ArticleEuropean radiology2023
Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 3 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
- 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
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- Attention-based deep learning network for predicting World Health Organization meningioma grade and Ki-67 expression based on magnetic resonance imaging.European radiology · 2026Article
- A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.Discover oncology · 2026Review
- Automated segmentation and diagnosis of parotid tumors using a combined deep learning and radiomics model on T2-weighted MRI: a multi-vendor validation study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Differentiation of Low and High-Grade Meningiomas Using Transfer Learning on MR Images.Healthcare technology lettersArticle
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Authors and funding
10 authors.
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Abstract
objectivesTo establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation.
methodsIn total, 257 patients with pathologically confirmed meningiomas (162 low-grade, 95 high-grade) who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted images (T1C), were included in the institutional training set. A two-stage DL grading model was constructed for segmentation and classification based on multiparametric three-dimensional U-net and ResNet. The models were validated in the external validation set consisting of 61 patients with meningiomas (46 low-grade, 15 high-grade). Relevance-weighted Class Activation Mapping (RCAM) method was used to interpret the DL features contributing to the prediction of the DL grading model.
resultsOn external validation, the combined T1C and T2 model showed a Dice coefficient of 0.910 in segmentation and the highest performance for meningioma grading compared to the T2 or T1C only models, with an area under the curve (AUC) of 0.770 (95% confidence interval: 0.644-0.895) and accuracy, sensitivity, and specificity of 72.1%, 73.3%, and 71.7%, respectively. The AUC and accuracy of the combined DL grading model were higher than those of the human readers (AUCs of 0.675-0.690 and accuracies of 65.6-68.9%, respectively). The RCAM of the DL grading model showed activated maps at the surface regions of meningiomas indicating that the model recognized the features at the tumor margin for grading.
conclusionsAn interpretable multiparametric DL model combining T1C and T2 can enable fully automatic grading of meningiomas along with segmentation. KEY POINTS: • The multiparametric DL model showed robustness in grading and segmentation on external validation. • The diagnostic performance of the combined DL grading model was higher than that of the human readers. • The RCAM interpreted that DL grading model recognized the meaningful features at the tumor margin for grading.
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