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.

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.

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.

Pediatric-type diffuse low-grade gliomas.

The World Health Organization's 5th edition classification of Central Nervous System (CNS) tumors differentiates diffuse gliomas into adult and pediatric variants. Pediatric-type diffuse low-grade gliomas (pDLGGs) are distinct from adult gliomas in their molecular characteristics, biological behavior, clinical progression, and prognosis. Various molecular alterations identified in pDLGGs are crucial for treatment.

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.

Brain Volumes in Opsoclonus-Myoclonus Ataxia Syndrome: A Longitudinal Study.

Introduction: Little is known about the longitudinal trajectory of brain growth in children with opsoclonus-myoclonus ataxia syndrome. We performed a longitudinal evaluation of brain volumes in pediatric opsoclonus-myoclonus ataxia syndrome patients compared with age- and sex-matched healthy children.

Patients And Methods: This longitudinal case-control study included brain magnetic resonance imaging (MRI) scans from consecutive pediatric opsoclonus-myoclonus ataxia syndrome patients (2009-2020) and age- and sex-matched healthy control children.

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.

The unintended consequences of artificial intelligence in paediatric radiology.

Over the past decade, there has been a dramatic rise in the interest relating to the application of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with ease of access to powerful computer processing capabilities, we are becoming more able to generate complex and nuanced prediction models and elaborate solutions for healthcare. Nevertheless, these AI models are not without their failings, and sometimes the intended use for these solutions may not lead to predictable impacts for patients, society or those working within the healthcare profession.

Increased rate of significant findings on brain MRI during the early stage of the COVID-19 pandemic.

Objectives: To assess the effect of the COVID-19 pandemic on the proportion of abnormal paediatric neuroimaging findings as a surrogate marker for potential underutilisation.

Methods: Consecutive paediatric brain MRIs performed between March 27th and June 19th 2019 (T) and March 23rd and June 1st 2020 (T) were reviewed and classified according to presence or absence and type of imaging abnormality, and graded regarding severity on a 5-point Likert scale, where grade 4 was defined as abnormal finding requiring non-urgent intervention and grade 5 was defined as acute illness prompting urgent medical intervention. Non-parametric statistical testing was used to assess for significant differences between T vs.

MRI-Based End-To-End Pediatric Low-Grade Glioma Segmentation and Classification.

MRI-based radiomics models can predict genetic markers in pediatric low-grade glioma (pLGG). These models usually require tumour segmentation, which is tedious and time consuming if done manually. We propose a deep learning (DL) model to automate tumour segmentation and build an end-to-end radiomics-based pipeline for pLGG classification.

Accuracy of Information and References Using ChatGPT-3 for Retrieval of Clinical Radiological Information.

To assess the accuracy of answers provided by ChatGPT-3 when prompted with questions from the daily routine of radiologists and to evaluate the text response when ChatGPT-3 was prompted to provide references for a given answer. ChatGPT-3 (San Francisco, OpenAI) is an artificial intelligence chatbot based on a large language model (LLM) that has been designed to generate human-like text. A total of 88 questions were submitted to ChatGPT-3 using textual prompt.

MRI Volumetric Analysis of the Hypothalamus and Limbic System across the Pediatric Age Span.

Purpose: Literature is scarce regarding volumetric measures of limbic system components across the pediatric age range. The purpose of this study is to remedy this scarcity by reporting continuous volumetric measurements of limbic system components, and to provide consistent stratification data including age-related trajectories and sex-related differences in the pediatric age range in order to improve the recognition of structural variations that might reflect pathology.

Methods: In this retrospective study, MRI sequences of children with normal clinical MRI examinations of the brain acquired between January 2010 and December 2019 were included.

Volumetric differences of thalamic nuclei in children with trisomy 21.

Objectives: Histological studies have shown alterations of thalamic nuclei in patients with Down syndrome (DS). The correlation of these changes on MRI (magnetic resonance imaging) is unclear. Therefore, this study investigates volumetric differences of thalamic nuclei in children with DS compared to controls.

Fet-Net Algorithm for Automatic Detection of Fetal Orientation in Fetal MRI.

Identifying fetal orientation is essential for determining the mode of delivery and for sequence planning in fetal magnetic resonance imaging (MRI). This manuscript describes a deep learning algorithm named Fet-Net, composed of convolutional neural networks (CNNs), which allows for the automatic detection of fetal orientation from a two-dimensional (2D) MRI slice. The architecture consists of four convolutional layers, which feed into a simple artificial neural network.

An Educational Graphical User Interface to Construct Convolutional Neural Networks for Teaching Artificial Intelligence in Radiology.

Deep learning techniques using convolutional neural networks (CNNs) have been successfully developed for various medical image analysis tasks. However, the skills to understand and develop deep learning models are not usually taught during radiology training, which constitutes a barrier for radiologists looking to integrate machine learning (ML) into their research or clinical practice. In this work, we developed and evaluated an educational graphical user interface (GUI) to construct CNNs for teaching deep learning concepts to radiology trainees.

Improving the Segmentation of Pediatric Low-Grade Gliomas Through Multitask Learning.

Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While there has been extensive research focused on optimizing brain tumor segmentation in the adult population, studies on AI guided pediatric tumor segmentation are scarce.

MRI based radiomics enhances prediction of neurodevelopmental outcome in very preterm neonates.

To predict adverse neurodevelopmental outcome of very preterm neonates. A total of 166 preterm neonates born between 24-32 weeks' gestation underwent brain MRI early in life. Radiomics features were extracted from T1- and T2- weighted images.