ArticleBMC medical imaging2025
Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction.Journal of imaging informatics in medicine · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.bioRxiv : the preprint server for biology · 2026Article
- A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.Discover oncology · 2026Review
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMeningioma consistency critically impacts surgical planning, as soft tumors are easier to resect than hard tumors. Current assessments of tumor consistency using MRI are subjective and lack quantitative accuracy. Integrating deep learning and radiomics could enhance the predictive accuracy of meningioma consistency.
methodsA retrospective study analyzed 204 meningioma patients from two centers: the Second Affiliated Hospital of Guangzhou Medical University and the Southern Theater Command Hospital PLA. Three models-a radiomics model (Rad_Model), a deep learning model (DL_Model), and a combined model (DLR_Model)-were developed. Model performance was evaluated using AUC, accuracy, sensitivity, specificity, and precision.
resultsThe DLR_Model outperformed other models across all cohorts. In the training set, it achieved AUC 0.957, accuracy of 0.908, and precision of 0.965. In the external test cohort, it maintained superior performance with an AUC of 0.854, accuracy of 0.778, and precision of 0.893, surpassing both the Rad_Model (AUC = 0.768) and DL_Model (AUC = 0.720). Combining radiomics and deep learning features improved predictive performance and robustness.
conclusionOur study introduced and evaluated a deep learning radiomics model (DLR-Model) to accurately predict the consistency of meningiomas, which has the potential to improve preoperative assessments and surgical planning.
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