheimer's disease.

Sex differences in Alzheimer's disease (AD) biology and progression are not yet fully characterized. The goal of this study is to examine the effect of sex on cognitive progression in subjects with high likelihood of mild cognitive impairment (MCI) due to Alzheimer's and followed up to 10 years in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cerebrospinal fluid total-tau and amyloid-beta (Aβ42) ratio values were used to sub-classify 559 MCI subjects (216 females, 343 males) as having "high" or "low" likelihood for MCI due to Alzheimer's.

Educational attainment and hippocampal atrophy in the Alzheimer's disease neuroimaging initiative cohort.

Introduction: Subjects with higher cognitive reserve (CR) may be at a lower risk for Alzheimer's disease (AD), but the neural mechanisms underlying this are not known. Hippocampal volume loss is an early event in AD that triggers cognitive decline.

Materials And Methods: Regression analyses of the effects of education on MRI-measured baseline HV in 675 subjects (201 normal, 329 with mild cognitive impairment (MCI), and 146 subjects with mild AD), adjusting for age, gender, APOE ɛ4 status and intracranial volume (ICV).

Impact of ¹⁸F-florbetapir PET imaging of β-amyloid neuritic plaque density on clinical decision-making.

¹⁸F-florbetapir positron emission tomography (PET) imaging of the brain is now approved by the Food and Drug Administration (FDA) approved for estimation of β -amyloid neuritic plaque density when evaluating patients with cognitive impairment. However, its impact on clinical decision-making is not known. We present 11 cases (age range 67-84) of cognitively impaired subjects in whom clinician surveys were done before and after PET scanning to document the theoretical impact of amyloid imaging on the diagnosis and treatment plan of cognitively impaired subjects.

Mapping the effects of ApoE4, age and cognitive status on 18F-florbetapir PET measured regional cortical patterns of beta-amyloid density and growth.

Background: Although it is well known that many clinical and genetic factors have been associated with beta-amyloid deposition, few studies have examined the interactions of such factors across different stages of Alzheimer's pathogenesis.

Methods: We used 18F-florbetapir F18 PET imaging to quantify neuritic beta-amyloid plaque density across four cortical regions in 602 elderly (55-94 years) subjects from the national ADNI biomarker study. The group comprised of 194 normal elderly, 212 early mild cognitive impairment [EMCI], 132 late mild cognitive impairment [LMCI], and 64 mild Alzheimer's (AD).