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✨ Génération du profil synthétique IA en cours…
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Lien Doctolib = recherche Google site:doctolib.fr (le 1er résultat est presque toujours le profil correct s'il existe).
Source : Google News (recherche par nom complet — homonymes possibles, vérifier le contenu).
📰 Le Télégramme · 08/05/2025
<a href="https://news.google.com/rss/articles/CBMixgJBVV95cUxOS1ZCWUVBNW1ibjdVYV9GNWptN3NybklvV2o4NVdTYjUwLWlwNVZnQUJpWTMwWGpJclJobkR6MnlpSDZ1VjZ3ak01STlFNXNacHktdmJ0WjBqWE9LSnRSb0FLdHotVDJybG5qUzVKY1pCdFNaSVppZnVmRmVfRC1zVE5qamdJTENnV014UlJHeDBwRHJGZk1mTEwxZlBxNlU3X1dBN3JQWS15WjRRTlpJUm1QZEd1ZWliVE
📰 Libération · 21/10/2021
<a href="https://news.google.com/rss/articles/CBMi0AFBVV95cUxOdk5WYld5bU8ycUd0VHVXeWJUVlJpMFBTUnFxemluYzhZWUo5YkQ4SFNsbnBuN2hQYlB1ME5WSnlXVC1CNnAzT0x4dnh5WVpQRFV1QWV1eGZPbkYzQ3BEWWxXc1lOOGE5RGQ2N2JpVzMwOEdtT1Z4MXFMaDAwSGxtTlFtbl9zbEFFUlo2MUVQR1lfT1F0YW84TDFnZFRtUW0tVXFvQnpfclJvWWNtNFBjNGp2MW1ZbFA0X0
Radiology. Artificial intelligence · 2022
Scientific data · 2024
AbstractMeningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.
Frontiers in oncology · 2022
BackgroundNeurocognitive deficits in pediatric cancer survivors occur frequently; however, individual outcomes are unpredictable. We investigate clinical, genetic, and imaging predictors of neurocognition in pediatric cancer survivors, with a focus on survivors of central nervous system (CNS) tumors exposed to radiation.MethodsOne hundred eighteen patients with benign or malignant cancers (median diagnosis age: 7; 32% embryonal CNS tumors) were selected from an existing multi-institutional cohort (RadART Pro) if they had: 1) neurocognitive evaluation; 2) available DNA; 3) standard imaging. Utilizing RadART Pro, we collected clinical history, genomic sequencing, CNS imaging, and neurocognitive outcomes. We performed single nucleotide polymorphism (SNP) genotyping for candidate genes associated with neurocognition: COMT, BDNF, KIBRA, APOE, KLOTHO. Longitudinal neurocognitive testing were performed using validated computer-based CogState batteries. The imaging cohort was made of patients with available iron-sensitive (n = 28) and/or T2 FLAIR (n = 41) sequences. Cerebral microbleeds (CMB) were identified using a semi-automated algorithm. Volume of T2 FLAIR white matter lesions (WML) was measured using an automated method based on a convolutional neural network. Summary statistics were performed for patient characteristics, neurocognitive assessments, and imaging. Linear mixed effects and hierarchical models assessed patient characteristics and SNP relationship with neurocognition over time. Nested case-control analysis was performed to compare candidate gene carriers to non-carriers.ResultsCMB presence at baseline correlated with worse performance in 3 of 7 domains, including executive function. Higher baseline WML volumes correlated with worse performance in executive function and verbal learning. No candidate gene reliably predicted neurocognitive outcomes; however, APOE ϵ4 carriers trended toward worse neurocognitive function over time compared to other candidate genes and carried the highest odds of low neurocognitive performance across all domains (odds ratio 2.85, P=0.002). Hydrocephalus and seizures at diagnosis were the clinical characteristics most frequently associated with worse performance in neurocognitive domains (5 of 7 domains). Overall, executive function and verbal learning were the most frequently negatively impacted neurocognitive domains.ConclusionPresence of CMB, APOE ϵ4 carrier status, hydrocephalus, and seizures correlate with worse neurocognitive outcomes in pediatric cancer survivors, enriched with CNS tumors exposed to radiation. Ongoing research is underway to verify trends in larger cohorts.
Source PubMed · Recherche par auteur (homonymes possibles, vérifier l'affiliation).
Radiology. Artificial intelligence · 2022 · Journal Article
Rauschecker AM, Gleason TJ, Nedelec P, Duong MT, et al.
Frontiers in oncology · 2022 · Journal Article
Kline C, Stoller S, Byer L, Samuel D, et al.
Journal of neurosurgery. Case lessons · 2025 · Journal Article
Starkweather CK, Sugrue LP, Nedelec P, Krystal AD, et al.
Scientific data · 2024 · Journal Article
LaBella D, Khanna O, McBurney-Lin S, Mclean R, et al.
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterog
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multi
Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain
Source : DataCite — DOIs pour datasets, logiciels, protocoles, registres patient. Hors articles (déjà couverts).