ReviewFrontiers in oncology2020
The Current State of Radiomics for Meningiomas: Promises and Challenges.
Review in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 46 citations in OpenAlex.
- Review
- CT-based intrathrombus and perithrombus radiomics for predicting complete recanalization after endovascular thrombectomy in acute ischemic stroke.Frontiers in neurology · 2026Article
- A systematic review of multiparametric magnetic resonance imaging radiomics in the prediction of brain invasion in meningioma.Translational cancer research · 2025Review
- Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- CT-Based Intrathrombus and Perithrombus Radiomics for Prediction of Prognosis after Endovascular Thrombectomy: A Retrospective Study across 2 Centers.AJNR. American journal of neuroradiology · 2025Article
- Research on imaging biomarkers for chronic subdural hematoma recurrence.Medical & biological engineering & computing · 2025Article
- MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion.Journal of imaging informatics in medicine · 2024Article
- Predicting meningioma grades and pathologic marker expression via deep learning.European radiology · 2024Article
- Review
- The development of a combined clinico-radiomics model for predicting post-operative recurrence in atypical meningiomas: a multicenter study.Journal of neuro-oncology · 2024Article
- Deep-learning model for diagnostic clue: detecting the dural tail sign for meningiomas on contrast-enhanced T1 weighted images.Quantitative imaging in medicine and surgery · 2023Article
- Changes in radiomic and radiologic features in meningiomas after radiation therapy.BMC medical imaging · 2023Article
- MRI-based machine learning models predict the malignant biological behavior of meningioma.BMC medical imaging · 2023Article
- Somatostatin receptor subtype expression and radiomics from DWI-MRI represent SUV of [68Ga]Ga-DOTATOC PET in patients with meningioma.Journal of neuro-oncology · 2023Article
- One Step Forward-The Current Role of Artificial Intelligence in Glioblastoma Imaging.Life (Basel, Switzerland) · 2023Review
- Dosimetric variability across a library of computational tumor phantoms.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Surgeon's Eyes on the Relevant Surgical Target.Acta neurochirurgica. Supplement · 2023Article
- Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model.Frontiers in oncology · 2023Article
- Preoperative surgical risk assessment of meningiomas: a narrative review based on MRI radiomics.Neurosurgical review · 2022Review
- Medical student's artificial intelligence education and research experiences.Korean journal of medical education · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Meningiomas are the most common primary tumors of the central nervous system. Given the fact that the majority of meningiomas are benign, the preoperative risk stratification and treatment strategy decision-making highly rely on the conventional subjective radiologic evaluation. However, this traditional diagnostic and treatment modality may not be effective in patients with aggressive-growing tumors or symptomatic patients with potential risk of recurrence after surgical resection or radiotherapy, as this passive "wait and see" strategy could miss the optimal opportunity of intervention. Radiomics, a new rising discipline, translates high-dimensional image information into abundant mathematical data by multiple computational algorithms. It provides an objective and quantitative approach to interpret the imaging data, rather than the subjective and qualitative interpretation from relatively limited human visual observation. In fact, the enormous amount of information generated by radiomics analyses provides radiological to histopathological tumor information, which are visually imperceptible, and offers technological basis to its applications amid diagnosis, treatment, and prognosis. Here, we review the latest advancements of radiomics and its applications in the prediction of the pathological grade, pathological subtype, recurrence possibility, and differential diagnosis of meningiomas, and the potential and challenges in general clinical applications. In this review, we highlight the generalization of shared radiomic features among different studies and compare different performances of popular algorithms. At last, we discuss several possible aspects of challenges and future directions in the development of radiomic applications in meningiomas.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.