Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database
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 develo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-020-0219-5 |
| _version_ | 1797641218465726464 |
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| author | Jeremy T. Moreau Todd C. Hankinson Sylvain Baillet Roy W. R. Dudley |
| author_facet | Jeremy T. Moreau Todd C. Hankinson Sylvain Baillet Roy W. R. Dudley |
| author_sort | Jeremy T. Moreau |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T13:42:26Z |
| format | Article |
| id | doaj.art-8c580cb80a094cad8e4a04fe593e6768 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-11T13:42:26Z |
| publishDate | 2020-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-8c580cb80a094cad8e4a04fe593e67682023-11-02T11:24:15ZengNature Portfolionpj Digital Medicine2398-63522020-01-013111010.1038/s41746-020-0219-5Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results databaseJeremy T. Moreau0Todd C. Hankinson1Sylvain Baillet2Roy W. R. Dudley3McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill UniversityDepartment of Pediatric Neurosurgery, Children’s Hospital Colorado, University of Colorado Anschutz Medical CampusMcConnell Brain Imaging Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill UniversityDepartment of Pediatric Surgery, Division of Neurosurgery, Montreal Children’s HospitalAbstract 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.https://doi.org/10.1038/s41746-020-0219-5 |
| spellingShingle | Jeremy T. Moreau Todd C. Hankinson Sylvain Baillet Roy W. R. Dudley Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database npj Digital Medicine |
| title | Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database |
| title_full | Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database |
| title_fullStr | Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database |
| title_full_unstemmed | Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database |
| title_short | Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database |
| title_sort | individual patient prediction of meningioma malignancy and survival using the surveillance epidemiology and end results database |
| url | https://doi.org/10.1038/s41746-020-0219-5 |
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