Journal article
Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging
Clinical neurology and neurosurgery, Vol.198, pp.106205-106205
11/2020
DOI: 10.1016/j.clineuro.2020.106205
PMID: 32932028
Abstract
•Prior studies on meningiomas have correlated histologic grading with semantic MRI features like heterogeneity and tumor-brain interface.•Main limitation of semantic analysis is subjective assessment. Radiomics offers a more reproducible method to quantitatively assess lesions.•Studies showed diagnostic utility of MRI texture analysis in meningioma grading. None of these studies evaluated brain invasion status alone.•In this study, we assessed a radiomics based approach on post-contrast T1-WI for characterization of brain invasion status of meningiomas.•Our results indicate that first-order radiomics-derived texture analysis features can detect brain invasion status with good success.
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.
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.
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.
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.
Details
- Title: Subtitle
- Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging
- Creators
- Sedat Giray Kandemirli - University of IowaSaurav Chopra - University of IowaSarv Priya - University of IowaCaitlin Ward - University of IowaThomas Locke - University of IowaNeetu Soni - University of IowaSanvesh Srivastava - University of IowaKarra Jones - University of IowaGirish Bathla - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Clinical neurology and neurosurgery, Vol.198, pp.106205-106205
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.clineuro.2020.106205
- PMID
- 32932028
- ISSN
- 0303-8467
- eISSN
- 1872-6968
- Language
- English
- Date published
- 11/2020
- Academic Unit
- Statistics and Actuarial Science; Radiology; Pathology; Nursing
- Record Identifier
- 9984186554002771
Metrics
8 Record Views