Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity

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 a...

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Main Authors:	Hassan, Islam, Kotrotsou, Aikaterini, Bakhtiari, Ali Shojaee, Thomas, Ginu A., Weinberg, Jeffrey S., Kumar, Ashok J., Sawaya, Raymond, Luedi, Markus M., Zinn, Pascal O., Colen, Rivka R.
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4858648/

id	pubmed-4858648
recordtype	oai_dc
spelling	pubmed-48586482016-05-19 Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity Hassan, Islam Kotrotsou, Aikaterini Bakhtiari, Ali Shojaee Thomas, Ginu A. Weinberg, Jeffrey S. Kumar, Ashok J. Sawaya, Raymond Luedi, Markus M. Zinn, Pascal O. Colen, Rivka R. Article 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. Nature Publishing Group 2016-05-06 /pmc/articles/PMC4858648/ /pubmed/27151623 http://dx.doi.org/10.1038/srep25295 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Hassan, Islam Kotrotsou, Aikaterini Bakhtiari, Ali Shojaee Thomas, Ginu A. Weinberg, Jeffrey S. Kumar, Ashok J. Sawaya, Raymond Luedi, Markus M. Zinn, Pascal O. Colen, Rivka R.
spellingShingle	Hassan, Islam Kotrotsou, Aikaterini Bakhtiari, Ali Shojaee Thomas, Ginu A. Weinberg, Jeffrey S. Kumar, Ashok J. Sawaya, Raymond Luedi, Markus M. Zinn, Pascal O. Colen, Rivka R. Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity
author_facet	Hassan, Islam Kotrotsou, Aikaterini Bakhtiari, Ali Shojaee Thomas, Ginu A. Weinberg, Jeffrey S. Kumar, Ashok J. Sawaya, Raymond Luedi, Markus M. Zinn, Pascal O. Colen, Rivka R.
author_sort	Hassan, Islam
title	Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity
title_short	Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity
title_full	Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity
title_fullStr	Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity
title_full_unstemmed	Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity
title_sort	radiomic texture analysis mapping predicts areas of true functional mri activity
description	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.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4858648/
_version_	1613576222219960320

Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity

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