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...

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Autores principales:	Jeremy T. Moreau, Todd C. Hankinson, Sylvain Baillet, Roy W. R. Dudley
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/8c580cb80a094cad8e4a04fe593e6768
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spelling	oai:doaj.org-article:8c580cb80a094cad8e4a04fe593e67682021-12-02T10:59:53ZIndividual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database10.1038/s41746-020-0219-52398-6352https://doaj.org/article/8c580cb80a094cad8e4a04fe593e67682020-01-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0219-5https://doaj.org/toc/2398-6352Abstract 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.Jeremy T. MoreauTodd C. HankinsonSylvain BailletRoy W. R. DudleyNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 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
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.
format	article
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
title	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_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_sort	individual-patient prediction of meningioma malignancy and survival using the surveillance, epidemiology, and end results database
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/8c580cb80a094cad8e4a04fe593e6768
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Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database

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