AIMC Topic: Meningeal Neoplasms
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Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments.
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Jun 11, 2024
PURPOSE: MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially availa...
Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.
Behavioural neurology
May 30, 2024
The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth grade of the disease. As a result, the medical plan may be a crucial step toward improving the well-being of a patient. Both diagnosis and therapy are...
Combined radiomics nomogram of different machine learning models for preoperative distinguishing intraspinal schwannomas and meningiomas: a multicenter and comparative study.
Clinical radiology
May 16, 2024
AIMS: The objective of our study was to establish and verify a novel combined model based on multiparameter magnetic resonance imaging (MRI) radiomics and clinical features to distinguish intraspinal schwannomas from meningiomas.
Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions.
Physics in medicine and biology
Apr 26, 2024
To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques.The primary focus is on develo...
Harnessing generative artificial intelligence for meningioma prediction: a correspondence.
Neurosurgical review
Apr 23, 2024
Employing deep learning and transfer learning for accurate brain tumor detection.
Scientific reports
Mar 27, 2024
Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosi...
Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.
BMC medical imaging
Mar 5, 2024
BACKGROUND: This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentatio...
Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas.
Academic radiology
Feb 26, 2024
A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and...
Detection and classification of brain tumor using hybrid deep learning models.
Scientific reports
Dec 27, 2023
Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diag...
Meningioma consistency assessment based on the fusion of deep learning features and radiomics features.
European journal of radiology
Dec 5, 2023
PURPOSE: This study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency.
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