Imaging Clusters of Pediatric Low-Grade Glioma are Associated with Distinct Molecular Characteristics.

Background And Purpose: Cancers show heterogeneity at various levels, from genome to radiological imaging. This study aimed to explore the interplay between genomic, transcriptomic, and radiophenotypic data in pediatric low-grade glioma (pLGG), the most common group of brain tumors in children.

Materials And Methods: We analyzed data from 201 pLGG patients in the Children's Brain Tumor Network (CBTN), using principal component analysis and K-Means clustering on 881 radiomic features, along with clinical variables (age, sex, tumor location), to identify imaging clusters and examine their association with 2021 WHO pLGG classifications.

A single-arm phase 2 study of abemaciclib in adult patients with recurrent grade 3 oligodendroglioma.

Background: Novel treatments are needed for oligodendroglioma that has recurred following radiotherapy (RT) and chemotherapy. The cyclin D1-CDK4 axis is frequently dysregulated in oligodendroglioma. Abemaciclib is a selective CDK4/6 inhibitor that achieves pharmacologically relevant concentrations in brain tumor tissue.

Corticosteroid-dependent association between prognostic peripheral blood cell-free DNA levels and neutrophil-mediated NETosis in patients with glioblastoma.

Purpose: Non-invasive prognostic biomarkers to inform clinical decision-making are an urgent unmet need for the management of patients with glioblastoma (GBM). We previously showed that higher circulating cell-free DNA concentration [ccfDNA] is associated with worse survival in GBM. However, the biology underlying this is unknown.

Fronto-Orbital Osteoblastoma With Unilateral Exophthalmos: A Rare Presentation and Review of Diagnostic Challenges.

Osteoblastoma is an uncommon benign bone tumor rarely involving the craniofacial skeleton. Manifestations in the fronto-orbital region are exceptionally rare. A 19-year-old man presented with persistent headache, nausea, vomiting, right eye pain, and longstanding right exophthalmos.

Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.

Background: Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack generalizability, particularly for rare tumors in the sellar/suprasellar regions and when applied to real-world clinical data in limited data scenarios. To address these challenges, we propose AI-driven techniques for skull-stripping and tumor segmentation.

The association between thyroid function and insulin resistance as measured by the metabolic score for insulin resistance (METS-IR): insights from NHANES 2007-2012.

Background: Altered thyroid function has been linked to insulin resistance (IR), but its relationship with the Metabolic Score for Insulin Resistance (METS-IR), a novel non-insulin-based index of IR, remains unclear. This study aimed to investigate the association between thyroid function status and METS-IR in a U.S.

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.

Enhancing clinical decision-making: An externally validated machine learning model for predicting isocitrate dehydrogenase mutation in gliomas using radiomics from presurgical magnetic resonance imaging.

Background: Glioma, the most prevalent primary brain tumor, poses challenges in prognosis, particularly in the high-grade subclass, despite advanced treatments. The recent shift in tumor classification underscores the crucial role of isocitrate dehydrogenase (IDH) mutation status in the clinical care of glioma patients. However, conventional methods for determining IDH status, including biopsy, have limitations.

Empowering Data Sharing in Neuroscience: A Deep Learning De-identification Method for Pediatric Brain MRIs.

Background And Purpose: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging datasets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this, however existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility.

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.

A Neuroradiologist's Guide to Operationalizing the Response Assessment in Neuro-Oncology (RANO) Criteria Version 2.0 for Gliomas in Adults.

Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults.

University of Washington Quality of Life subdomain outcomes after treatment of sinonasal malignancy: A prospective, multicenter study.

Purpose: Sinonasal malignancies (SNMs) adversely impact patients' quality of life (QOL) and are frequently identified at an advanced stage. Because these tumors are rare, there are few studies that examine the specific QOL areas that are impacted. This knowledge would help improve the care of these patients.

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

Background And Purpose: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images.

Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.

Deep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the most informative images using real-world clinical datasets for brain tumor segmentation and proposing a framework that minimizes the data needed while maintaining performance. Then, 638 multi-institutional brain tumor magnetic resonance imaging scans were used to train three-dimensional U-net models and compare active learning strategies.

Intrathecal bivalent CAR T cells targeting EGFR and IL13Rα2 in recurrent glioblastoma: phase 1 trial interim results.

Recurrent glioblastoma (rGBM) remains a major unmet medical need, with a median overall survival of less than 1 year. Here we report the first six patients with rGBM treated in a phase 1 trial of intrathecally delivered bivalent chimeric antigen receptor (CAR) T cells targeting epidermal growth factor receptor (EGFR) and interleukin-13 receptor alpha 2 (IL13Rα2). The study's primary endpoints were safety and determination of the maximum tolerated dose.

Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial.

Background And Purpose: Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response assessment. There is sparse research on the role of volumetrics in pediatric gliomas.

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 Quality Evaluation Index for Arterial Spin Labeling Derived Cerebral Blood Flow Maps.

Background: Arterial spin labeling (ASL) derived cerebral blood flow (CBF) maps are prone to artifacts and noise that can degrade image quality.

Purpose: To develop an automated and objective quality evaluation index (QEI) for ASL CBF maps.

Study Type: Retrospective.

Introduction to the Special Issue on "Role of Novel Imaging Technique in Brain Tumors".

In recent years, significant strides have been made in the field of neuro-oncology imaging, contributing to our understanding and management of brain tumors [...