Fathi Kazerooni, A, Nabil, M, Zeinali Zadeh, M et al. (5 more authors) (2018) Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI. Journal of Magnetic Resonance Imaging, 48 (4). pp. 938-950. ISSN 1053-1807
Abstract
Background: Targeted localized biopsies and treatments for diffuse gliomas rely on accurate identification of tissue subregions, for which current MRI techniques lack specificity.
Purpose: To explore the complementary and competitive roles of a variety of conventional and quantitative MRI methods for distinguishing subregions of brain gliomas.
Study Type: Prospective.
Population: Fifty‐one tissue specimens were collected using image‐guided localized biopsy surgery from 10 patients with newly diagnosed gliomas.
Field Strength/Sequence: Conventional and quantitative MR images consisting of pre‐ and postcontrast T1w, T2w, T2‐FLAIR, T2‐relaxometry, DWI, DTI, IVIM, and DSC‐MRI were acquired preoperatively at 3T.
Assessment: Biopsy specimens were histopathologically attributed to glioma tissue subregion categories of active tumor (AT), infiltrative edema (IE), and normal tissue (NT) subregions. For each tissue sample, a feature vector comprising 15 MRI‐based parameters was derived from preoperative images and assessed by a machine learning algorithm to determine the best multiparametric feature combination for characterizing the tissue subregions.
Statistical Tests: For discrimination of AT, IE, and NT subregions, a one‐way analysis of variance (ANOVA) test and for pairwise tissue subregion differentiation, Tukey honest significant difference, and Games‐Howell tests were applied (P < 0.05). Cross‐validated feature selection and classification methods were implemented for identification of accurate multiparametric MRI parameter combination.
Results: After exclusion of 17 tissue specimens, 34 samples (AT = 6, IE = 20, and NT = 8) were considered for analysis. Highest accuracies and statistically significant differences for discrimination of IE from NT and AT from NT were observed for diffusion‐based parameters (AUCs >90%), and the perfusion‐derived parameter as the most accurate feature in distinguishing IE from AT. A combination of “CBV, MD, T2_ISO, FLAIR” parameters showed high diagnostic performance for identification of the three subregions (AUC ∼90%).
Data Conclusion: Integration of a few quantitative along with conventional MRI parameters may provide a potential multiparametric imaging biomarker for predicting the histopathologically proven glioma tissue subregions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2018, International Society for Magnetic Resonance in Medicine. This is the peer reviewed version of the following article: 'Fathi Kazerooni, A, Nabil, M, Zeinali Zadeh, M et al (2018). Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI. Journal of Magnetic Resonance Imaging,' which has been published in final form at [https://doi.org/10.1002/jmri.25963]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. |
Keywords: | glioma; imaging biomarker; intratumor heterogeneity; multiparametric MRI; machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Aug 2018 15:54 |
Last Modified: | 07 Feb 2019 01:39 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/jmri.25963 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135013 |