ArticleJournal of neuro-oncology2024
The development of a combined clinico-radiomics model for predicting post-operative recurrence in atypical meningiomas: a multicenter study.
Article in Journal of neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 9 citations in OpenAlex.
- Machine learning-based models and radiomics: can they be reliable predictors for meningioma recurrence? A systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Machine learning based radiomics approach for outcome prediction of meningioma - a systematic review.F1000Research · 2025Pooled it
- Multidisciplinary management of meningiomas in the era of precision oncology.Nature reviews. Clinical oncology · 2026Review
- Advancements in the application of MRI radiomics in meningioma.Radiation oncology (London, England) · 2025Review
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics.Scientific reports · 2024Article
- Harnessing generative artificial intelligence for meningioma prediction: a correspondence.Neurosurgical review · 2024Article
- A study combining microbubble-mediated focused ultrasound and radiation therapy in the healthy rat brain and a F98 glioma model.Scientific reports · 2024Article
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Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
Abstract
purposeAtypical meningiomas could manifest early recurrence after surgery and even adjuvant radiotherapy. We aimed to construct a clinico-radiomics model to predict post-operative recurrence of atypical meningiomas based on clinicopathological and radiomics features. MATERIALS AND
methodsThe study cohort was comprised of 224 patients from two neurosurgical centers. 164 patients from center I were divided to the training cohort for model development and the testing cohort for internal validation. 60 patients from center II were used for external validation. Clinicopathological characteristics, radiological semantic, and radiomics features were collected. A radiomic signature was comprised of four radiomics features. A clinico-radiomics model combining the radiomics signature and clinical characteristics was constructed to predict the recurrence of atypical meningiomas.
results1920 radiomics features were extracted from the T1 Contrast and T2-FLAIR sequences of patients in center I. The radiomics signature was able to differentiate post-operative patients into low-risk and high-risk groups based on tumor recurrence (P < 0.001). A clinic-radiomics model was established by combining age, extent of resection, Ki-67 index, surgical history and the radiomics signature for recurrence prediction in atypical meningiomas. The model achieved a good prediction performance with the integrated AUC of 0.858 (0.802-0.915), 0.781 (0.649-0.912) and 0.840 (0.747-0.933) in the training, internal validation and external validation cohort, respectively.
conclusionsThe present study established a radiomics signature and a clinico-radiomics model with a favorable performance in predicting tumor recurrence for atypical meningiomas.
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