Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity
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- Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity
- Han, SoHyun; Stoyanova, R; Lee, Hansol; Carlin, Sean D.; Koutcher, Jason A.; Cho, Hyungjoon; Ackerstaff, Ellen
- DCE-MRI; pattern recognition analysis; principalcomponent analysis; automation; intratumoral vascularheterogeneity
- Issue Date
- MAGNETIC RESONANCE IN MEDICINE, v.79, no.3, pp.1736 - 1744
- 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.
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