AIMC Topic: Meningioma
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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
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...
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.
Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.
Journal of computer assisted tomography
Nov 27, 2023
PURPOSE: This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images.
Predicting meningioma grades and pathologic marker expression via deep learning.
European radiology
Oct 19, 2023
OBJECTIVES: To establish a deep learning (DL) model for predicting tumor grades and expression of pathologic markers of meningioma.
TumorDetNet: A unified deep learning model for brain tumor detection and classification.
PloS one
Sep 27, 2023
Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, ...
Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.
Journal of magnetic resonance imaging : JMRI
Aug 12, 2023
BACKGROUND: Deep learning-based segmentation algorithms usually required large or multi-institute data sets to improve the performance and ability of generalization. However, protecting patient privacy is a key concern in the multi-institutional stud...
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