ArticleJournal of personalized medicine2021
Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 4 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Neuroinformatics · 2025Pooled it
- Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.Discover oncology · 2026Review
- Multiparameter MRI-based automatic segmentation and diagnostic models for the differentiation of intracranial solitary fibrous tumors and meningiomas.Annals of medicine · 2025Article
- Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Predicting meningioma grades and pathologic marker expression via deep learning.European radiology · 2024Article
- A semi-automatic segmentation method for meningioma developed using a variational approach model.The neuroradiology journal · 2024Article
- Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets.Journal of medical Internet research · 2023Article
- Deep-learning model for diagnostic clue: detecting the dural tail sign for meningiomas on contrast-enhanced T1 weighted images.Quantitative imaging in medicine and surgery · 2023Article
- Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization.Cancers · 2022Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed using deep-learning technology for automatic tumor detection, visual assessment, and grading prediction. Specifically, a modified U-Net convolutional neural network was first established to segment tumor images. Subsequently, the segmentations were introduced into rendering algorithms for spatial reconstruction and another DenseNet convolutional neural network for grading prediction. The trained models were integrated as a system, and the robustness was tested based on its performance on an external dataset from the second institution involving different magnetic resonance imaging platforms. The results showed that the segment model represented a noteworthy performance with dice coefficients of 0.920 ± 0.009 in the validation group. With accurate segmented tumor images, the rendering model delicately reconstructed the tumor body and clearly displayed the important intracranial vessels. The DenseNet model also achieved high accuracy with an area under the curve of 0.918 ± 0.006 and accuracy of 0.901 ± 0.039 when classifying tumors into low-grade and high-grade meningiomas. Moreover, the system exhibited good performance on the external validation dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.