Preoperative and postoperative prediction of long-term meningioma outcomes.
Efstathios D GennatasAshley WuSteve E BraunsteinOlivier MorinWilliam C ChenStephen T MagillChetna GopinathJavier E Villaneueva-MeyerArie PerryMichael W McDermottTimothy D SolbergGilmer ValdesDavid R RaleighPublished in: PloS one (2018)
Clinical information has been historically underutilized in the prediction of meningioma outcomes. Predictive models trained on preoperative clinical data perform comparably to conventional models trained on meningioma grade and extent of resection. Combination of all available information can help stratify meningioma patients more accurately.
Keyphrases
- patients undergoing
- optic nerve
- end stage renal disease
- ejection fraction
- chronic kidney disease
- newly diagnosed
- resistance training
- health information
- prognostic factors
- peritoneal dialysis
- healthcare
- electronic health record
- type diabetes
- metabolic syndrome
- adipose tissue
- big data
- social media
- body composition
- optical coherence tomography
- high intensity
- glycemic control
- deep learning
- data analysis