ArticleScientific reports2022
A radiomics-based study for differentiating parasellar cavernous hemangiomas from meningiomas.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Intraventricular giant cavernous malformation mimicking central neurocytoma characterized by digital subtraction angiography: illustrative case.Journal of neurosurgery. Case lessons · 2026Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Advancements in the application of MRI radiomics in meningioma.Radiation oncology (London, England) · 2025Review
- Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas.BMC medical imaging · 2025Article
- A severity classification model of cervical spondylotic radiculopathy symptoms based on MRI radiomics: A retrospective study.PloS one · 2025Article
- Rare case of tentorial cavernous malformation mimicking a meningioma: illustrative case.Journal of neurosurgery. Case lessons · 2024Article
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8 authors.
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Abstract
To investigate the value of the radiomic models for differentiating parasellar cavernous hemangiomas from meningiomas and to compare the classification performance with different MR sequences and classifiers. A total of 96 patients with parasellar tumors (40 cavernous hemangiomas and 56 meningiomas) were enrolled in this retrospective multiple-center study. Univariate and multivariate analyses were performed to identify the clinical factors and semantic features of MRI scans. Radiomics features were extracted from five MRI sequences using radiomics software. Three feature selection methods and six classifiers were evaluated in the training cohort to construct favorable radiomic machine-learning classifiers. The performance of different classifiers was evaluated using the AUC and compared to neuroradiologists. The detection rates of T
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