ArticleNeuroradiology2022
A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma.
Article in Neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- MRI radiomics in diagnosing high and low grade meningiomas through systematic review and meta analysis.Scientific reports · 2025Pooled 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
- 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
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- MRI and Meningioma Research: Hotspots and Trends via Bibliometric AnalysisCurrent medical imaging · 2026Article
- Advancements in the application of MRI radiomics in meningioma.Radiation oncology (London, England) · 2025Review
- An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade.Scientific reports · 2025Article
- Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas.BMC medical imaging · 2025Article
- MRI-based deep transfer learning models for predicting progesterone receptor expression in meningioma.Frontiers in oncology · 2025Article
- Study of prediction model for high-grade meningioma using fractal geometry combined with radiological features.Journal of neuro-oncology · 2025Article
- Differentiating low-risk thymomas from high-risk thymomas: preoperative radiomics nomogram based on contrast enhanced CT to minimize unnecessary invasive thoracotomy.BMC medical imaging · 2024Article
- Surgical Management of High-Grade Meningiomas.Cancers · 2024Review
- Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.BMC medical imaging · 2024Article
- Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study.BMC cancer · 2024Article
- Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter study.Frontiers in oncology · 2024Article
- Classification of nasal polyps and inverted papillomas using CT-based radiomics.Insights into imaging · 2023Article
- A radiomics model enables prediction venous sinus invasion in meningioma.Annals of clinical and translational neurology · 2023Article
- Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model.Frontiers in oncology · 2023Article
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8 authors.
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Abstract
purposeThis study aimed to investigate the clinical usefulness of the enhanced-T1WI-based deep learning radiomics model (DLRM) in differentiating low- and high-grade meningiomas.
methodsA total of 132 patients with pathologically confirmed meningiomas were consecutively enrolled (105 in the training cohort and 27 in the test cohort). Radiomics features and deep learning features were extracted from T1 weighted images (T1WI) (both axial and sagittal) and the maximum slice of the axial tumor lesion, respectively. Then, the synthetic minority oversampling technique (SMOTE) was utilized to balance the sample numbers. The optimal discriminative features were selected for model building. LightGBM algorithm was used to develop DLRM by a combination of radiomics features and deep learning features. For comparison, a radiomics model (RM) and a deep learning model (DLM) were constructed using a similar method as well. Differentiating efficacy was determined by using the receiver operating characteristic (ROC) analysis.
resultsA total of 15 features were selected to construct the DLRM with SMOTE, which showed good discrimination performance in both the training and test cohorts. The DLRM outperformed RM and DLM for differentiating low- and high-grade meningiomas (training AUC: 0.988 vs. 0.980 vs. 0.892; test AUC: 0.935 vs. 0.918 vs. 0.718). The accuracy, sensitivity, and specificity of the DLRM with SMOTE were 0.926, 0.900, and 0.924 in the test cohort, respectively.
conclusionThe DLRM with SMOTE based on enhanced T1WI images has favorable performance for noninvasively individualized prediction of meningioma grades, which exhibited favorable clinical usefulness superior over the radiomics features.
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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.