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A deep learning radiomics model for preoperative grading in meningioma

https://doi.org/10.1016/j.ejrad.2019.04.022
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Extra-axial brain tumors
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Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer
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Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival
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Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?
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Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI
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World Health Organization Grade II Meningioma: A 10-Year Retrospective Study for Recurrence and Prognostic Factor Assessment
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Bagging Predictors
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Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
inferredcites →https://doi.org/10.1055/s-0037-1600633openalex-snapshot
Radiographic Prediction of Meningioma Grade and Genomic Profile
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Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer
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Adjuvant radiotherapy for atypical and malignant meningiomas: a systematic review
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Grading Meningioma
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Sequential algorithms for observation selection
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Medical Image Analysis
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Computational Radiomics System to Decode the Radiographic Phenotype
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Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma
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Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer
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Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging
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A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy
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Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning
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Meningiomas of the Cerebellopontine Angle: Radiological Differences in Tumors with Internal Auditory Canal Involvement and Their Influence on Surgical Outcome
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Radiographic prediction of meningioma grade by semantic and radiomic features
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-3769601/v1openalex-snapshot
A deep learning radiomics model based on CT images for predicting the biological activity grading of hepatic cystic echinococcosis
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4560904/v1openalex-snapshot
Deep learning radiomics analysis of CT imaging for preoperative Lauren classification in gastric cancer
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9584167/v1openalex-snapshot
Development and Validation of a Radiomics Nomogram Using Intratumoral and Peritumoral CT Features to Predict Lauren Classification in Gastric Cancer
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9640465/v1openalex-snapshot
An integrated explainable machine learning model for post-treatment peritumoral edema formation in Gamma Knife radiosurgery of meningioma
inferred← citeshttps://doi.org/10.2196/44119openalex-snapshot
Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets
inferred← citeshttps://doi.org/10.2196/preprints.17772openalex-snapshot
Prediction Model for children's risk of autism spectrum disorder: a method based on deep learning artificial neural network (Preprint)
inferred← citeshttps://doi.org/10.3233/xst-230326openalex-snapshot
CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer
inferred← citeshttps://doi.org/10.3348/kjr.2022.0765openalex-snapshot
Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance
inferred← citeshttps://doi.org/10.3389/fneur.2021.636235openalex-snapshot
Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas
inferred← citeshttps://doi.org/10.3389/fneur.2024.1321923openalex-snapshot
Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms
inferred← citeshttps://doi.org/10.3389/fnut.2026.1705520openalex-snapshot
CT-based radiomics-clinical model for risk assessment of parenteral nutrition-associated hepatic steatosis in chronic intestinal failure and its metabolomic interpretation
inferred← citeshttps://doi.org/10.3389/fonc.2020.567736openalex-snapshot
The Current State of Radiomics for Meningiomas: Promises and Challenges
inferred← citeshttps://doi.org/10.3389/fonc.2020.599888openalex-snapshot
Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery
inferred← citeshttps://doi.org/10.3389/fonc.2021.665891openalex-snapshot
Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion
inferred← citeshttps://doi.org/10.3389/fonc.2021.752672openalex-snapshot
Clinical-Deep Neural Network and Clinical-Radiomics Nomograms for Predicting the Intraoperative Massive Blood Loss of Pelvic and Sacral Tumors
inferred← citeshttps://doi.org/10.3389/fonc.2021.802205openalex-snapshot
A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma
inferred← citeshttps://doi.org/10.3389/fonc.2022.1100350openalex-snapshot
The efficacy of preoperative MRI features in the diagnosis of meningioma WHO grade and brain invasion
inferred← citeshttps://doi.org/10.3389/fonc.2022.811767openalex-snapshot
A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas
inferred← citeshttps://doi.org/10.3389/fonc.2022.870544openalex-snapshot
Preoperative Prediction of Malignant Transformation of Sinonasal Inverted Papilloma Using MR Radiomics
inferred← citeshttps://doi.org/10.3389/fonc.2022.879528openalex-snapshot
Application of MRI-Based Radiomics in Preoperative Prediction of NF2 Alteration in Intracranial Meningiomas
inferred← citeshttps://doi.org/10.3389/fonc.2024.1246730openalex-snapshot
Multi-parameter MRI radiomic features may contribute to predict progression-free survival in patients with WHO grade II meningiomas
inferred← citeshttps://doi.org/10.3389/fonc.2024.1363756openalex-snapshot
A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors
inferred← citeshttps://doi.org/10.3389/fonc.2024.1399270openalex-snapshot
Preoperative MRI-based radiomic nomogram for distinguishing solitary fibrous tumor from angiomatous meningioma: a multicenter study
inferred← citeshttps://doi.org/10.3389/fonc.2025.1517205openalex-snapshot
MRI-based deep transfer learning models for predicting progesterone receptor expression in meningioma
inferred← citeshttps://doi.org/10.3389/fonc.2026.1935311openalex-snapshot
Non-invasive preoperative MRI-based deep transfer learning radiomics model for predicting meningioma brain invasion
inferred← citeshttps://doi.org/10.3389/fphys.2024.1426468openalex-snapshot
A deep learning radiomics model based on CT images for predicting the biological activity of hepatic cystic echinococcosis
inferred← citeshttps://doi.org/10.3390/cancers13195010openalex-snapshot
Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm
inferred← citeshttps://doi.org/10.3390/cancers14030836openalex-snapshot
Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors
inferred← citeshttps://doi.org/10.3390/cancers14112605openalex-snapshot
Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization
inferred← citeshttps://doi.org/10.3390/cancers14112676openalex-snapshot
Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review
inferred← citeshttps://doi.org/10.3390/cancers16111978openalex-snapshot
Surgical Management of High-Grade Meningiomas
inferred← citeshttps://doi.org/10.3390/diagnostics13203266openalex-snapshot
Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics
inferred← citeshttps://doi.org/10.3390/diagnostics14070748openalex-snapshot
Susceptibility-Weighted MRI for Predicting NF-2 Mutations and S100 Protein Expression in Meningiomas
inferred← citeshttps://doi.org/10.3390/jpm11080786openalex-snapshot
Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method
inferred← citeshttps://doi.org/10.3390/jpm12040522openalex-snapshot
Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features
inferred← citeshttps://doi.org/10.3390/life12040586openalex-snapshot
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
inferred← citeshttps://doi.org/10.35711/aimi.v1.i1.19openalex-snapshot
Artificial intelligence in pancreatic disease
inferred← citeshttps://doi.org/10.35713/aic.v7.i1.116460openalex-snapshot
Magnetic resonance imaging-based deep learning model for prediction of the neoadjuvant chemotherapy response and survival prognosis in adolescents with osteosarcoma
inferred← citeshttps://doi.org/10.37349/etat.2023.00151openalex-snapshot
Current role of machine learning and radiogenomics in precision neuro-oncology
inferred← citeshttps://doi.org/10.4251/wjgo.v16.i12.4663openalex-snapshot
Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors
inferred← citeshttps://doi.org/10.5281/zenodo.168752openalex-snapshot
FIGURES 1-2 in Two new species of Anthaxia Eschscholtz, 1829 from Spain (Coleoptera: Buprestidae)
inferred← citeshttps://doi.org/10.6084/m9.figshare.19549641openalex-snapshot
Additional file 1 of Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods
inferred← citeshttps://doi.org/10.6084/m9.figshare.25347061openalex-snapshot
Additional file 1 of Deep learning–based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features
inferred← citeshttps://doi.org/10.6084/m9.figshare.25347250openalex-snapshot
Additional file 2 of Deep learning–based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features
inferred← citeshttps://doi.org/10.64898/2026.08.05.26359812openalex-snapshot
A Computational and Statistical Framework Leveraging AI-Derived CT Phenotypes for Causal Mediation Effects Between Genetic Variants and Disease
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