Preoperative and postoperative prediction of long-term meningioma outcomes
| Autor: | Ashley Wu, William C. Chen, Efstathios D. Gennatas, Arie Perry, Stephen T. Magill, Steve Braunstein, Michael W. McDermott, Timothy D. Solberg, Gilmer Valdes, Chetna Gopinath, Javier E Villaneueva-Meyer, Olivier Morin, David R. Raleigh |
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| Přispěvatelé: | Pourhomayoun, Mohammad |
| Rok vydání: | 2018 |
| Předmět: |
Decision Analysis
Time Factors Patient demographics Radiography Cancer Treatment lcsh:Medicine 030218 nuclear medicine & medical imaging Machine Learning Mathematical and Statistical Techniques 0302 clinical medicine Medicine and Health Sciences 80 and over Cluster Analysis Single institution lcsh:Science Neurological Tumors Cancer Aged 80 and over Multidisciplinary Applied Mathematics Simulation and Modeling Middle Aged 3. Good health Treatment Outcome Oncology Neurology Physical Sciences Engineering and Technology Radiology Patient Safety Meningioma Management Engineering Statistics (Mathematics) Algorithms Research Article Clinical Oncology Adult Computer and Information Sciences medicine.medical_specialty Adolescent General Science & Technology Radiation Therapy Surgical and Invasive Medical Procedures Research and Analysis Methods Extent of resection Preoperative care Machine Learning Algorithms 03 medical and health sciences Young Adult Rare Diseases Artificial Intelligence Preoperative Care medicine Humans Statistical Methods Aged Postoperative Care Surgical Resection business.industry lcsh:R Decision Trees Cancers and Neoplasms Local failure Nomogram medicine.disease Brain Disorders Brain Cancer Nomograms lcsh:Q Clinical Medicine business Mathematics 030217 neurology & neurosurgery Forecasting |
| Zdroj: | PloS one, vol 13, iss 9 PLoS ONE, Vol 13, Iss 9, p e0204161 (2018) PLoS ONE |
| Popis: | Author(s): Gennatas, Efstathios D; Wu, Ashley; Braunstein, Steve E; Morin, Olivier; Chen, William C; Magill, Stephen T; Gopinath, Chetna; Villaneueva-Meyer, Javier E; Perry, Arie; McDermott, Michael W; Solberg, Timothy D; Valdes, Gilmer; Raleigh, David R | Abstract: BackgroundMeningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes.Methods and findingsWe developed a comprehensive database containing information from 235 patients who underwent surgery for 257 meningiomas at a single institution from 1990 to 2015. The median follow-up was 4.3 years, and resection specimens were re-evaluated according to current diagnostic criteria, revealing 128 WHO grade I, 104 grade II and 25 grade III meningiomas. A series of ML algorithms were trained and tuned by nested resampling to create models based on preoperative features, conventional postoperative features, or both. We compared different algorithms' accuracy as well as the unique insights they offered into the data. Machine learning models restricted to preoperative information, such as patient demographics and radiographic features, had similar accuracy for predicting local failure (AUC = 0.74) or overall survival (AUC = 0.68) as models based on meningioma grade and extent of resection (AUC = 0.73 and AUC = 0.72, respectively). Integrated models incorporating all available demographic, clinical, radiographic and pathologic data provided the most accurate estimates (AUC = 0.78 and AUC = 0.74, respectively). From these models, we developed decision trees and nomograms to estimate the risks of local failure or overall survival for meningioma patients.ConclusionsClinical 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. |
| Databáze: | OpenAIRE |
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