ArticleNPJ digital medicine2020
Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.Acta neurochirurgica · 2024Pooled it
- EANO guideline on the diagnosis and management of meningiomas.Neuro-oncology · 2021Guideline
- Giant Right Sphenoid Wing Meningioma as a Reversible Frontal Network Lesion: A Pseudo-bvFTD Case with Venous-Sparing Skull-Base Resection.Diagnostics (Basel, Switzerland) · 2026Article
- Determinants of clinical outcome and revision surgery in spinal tumors: Analysis of 344 procedures.Brain & spine · 2026Article
- Development and validation of a prognostic nomogram for overall survival in individuals with malignant meningiomas: a population-based study.Scientific reports · 2025Article
- Racial or Ethnic Disparities in Incidence, Treatment, and Prognosis of Adult Intracranial Meningiomas.Journal of racial and ethnic health disparities · 2025Article
- A nomogram to predict cancer-specific mortality in adult patients with malignant meningioma: a competing risk analysis.Discover oncology · 2024Article
- Rethinking the effects of adjuvant beam radiation therapy on overall survival in atypical meningioma patients: age considerations.Frontiers in neurology · 2024Article
- A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit.BMC medical informatics and decision making · 2023Article
- Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models.Journal of neuro-oncology · 2023Article
- Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis.Scientific reports · 2023Article
- Emerging systemic treatment options in meningioma.Journal of neuro-oncology · 2023Review
- Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review.Open life sciences · 2023Review
- Advances and trends in meningioma research over the last decade: A scientometric and visual analysis.Frontiers in oncology · 2023Review
- Consensus core clinical data elements for meningiomas (v2021.1).Neuro-oncology · 2022Article
- De Novo Glioblastoma Masqueraded within a Hemispheric Dural Meningiomatosis: Rare Imaging Findings and Rationale for Two-Staged Resection.Journal of neurological surgery reports · 2022Article
- Trends in the use of radiation for meningioma across the United States.Radiation oncology journal · 2022Article
- Prognostic Factors and Treatment Strategies for Elderly Patients with Malignant Meningioma: A SEER Population-Based Study.Frontiers in oncology · 2022Article
- Development and Validation of Nomograms to Predict Overall Survival Outcomes in Serous Ovarian Cancer Patients with Satisfactory Cytoreductive Surgery and Chemotherapy.International journal of general medicine · 2022Article
- Papillary meningioma of the central nervous system: a SEER database analysis.Neurosurgical review · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Meningiomas are known to have relatively lower aggressiveness and better outcomes than other central nervous system (CNS) tumors. However, there is considerable overlap between clinical and radiological features characterizing benign, atypical, and malignant tumors. In this study, we developed methods and a practical app designed to assist with the diagnosis and prognosis of meningiomas. Statistical learning models were trained and validated on 62,844 patients from the Surveillance, Epidemiology, and End Results database. We used balanced logistic regression-random forest ensemble classifiers and proportional hazards models to learn multivariate patterns of association between malignancy, survival, and a series of basic clinical variables-such as tumor size, location, and surgical procedure. We demonstrate that our models are capable of predicting meaningful individual-specific clinical outcome variables and show good generalizability across 16 SEER registries. A free smartphone and web application is provided for readers to access and test the predictive models (www.meningioma.app). Future model improvements and prospective replication will be necessary to demonstrate true clinical utility. Rather than being used in isolation, we expect that the proposed models will be integrated into larger and more comprehensive models that integrate imaging and molecular biomarkers. Whether for meningiomas or other tumors of the CNS, the power of these methods to make individual-patient predictions could lead to improved diagnosis, patient counseling, and outcomes.
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