ArticleAnnals of clinical and translational neurology2023
A radiomics model enables prediction venous sinus invasion in meningioma.
Article in Annals of clinical and translational neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- MRI radiomics in diagnosing high and low grade meningiomas through systematic review and meta analysis.Scientific reports · 2025Pooled it
- MRI-based preoperative WHO grade prediction in meningioma: development and comparison of deep learning, radiomics, clinical, and combined models in multicenter data.Journal of neuro-oncology · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study.BMC medical imaging · 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
- MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study.Frontiers in neurology · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
objectivePreoperative prediction of meningioma venous sinus invasion would facilitate the selection of surgical approaches and predicting the prognosis. To predict venous sinus invasion in meningiomas, we used radiomic signatures to construct a model based on preoperative contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging.
methodsIn total, 599 patients with pathologically confirmed meningioma were retrospectively enrolled. For each patient enrolled in this study, 1595 radiomic signatures were extracted from T1C and T2 image sequences. Pearson correlation analysis and recursive feature elimination were used to select the most relevant signatures extracted from different image sequences, and logistic regression algorithms were used to build a radiomic model for risk prediction of meningioma sinus invasion. Furthermore, a nomogram was built by incorporating clinical characteristics and radiomic signatures, and a decision curve analysis was used to evaluate the clinical utility of the nomogram.
resultsTwenty radiomic signatures that were significantly related to venous sinus invasion were screened from 3190 radiomic signatures. Venous sinus invasion was associated with tumor position, and the clinicoradiomic model that incorporated the above characteristics (20 radiomic signatures and tumor position) had the best discriminating ability. The areas under the curve for the training and validation cohorts were 0.857 (95% confidence interval [CI], 0.824-0.890) and 0.824 (95% CI, 0.752-0.8976), respectively.
interpretationThe clinicoradiomic model had good predictive performance for venous sinus invasion in meningioma, which can aid in devising surgical strategies and predicting prognosis.
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