ArticleJournal of magnetic resonance imaging : JMRI2019
Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.Sovremennye tekhnologii v meditsine · 2021Pooled it
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.Discover oncology · 2026Review
- Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.Surgical neurology international · 2026Review
- Radiomics-based prediction of intraoperative blood loss during intracranial meningiomas surgery.Neurosurgical review · 2025Article
- Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors.World journal of gastrointestinal oncology · 2024Article
- A repository of grade 1 and 2 meningioma MRIs in a public dataset for radiomics reproducibility tests.Medical physics · 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
- Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review.Journal of Korean Neurosurgical Society · 2023Article
- Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.European radiology · 2023Article
- Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method.BMC medical imaging · 2023Article
- Preoperative surgical risk assessment of meningiomas: a narrative review based on MRI radiomics.Neurosurgical review · 2022Review
- Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology.Biomedicines · 2022Review
- Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.Scientific reports · 2022Article
- A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma.Neuroradiology · 2022Article
- Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.Cancers · 2022Review
- A Spotlight on the Role of Radiomics and Machine-Learning Applications in the Management of Intracranial Meningiomas: A New Perspective in Neuro-Oncology: A Review.Life (Basel, Switzerland) · 2022Review
- Transfer learning for medical image classification: a literature review.BMC medical imaging · 2022Review
- Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGrading of meningiomas is important in the choice of the most effective treatment for each patient. PURPOSE: To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images. STUDY TYPE: Retrospective. POPULATION: In all, 117 meningioma-affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III. FIELD STRENGTH/SEQUENCE: 1.5 T, 3.0 T postcontrast enhanced T ASSESSMENT: WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception-V3 and AlexNet DCNNs was tested on ADC maps and PCT STATISTICAL TEST: Leave-one-out cross-validation.
resultsThe application of the Inception-V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88-0.98). Remarkably, only 1/38 WHO Grade II-III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59-0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II-III cases. The discriminating accuracy of both DCNNs on postcontrast T DATA
conclusionDCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152-1159.
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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.