Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity Journal Article

   

Authors:	Han, S.; Stoyanova, R.; Lee, H.; Carlin, S. D.; Koutcher, J. A.; Cho, H.; Ackerstaff, E.
Article Title:	Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity
Abstract:	Purpose: To automate dynamic contrast-enhanced MRI (DCE-MRI) data analysis by unsupervised pattern recognition (PR) to enable spatial mapping of intratumoral vascular heterogeneity. Methods: Three steps were automated. First, the arrival time of the contrast agent at the tumor was determined, including a calculation of the precontrast signal. Second, four criteria-based algorithms for the slice-specific selection of number of patterns (NP) were validated using 109 tumor slices from subcutaneous flank tumors of five different tumor models. The criteria were: half area under the curve, standard deviation thresholding, percent signal enhancement, and signal-to-noise ratio (SNR). The performance of these criteria was assessed by comparing the calculated NP with the visually determined NP. Third, spatial assignment of single patterns and/or pattern mixtures was obtained by way of constrained nonnegative matrix factorization. Results: The determination of the contrast agent arrival time at the tumor slice was successfully automated. For the determination of NP, the SNR-based approach outperformed other selection criteria by agreeing >97% with visual assessment. The spatial localization of single patterns and pattern mixtures, the latter inferring tumor vascular heterogeneity at subpixel spatial resolution, was established successfully by automated assignment from DCE-MRI signal-versus-time curves. Conclusion: The PR-based DCE-MRI analysis was successfully automated to spatially map intratumoral vascular heterogeneity. Magn Reson Med 79:1736–1744, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine
Keywords:	automation; dce-mri; principal component analysis; pattern recognition analysis; intratumoral vascular heterogeneity
Journal Title:	Magnetic Resonance in Medicine
Volume:	79
Issue:	3
ISSN:	0740-3194
Publisher:	John Wiley & Sons
Date Published:	2018-03-01
Start Page:	1736
End Page:	1744
Language:	English
DOI:	10.1002/mrm.26822
PROVIDER:	scopus
PMCID:	PMC5775918
PUBMED:	28727185
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040764842&doi=10.1002%2fmrm.26822&partnerID=40&md5=5189446385f812cad80e4351eead3d58
Notes:	Article -- Export Date: 1 February 2018 -- Source: Scopus

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