ArticleBioMed research international2020
Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Multimodal MRI-based nomogram integrating clinical-radiological, radiomic, and habitat features to discriminate solitary fibrous tumors from atypical meningiomas.Scientific reports · 2026Article
- Development of a multimodal magnetic resonance imaging-based machine learning prediction model for flight cadets.Scientific reports · 2026Article
- A Case Report Depicting a Rare Neurosurgical Disease: Aggressive Meningiomatosis.Journal of clinical medicine · 2025Article
- A novel rotation and scale-invariant deep learning framework leveraging conical transformers for precise differentiation between meningioma and solitary fibrous tumor.Journal of pathology informatics · 2025Article
- Article
- Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics.Scientific reports · 2024Article
- Detection of diagnostic and prognostic methylation-based signatures in liquid biopsy specimens from patients with meningiomas.Nature communications · 2023Article
- 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
- Review
- Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram.Frontiers in oncology · 2022Article
- Deep Learning Model for Intracranial Hemangiopericytoma and Meningioma Classification.Frontiers in oncology · 2022Article
- The Feasibility of Combining ADC Value With Texture Analysis of TFrontiers in oncology · 2021Article
- To Explore MR Imaging Radiomics for the Differentiation of Orbital Lymphoma and IgG4-Related Ophthalmic Disease.BioMed research international · 2021Article
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11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIntracranial solitary fibrous tumor(SFT)/hemangiopericytoma (HPC) is an aggressive malignant tumor originating from the intracranial vasculature. Angiomatous meningioma (AM) is a benign tumor with a good prognosis. The imaging manifestations of the two are very similar. Thus, novel noninvasive diagnostic method is urgently needed in clinical practice. Texture analysis and model building through machine learning may have good prospects.
aimTo evaluate whether a 3D-MRI texture feature model could be used to differentiate malignant intracranial SFT/HPC from AM.
methodA total of 97 patients with SFT/HPC and 95 with AM were included in this study. Patients from each group were randomly divided into the train (70%) and test (30%) sets. ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI using ITK-SNAP software. The segmented image was imported into the AK software for texture feature extraction, and the 3D ROI signal intensity histograms of T1WI, T2WI, and contrasted T1WI were automatically obtained along with all the parameters. Modeling was performed using the language R. Confusion matrix was used to analyze the accuracy of the model. ROC curve was constructed to assess the grading ability of the logistic regression model.
resultsAfter Lasso dimension reduction, 5, 9, and 7 texture features were extracted from T1WI, T2WI, and contrasted T1WI, respectively; additional 8 texture features were extracted from the combined sequence for modeling. The ROC analyses on four models resulted in an area under the curve (AUC) of 0.885 (sensitivity 76.1%, specificity 87.9%) for T1WI model, 0.918 (73.1%, 95.5%) for T2WI model, 0.815 (55.2%, 93.9%) for contrasted T1WI model, and 0.959 (92.5%, 84.8%) for the combined sequence model and were enough to correctly distinguish the two groups in 71.2%, 81.4%, 69.5%, and 83.1% of cases in test set, respectively.
conclusionsThe radiological model based on texture features could be used to differentiate SFT/HPC from AM.
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