A foundation model for generalized brain MRI analysis.

Artificial intelligence (AI) applied to brain magnetic resonance imaging (MRI) has the potential to improve disease diagnosis and management but requires algorithms with generalizable knowledge that can perform well in a variety of clinical scenarios. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. Foundation models, by leveraging self-supervised learning, pretraining, and targeted adaptation, present a promising paradigm to overcome these limitations.

Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas.

Background: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning (DL) of magnetic resonance imaging (MRI) tumor features could improve postoperative pLGG risk stratification.

Methods: We used a pretrained DL tool designed for pLGG segmentation to extract pLGG imaging features from preoperative T2-weighted MRI from patients who underwent surgery (DL-MRI features).

Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.

Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from a national brain tumor consortium ( = 184; median age, 7 years [range, 1-23 years]; 94 male patients) and a pediatric cancer center ( = 100; median age, 8 years [range, 1-19 years]; 47 male patients) to develop and evaluate deep learning neural networks for pediatric low-grade glioma segmentation using a stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally tested on an independent test set and subjected to randomized blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests.

Longitudinal risk prediction for pediatric glioma with temporal deep learning.

Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern and severity are heterogeneous and challenging to predict with established clinical and genomic markers. Resultingly, almost all children undergo frequent, long-term, magnetic resonance (MR) brain surveillance regardless of individual recurrence risk. Deep learning analysis of longitudinal MR may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers but has thus far been infeasible with current frameworks.

Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning.

Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, = 214 [113 (52.8%) male; 104 (48.

Automated temporalis muscle quantification and growth charts for children through adulthood.

Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI).

Review of imaging recommendations from Response Assessment in Pediatric Neuro-Oncology (RAPNO).

The Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group includes neuroradiologists, neuro-oncologists, neurosurgeons, radiation oncologists, and clinicians in various additional specialties. This review paper will summarize the imaging recommendations from RAPNO for the six RAPNO publications to date covering pediatric low-grade glioma, pediatric high-grade glioma, medulloblastoma and other leptomeningeal seeding tumors, diffuse intrinsic pontine glioma, ependymoma, and craniopharyngioma.

Complications of Cancer Therapy in Children: A Comprehensive Review of Neuroimaging Findings.

Complications of cancer therapy in children can result in a spectrum of neurologic toxicities that may occur at the initiation of therapy or months to years after treatment. Although childhood cancer remains rare, increasing survival rates mean that more children will be living longer after cancer treatment. Therefore, complications of cancer therapy will most likely occur with increasing frequency.

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning.

Purpose: To develop and externally validate a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pLGG.

Materials And Methods: We conducted a retrospective study of two pLGG datasets with linked genomic and diagnostic T2-weighted MRI of patients: BCH (development dataset, n=214 [60 (28%) BRAF fusion, 50 (23%) BRAF V600E, 104 (49%) wild-type), and Child Brain Tumor Network (CBTN) (external validation, n=112 [60 (53%) BRAF-Fusion, 17 (15%) BRAF-V600E, 35 (32%) wild-type]). We developed a deep learning pipeline to classify BRAF mutational status (V600E vs.

Neurological and Psychological Sequelae Associated With Multisystem Inflammatory Syndrome in Children.

Importance: Acute neurological involvement occurs in some patients with multisystem inflammatory syndrome in children (MIS-C), but few data report neurological and psychological sequelae, and no investigations include direct assessments of cognitive function 6 to 12 months after discharge.

Objective: To characterize neurological, psychological, and quality of life sequelae after MIS-C.

Design, Setting, And Participants: This cross-sectional cohort study was conducted in the US and Canada.

Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning.

Purpose: Artificial intelligence (AI)-automated tumor delineation for pediatric gliomas would enable real-time volumetric evaluation to support diagnosis, treatment response assessment, and clinical decision-making. Auto-segmentation algorithms for pediatric tumors are rare, due to limited data availability, and algorithms have yet to demonstrate clinical translation.

Methods: We leveraged two datasets from a national brain tumor consortium (n=184) and a pediatric cancer center (n=100) to develop, externally validate, and clinically benchmark deep learning neural networks for pediatric low-grade glioma (pLGG) segmentation using a novel in-domain, stepwise transfer learning approach.

The Brain Tumor Segmentation (BraTS) Challenge 2023: .

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations.

Summary of the 2022 ACR Intersociety Meeting.

The ACR Intersociety Committee meeting of 2022 (ISC-2022) was convened around the theme of "Recovering From The Great Resignation, Moral Injury and Other Stressors: Rebuilding Radiology for a Robust Future." Representatives from 29 radiology organizations, including all radiology subspecialties, radiation oncology, and medical physics, as well as academic and private practice radiologists, met for 3 days in early August in Park City, Utah, to search for solutions to the most pressing problems facing the specialty of radiology in 2022. Of these, the mismatch between the clinical workload and the available radiologist workforce was foremost-as many other identifiable problems flowed downstream from this, including high job turnover, lack of time for teaching and research, radiologist burnout, and moral injury.

Harnessing imaging tools to guide immunotherapy trials: summary from the National Cancer Institute Cancer Imaging Steering Committee workshop.

As the immuno-oncology field continues the rapid growth witnessed over the past decade, optimising patient outcomes requires an evolution in the current response-assessment guidelines for phase 2 and 3 immunotherapy clinical trials and clinical care. Additionally, investigational tools-including image analysis of standard-of-care scans (such as CT, magnetic resonance, and PET) with analytics, such as radiomics, functional magnetic resonance agents, and novel molecular-imaging PET agents-offer promising advancements for assessment of immunotherapy. To document current challenges and opportunities and identify next steps in immunotherapy diagnostic imaging, the National Cancer Institute Clinical Imaging Steering Committee convened a meeting with diverse representation among imaging experts and oncologists to generate a comprehensive review of the state of the field.

Imaging of pediatric spine and spinal cord tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee/ASPNR White Paper.

Childhood spinal tumors are rare. Tumors can involve the spinal cord, the meninges, bony spine, and the paraspinal tissue. Optimized imaging should be utilized to evaluate tumors arising from specific spinal compartments.

Imaging of pediatric brain tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee/ASPNR White Paper.

Tumors of the central nervous system are the most common solid malignancies in children and the most common cause of pediatric cancer-related mortality. Imaging plays a central role in diagnosis, staging, treatment planning, and response assessment of pediatric brain tumors. However, the substantial variability in brain tumor imaging protocols across institutions leads to variability in patient risk stratification and treatment decisions, and complicates comparisons of clinical trial results.

Changes in Distribution of Severe Neurologic Involvement in US Pediatric Inpatients With COVID-19 or Multisystem Inflammatory Syndrome in Children in 2021 vs 2020.

Importance: In 2020 during the COVID-19 pandemic, neurologic involvement was common in children and adolescents hospitalized in the United States for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related complications.

Objective: To provide an update on the spectrum of SARS-CoV-2-related neurologic involvement among children and adolescents in 2021.

Design, Setting, And Participants: Case series investigation of patients reported to public health surveillance hospitalized with SARS-CoV-2-related illness between December 15, 2020, and December 31, 2021, in 55 US hospitals in 31 states with follow-up at hospital discharge.

Response assessment in paediatric intracranial ependymoma: recommendations from the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group.

Response criteria for paediatric intracranial ependymoma vary historically and across different international cooperative groups. The Response Assessment in the Pediatric Neuro-Oncology (RAPNO) working group, consisting of an international panel of paediatric and adult neuro-oncologists, neuro-radiologists, radiation oncologists, and neurosurgeons, was established to address both the issues and the unique challenges in assessing the response in children with CNS tumours. We established a subcommittee to develop response assessment criteria for paediatric ependymoma.

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.

Multisystem Inflammatory-like Syndrome in a Child Following COVID-19 mRNA Vaccination.

A 12-year-old male was presented to the hospital with acute encephalopathy, headache, vomiting, diarrhea, and elevated troponin after recent COVID-19 vaccination. Two days prior to admission and before symptom onset, he received the second dose of the Pfizer-BioNTech COVID-19 vaccine. Symptoms developed within 24 h with worsening neurologic symptoms, necessitating admission to the pediatric intensive care unit.

Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study.

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.