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Last Posted: Nov 10, 2023

• Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses.
  William C Chen et al. Nat Med 2023 11
• Early-onset grade 2-3 diffuse gliomas and schwannomas increase the risk of central nervous system tumors among the patients' relatives.
  Eljas Alanen et al. Neuro-oncology advances 2023 5(1) vdad008
• MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system.
  Haq Amin Ul et al. Journal of ambient intelligence and humanized computing 2022 1-12
• Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning.
  Kang Ho et al. Journal of magnetic resonance imaging : JMRI 2022
• Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.
  Windisch Paul et al. Cancers 2022 14(11)
• Using machine learning to predict health-related quality of life outcomes in patients with low grade glioma, meningioma, and acoustic neuroma.
  Karri Roshan et al. PloS one 2022 17(5) e0267931
• 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.
  Brunasso Lara et al. Life (Basel, Switzerland) 2022 12(4)
• Multi-Classification of Brain Tumors on Magnetic Resonance Images Using an Ensemble of Pre-Trained Convolutional Neural Networks.
  Wu Miao et al. Current medical imaging 2022
• Deep learning-based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study.
  Chen Haolin et al. European radiology 2022
• Molecular profile to guide personalized medicine in adult patients with primary brain tumors: results from the ProfiLER trial.
  Bonneville-Levard Alice et al. Medical oncology (Northwood, London, England) 2021 39(1) 4
• Genomic Biomarkers of Meningioma: A Focused Review.
  Pawloski Jacob A et al. International journal of molecular sciences 2021 22(19)
• Assessing the utility and attitudes toward molecular testing in neuro-oncology: a survey of the Society for Neuro-Oncology members.
  Fortin Ensign Shannon et al. Neuro-oncology practice 2021 8(3) 310-316
• Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy.
  Ziebart Andreas et al. Frontiers in oncology 2021 11668273
• Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis.
  Ugga Lorenzo et al. Neuroradiology 2021
• Associations of Meningioma Molecular Subgroup and Tumor Recurrence.
  Youngblood Mark W et al. Neuro-oncology 2020 Oct
• Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading.
  Wodzinski Marek et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2020 Jul 20201758-1761
• Machine Learning in Meningioma MRI: Past to Present. A Narrative Review.
  Neromyliotis Eleftherios et al. Journal of magnetic resonance imaging : JMRI 2020 Oct
• Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma.
  Zhang Hua et al. Neuroinformatics 2020 Sep
• Subsequent Neoplasm Risk Associated With Rare Variants in DNA Damage Response and Clinical Radiation Sensitivity Syndrome Genes in the Childhood Cancer Survivor Study.
  Morton Lindsay M et al. JCO precision oncology 2020 4
• Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study.
  McCradden Melissa D et al. CMAJ open 8(1) E90-E95

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