Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging.

Journal Article (Journal Article)

OBJECTIVE: Invasion of brain parenchyma by meningioma can be a critical factor in surgical planning. The aim of this study was to determine the diagnostic utility of first-order texture parameters derived from both whole tumor and single largest slice of T1-contrast enhanced (T1-CE) images in differentiating meningiomas with and without brain invasion based on histopathology demonstration. METHODS: T1-CE images of a total of 56 cases of grade II meningiomas with brain invasion (BI) and 52 meningiomas (37 grade I and 15 grade II) with no brain invasion (NBI) were analyzed. Filtration-based first-order histogram derived texture parameters were calculated both for whole tumor volume and largest axial cross-section. Random forest models were constructed both for whole tumor volume and largest axial cross-section individually and were assessed using a 5-fold cross validation with 100 repeats. RESULTS: In detection of brain invasion, random forest model based on whole tumor segmentation had an AUC of 0.988 (95 % CI 0.976-1.00) with a cross validated value of 0.74 (95 % CI 0.45-0.96). For differentiation of grade I meningiomas from grade II meningiomas with brain invasion, the AUC was 0.999 (95 % CI 0.995-1.00) and 0.81 (95 % CI 0.61-0.99) in the training and validation cohorts, respectively. Similarly, when using only the single largest slice, the cross-validated AUC to distinguish BI versus NBI and BI versus grade I meningiomas was 0.67 (95 % CI 0.47, 0.92 and 0.78 (95 % CI 0.52, 0.95) respectively. CONCLUSION: Radiomics based feature analysis applied on routine MRI post-contrast images may be helpful to predict presence of brain invasion in meningioma, possibly with better performance when comparing BI versus grade I meningiomas.

Full Text

Duke Authors

Cited Authors

  • Kandemirli, SG; Chopra, S; Priya, S; Ward, C; Locke, T; Soni, N; Srivastava, S; Jones, K; Bathla, G

Published Date

  • November 2020

Published In

Volume / Issue

  • 198 /

Start / End Page

  • 106205 -

PubMed ID

  • 32932028

Electronic International Standard Serial Number (EISSN)

  • 1872-6968

Digital Object Identifier (DOI)

  • 10.1016/j.clineuro.2020.106205

Language

  • eng

Conference Location

  • Netherlands