Canadian radiology: 2025 update.

Radiology in Canada is evolving through a combination of clinical innovation, collaborative research and the adoption of advanced imaging technologies. This overview highlights contributions from selected academic centres across the country that are shaping diagnostic and interventional practice. At Dalhousie University, researchers have led efforts to improve contrast media safety, refine imaging techniques for hepatopancreatobiliary diseases, and develop peer learning programs that support continuous quality improvement.

Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.

Background: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets along with a comprehensive radiomics pipeline based on our proposed technical protocol to investigate the effects of radiomics feature extraction on the reproducibility of the results.

Methods: We curated large-scale radiomics datasets based on three open-source datasets; BraTS 2020 for high-grade glioma (HGG) versus low-grade glioma (LGG) classification and survival analysis, BraTS 2023 for O6-methylguanine-DNA methyltransferase (MGMT) classification, and non-small cell lung cancer (NSCLC) survival analysis from the Cancer Imaging Archive (TCIA).

Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.

Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segmentation generally requires manually annotated segmentations, demanding extensive time and resources from radiologists. We propose a weakly supervised approach that utilizes binary image-level labels, which are much simpler to acquire, rather than manual annotations to segment brain tumors on magnetic resonance images.

Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis.

Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. That is mainly due to the fact that the features contributing to a model's prediction are unclear to radiologists and hence, clinically irrelevant, i.e.

Anomaly Detection in Pediatric and Adults Brain MRI with Generative Model.

Magnetic Resonance Imaging (MRI) serves as a valuable tool for detecting abnormalities in brain structures. However, a notable 5-10% of pathologies remain unnoticed in MRI scans. To address this challenge and reduce the burden on radiologists, machine learning methods have been used to automate anomaly detection.

Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks.

Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.

Artificial intelligence in emergency neuroradiology: Current applications and perspectives.

Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brain, head and neck, and spine. This narrative review aims at providing an up-to-date discussion about the state of the art of applications of artificial intelligence in emergency neuroradiology, which have substantially expanded in depth and scope in the past few years. A detailed analysis of machine learning and deep learning algorithms in several tasks related to acute ischemic stroke involving various imaging modalities, including a description of existing commercial products, is provided.

Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumours Molecular Subtype Identification Using MRI-based 3D Probability Distributions of Tumour Location.

Pediatric low-grade gliomas (pLGG) are the most common brain tumour in children, and the molecular diagnosis of pLGG enables targeted treatment. We use MRI-based Convolutional Neural Networks (CNNs) for molecular subtype identification of pLGG and augment the models using tumour location probability maps. MRI FLAIR sequences of 214 patients (110 male, mean age of 8.

The spine-brain axis: is spinal anatomy associated with brain volume?

First small sample studies indicate that disturbances of spinal morphology may impair craniospinal flow of cerebrospinal fluid and result in neurodegeneration. The aim of this study was to evaluate the association of cervical spinal canal width and scoliosis with grey matter, white matter, ventricular and white matter hyperintensity volumes of the brain in a large study sample. Four hundred participants underwent whole-body 3 T magnetic resonance imaging.

Imaging endolymphatic space of the inner ear in vestibular migraine.

Background: Vestibular migraine (VM), the most frequent episodic vertigo, is difficult to distinguish from Ménière's disease (MD) because reliable biomarkers are missing. The classical proof of MD was an endolymphatic hydrops (EH). However, a few intravenous gadolinium-enhanced MRI studies of the inner ear (MRI) also revealed an EH in VM.

Developmental Curves of the Paediatric Brain Using FLAIR MRI Texture Biomarkers.

Analysis of FLAIR MRI sequences is gaining momentum in brain maturation studies, and this study aimed to establish normative developmental curves for FLAIR texture biomarkers in the paediatric brain. A retrospective, single-centre dataset of 465/512 healthy paediatric FLAIR volumes was used, with one pathological volume for proof-of-concept. Participants were included if the MRI was unremarkable as determined by a neuroradiologist.

Applications of machine learning to MR imaging of pediatric low-grade gliomas.

Introduction: Machine learning (ML) shows promise for the automation of routine tasks related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading, typing, and segmentation. Moreover, it has been shown that ML can identify crucial information from medical images that is otherwise currently unattainable. For example, ML appears to be capable of preoperatively identifying the underlying genetic status of pLGG.

Canadian radiology: 2024 update.

Radiology in Canada is advancing through innovations in clinical practices and research methodologies. Recent developments focus on refining evidence-based practice guidelines, exploring innovative imaging techniques and enhancing diagnostic processes through artificial intelligence. Within the global radiology community, Canadian institutions play an important role by engaging in international collaborations, such as with the American College of Radiology to refine implementation of the Ovarian-Adnexal Reporting and Data System for ultrasound and magnetic resonance imaging.

Resistance, rebound, and recurrence regrowth patterns in pediatric low-grade glioma treated by MAPK inhibition: A modified Delphi approach to build international consensus-based definitions-International Pediatric Low-Grade Glioma Coalition.

Pediatric low-grade glioma (pLGG) is the most common childhood brain tumor group. The natural history, when curative resection is not possible, is one of a chronic disease with periods of tumor stability and episodes of tumor progression. While there is a high overall survival rate, many patients experience significant and potentially lifelong morbidities.

Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning.

Background And Purpose: Molecular biomarker identification increasingly influences the treatment planning of pediatric low-grade neuroepithelial tumors (PLGNTs). We aimed to develop and validate a radiomics-based ADC signature predictive of the molecular status of PLGNTs.

Materials And Methods: In this retrospective bi-institutional study, we searched the PACS for baseline brain MRIs from children with PLGNTs.

Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology.

Background And Purpose: Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and characteristics. This study aims to characterize the emergence and evolution of AI/ML articles within neuroradiology and provide a comprehensive overview of the trends, challenges, and future directions of the field.

Materials And Methods: We performed a bibliometric analysis of the ; the journal was queried for original research articles published since inception (January 1, 1980) to December 3, 2022 that contained any of the following key terms: "machine learning," "artificial intelligence," "radiomics," "deep learning," "neural network," "generative adversarial network," "object detection," or "natural language processing.

Brain Tumor Imaging in Adolescents and Young Adults: 2021 WHO Updates for Molecular-based Tumor Types.

Published in 2021, the fifth edition of the World Health Organization (WHO) classification of tumors of the central nervous system (CNS) introduced new molecular criteria for tumor types that commonly occur in either pediatric or adult age groups. Adolescents and young adults (AYAs) are at the intersection of adult and pediatric care, and both pediatric-type and adult-type CNS tumors occur at that age. Mortality rates for AYAs with CNS tumors have increased by 0.

Artificial Intelligence in the Future Landscape of Pediatric Neuroradiology: Opportunities and Challenges.

This paper will review how artificial intelligence (AI) will play an increasingly important role in pediatric neuroradiology in the future. A safe, transparent, and human-centric AI is needed to tackle the quadruple aim of improved health outcomes, enhanced patient and family experience, reduced costs, and improved well-being of the healthcare team in pediatric neuroradiology. Equity, diversity and inclusion, data safety, and access to care will need to always be considered.