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Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival

BACKGROUND: We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes. METHODS: Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization g...

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Autores principales: Morin, Olivier, Chen, William C, Nassiri, Farshad, Susko, Matthew, Magill, Stephen T, Vasudevan, Harish N, Wu, Ashley, Vallières, Martin, Gennatas, Efstathios D, Valdes, Gilmer, Pekmezci, Melike, Alcaide-Leon, Paula, Choudhury, Abrar, Interian, Yannet, Mortezavi, Siavash, Turgutlu, Kerem, Bush, Nancy Ann Oberheim, Solberg, Timothy D, Braunstein, Steve E, Sneed, Penny K, Perry, Arie, Zadeh, Gelareh, McDermott, Michael W, Villanueva-Meyer, Javier E, Raleigh, David R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777505/
https://www.ncbi.nlm.nih.gov/pubmed/31608329
http://dx.doi.org/10.1093/noajnl/vdz011
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author Morin, Olivier
Chen, William C
Nassiri, Farshad
Susko, Matthew
Magill, Stephen T
Vasudevan, Harish N
Wu, Ashley
Vallières, Martin
Gennatas, Efstathios D
Valdes, Gilmer
Pekmezci, Melike
Alcaide-Leon, Paula
Choudhury, Abrar
Interian, Yannet
Mortezavi, Siavash
Turgutlu, Kerem
Bush, Nancy Ann Oberheim
Solberg, Timothy D
Braunstein, Steve E
Sneed, Penny K
Perry, Arie
Zadeh, Gelareh
McDermott, Michael W
Villanueva-Meyer, Javier E
Raleigh, David R
author_facet Morin, Olivier
Chen, William C
Nassiri, Farshad
Susko, Matthew
Magill, Stephen T
Vasudevan, Harish N
Wu, Ashley
Vallières, Martin
Gennatas, Efstathios D
Valdes, Gilmer
Pekmezci, Melike
Alcaide-Leon, Paula
Choudhury, Abrar
Interian, Yannet
Mortezavi, Siavash
Turgutlu, Kerem
Bush, Nancy Ann Oberheim
Solberg, Timothy D
Braunstein, Steve E
Sneed, Penny K
Perry, Arie
Zadeh, Gelareh
McDermott, Michael W
Villanueva-Meyer, Javier E
Raleigh, David R
author_sort Morin, Olivier
collection PubMed
description BACKGROUND: We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes. METHODS: Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan–Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset. RESULTS: Multivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01–16.7, P = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1–3.5, P = .02) and worse OS (HR 2.94, 95% CI 1.47–5.56, P = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and FOXM1 expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone. CONCLUSIONS: Preoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma.
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spelling pubmed-67775052019-10-09 Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival Morin, Olivier Chen, William C Nassiri, Farshad Susko, Matthew Magill, Stephen T Vasudevan, Harish N Wu, Ashley Vallières, Martin Gennatas, Efstathios D Valdes, Gilmer Pekmezci, Melike Alcaide-Leon, Paula Choudhury, Abrar Interian, Yannet Mortezavi, Siavash Turgutlu, Kerem Bush, Nancy Ann Oberheim Solberg, Timothy D Braunstein, Steve E Sneed, Penny K Perry, Arie Zadeh, Gelareh McDermott, Michael W Villanueva-Meyer, Javier E Raleigh, David R Neurooncol Adv Clinical Investigations BACKGROUND: We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes. METHODS: Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan–Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset. RESULTS: Multivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01–16.7, P = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1–3.5, P = .02) and worse OS (HR 2.94, 95% CI 1.47–5.56, P = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and FOXM1 expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone. CONCLUSIONS: Preoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma. Oxford University Press 2019-08-28 /pmc/articles/PMC6777505/ /pubmed/31608329 http://dx.doi.org/10.1093/noajnl/vdz011 Text en © The Author(s) 2019. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Clinical Investigations
Morin, Olivier
Chen, William C
Nassiri, Farshad
Susko, Matthew
Magill, Stephen T
Vasudevan, Harish N
Wu, Ashley
Vallières, Martin
Gennatas, Efstathios D
Valdes, Gilmer
Pekmezci, Melike
Alcaide-Leon, Paula
Choudhury, Abrar
Interian, Yannet
Mortezavi, Siavash
Turgutlu, Kerem
Bush, Nancy Ann Oberheim
Solberg, Timothy D
Braunstein, Steve E
Sneed, Penny K
Perry, Arie
Zadeh, Gelareh
McDermott, Michael W
Villanueva-Meyer, Javier E
Raleigh, David R
Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival
title Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival
title_full Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival
title_fullStr Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival
title_full_unstemmed Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival
title_short Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival
title_sort integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival
topic Clinical Investigations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777505/
https://www.ncbi.nlm.nih.gov/pubmed/31608329
http://dx.doi.org/10.1093/noajnl/vdz011
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