12 research outputs found
Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI
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: Fiftyâone tissue specimens were collected using imageâguided localized biopsy surgery from 10 patients with newly diagnosed gliomas.
Field Strength/Sequence: Conventional and quantitative MR images consisting of preâ and postcontrast T1w, T2w, T2âFLAIR, T2ârelaxometry, DWI, DTI, IVIM, and DSCâMRI 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 MRIâbased 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 oneâway analysis of variance (ANOVA) test and for pairwise tissue subregion differentiation, Tukey honest significant difference, and GamesâHowell tests were applied (Pâ<â0.05). Crossâvalidated feature selection and classification methods were implemented for identification of accurate multiparametric MRI parameter combination.
Results: After exclusion of 17 tissue specimens, 34 samples (ATâ=â6, IEâ=â20, and NTâ=â8) were considered for analysis. Highest accuracies and statistically significant differences for discrimination of IE from NT and AT from NT were observed for diffusionâbased parameters (AUCs >90%), and the perfusionâderived parameter as the most accurate feature in distinguishing IE from AT. A combination of âCBV, MD, T2_ISO, FLAIRâ parameters showed high diagnostic performance for identification of the three subregions (AUC âŒ90%).
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
GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma
Purpose: There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions. However, the use of advanced MRI in clinical practice rem