ArticleFrontiers in oncology2021
Preoperative Prediction of Meningioma Consistency
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 45 citations in OpenAlex.
- Correlation of Preoperative Diffusion-weighted Magnetic Resonance Imaging-based Virtual Elastography with Intraoperative Quantitative Tumor Stiffness in Meningiomas.Neurologia medico-chirurgica · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction.Journal of imaging informatics in medicine · 2026Article
- A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.Discover oncology · 2026Review
- A single MRI scan contains sufficient imaging information for accurate prediction of meningioma growth risk.NeuroImage. Clinical · 2026Article
- Consistency and grade prediction of intracranial meningiomas based on fractal geometry analysis.Neurosurgical review · 2025Article
- Advancements in the application of MRI radiomics in meningioma.Radiation oncology (London, England) · 2025Review
- Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study.BMC medical imaging · 2025Article
- MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach.Sensors (Basel, Switzerland) · 2025Article
- Preoperative Assessment of Meningioma Consistency Using a Combination of MR Elastography and DTI.AJNR. American journal of neuroradiology · 2024Article
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- International Tuberculum Sellae Meningioma Study: Surgical Outcomes and Management Trends.Neurosurgery · 2023Article
- A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma.Frontiers in oncology · 2023Article
- Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters.Frontiers in neuroscience · 2023Article
- Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.Cancers · 2022Review
- Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization.Cancers · 2022Review
- 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
- Application of MRI-Based Radiomics in Preoperative Prediction ofFrontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making.
methodsA total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada's consistency grading system. Radiomics features were extracted from multiparametric MRI. Variance selection and LASSO regression were used for feature selection. Then, radiomics models were constructed by five classifiers, and the area under curve (AUC) was used to evaluate the performance of each classifiers. A radiomics nomogram was developed using the best classifier. The performance of this nomogram was assessed by AUC, calibration and discrimination.
resultsA total of 3840 radiomics features were extracted from each patient, of which 3719 radiomics features were stable features. 28 features were selected to construct the radiomics nomogram. Logistic regression classifier had the highest prediction efficacy. Radiomics nomogram was constructed using logistic regression in the train cohort. The nomogram showed a good sensitivity and specificity with AUCs of 0.861 and 0.960 in train and test cohorts, respectively. Moreover, the calibration graph of the nomogram showed a favorable calibration in both train and test cohorts.
conclusionsThe presented radiomics nomogram, as a non-invasive prediction tool, could predict meningiomas consistency preoperatively with favorable accuracy, and facilitated the determination of individualized operation schemes.
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