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

Olivier Morin, William C. Chen, Farshad Nassiri, Matthew Susko, Stephen T. Magill, Harish N. Vasudevan, Ashley Wu, Martin Vallières, Efstathios D. Gennatas, Gilmer Valdes, Melike Pekmezci, Paula Alcaide-Leon, Abrar Choudhury, Yannet Interian, Siavash Mortezavi, Kerem Turgutlu, Nancy Ann Oberheim Bush, Timothy D. Solberg, Steve E. Braunstein, Penny K. SneedArie Perry, Gelareh Zadeh, Michael W. McDermott, Javier E. Villanueva-Meyer, David R. Raleigh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Scopus citations


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.

Original languageEnglish (US)
Article numbervdz011
JournalNeuro-Oncology Advances
Issue number1
StatePublished - May 1 2019


  • diffusion weighted imaging
  • magnetic resonance imaging
  • meningioma
  • quantitative imaging
  • radiomics

ASJC Scopus subject areas

  • Clinical Neurology
  • Oncology
  • Surgery


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