ArticleMagnetic resonance in medicine2018
Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity.
Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy.Journal of biophotonics · 2020Article
- Pattern recognition analysis of dynamic susceptibility contrast (DSC)-MRI curves automatically segments tissue areas with intact blood-brain barrier in a rat stroke model: A feasibility and comparison study.Journal of magnetic resonance imaging : JMRI · 2020Article
- Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models.Cancer research · 2019Article
- Cortical bone vessel identification and quantification on contrast-enhanced MR images.Quantitative imaging in medicine and surgery · 2019Article
- Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.Cancer · 2018Review
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7 authors.
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Abstract
purposeTo automate dynamic contrast-enhanced MRI (DCE-MRI) data analysis by unsupervised pattern recognition (PR) to enable spatial mapping of intratumoral vascular heterogeneity.
methodsThree 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.
resultsThe 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.
conclusionThe 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.
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