title: Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity.
abstract: Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.
authors.affiliation: Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Islam
authors.initials: I
authors.lastname: Hassan
authors.affiliation: Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Aikaterini
authors.initials: A
authors.lastname: Kotrotsou
authors.affiliation: Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Ali Shojaee
authors.initials: AS
authors.lastname: Bakhtiari
authors.affiliation: Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Ginu A
authors.initials: GA
authors.lastname: Thomas
authors.affiliation: Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Weinberg
authors.affiliation: Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Ashok J
authors.initials: AJ
authors.lastname: Kumar
authors.affiliation: Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Raymond
authors.initials: R
authors.lastname: Sawaya
authors.affiliation: Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Markus M
authors.initials: MM
authors.lastname: Luedi
authors.affiliation: Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA.
authors.forename: Pascal O
authors.initials: PO
authors.lastname: Zinn
authors.affiliation: Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.|Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Rivka R
authors.initials: RR
authors.lastname: Colen
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D001931:Brain Mapping / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D013060:Speech / Q000502:physiology*; D055815:Young Adult