Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps
Open accessAbstract
Dynamic contrast‐enhanced MRI is becoming a standard tool for imaging‐based trials of anti‐vascular/angiogenic agents in cancer. So far, however, biomarkers derived from DCE‐MRI parameter maps have largely neglected the fact that the maps have spatial structure and instead focussed on distributional summary statistics. Such statistics—e.g., biomarkers based on median values—neglect the spatial arrangement of parameters, which may carry important diagnostic and prognostic information. This article describes two types of heterogeneity biomarker that are sensitive to both parameter values and their spatial arrangement. Methods based on Rényi fractal dimensions and geometrical properties are developed, both of which attempt to describe the complexity of DCE‐MRI parameter maps. Experiments using simulated data show that the proposed biomarkers are sensitive to changes that distribution‐based summary statistics cannot detect and demonstrate that heterogeneity biomarkers could be applied in the drug trial setting. An experiment using 23 DCE‐MRI parameter maps of gliomas—a class of tumour that is graded on the basis of heterogeneity—shows that the proposed heterogeneity biomarkers are able to differentiate between low‐ and high‐grade tumours. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.
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Rose et al showed that complex measures of texture heterogeneity in transfer coefficient maps from dynamic contrast-enhanced MR images could distinguish high- and low-grade gliomas with much higher statistical power (P < .00005) than median transfer coefficient maps alone (P = .005).
“They convincingly showed that complex measures of texture heterogeneity in transfer coefficient maps from dynamic contrast-enhanced MR images could be used to distinguish high- and low-grade gliomas with much higher statistical power (P < .00005) than could median transfer coefficient maps alone (P = .005).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [44]
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References 20 · Cited by 125
Research works printed but not linked: 3
- O'Connor EL, Fieller N, Holmes A, Waterton J. How to analyse dynamic MRI in oncology; advanced histogram analysis gives better statisti- cal power & insight than simple averaging. In Proc 13th International Society for Magnetic Resonance Medicine 2005; 2101.
- Dzik-Jurasz A, Walker-Samuel S, Leach MO, Brown G, Padhani A, George M, Collins DJ. Fractal parameters derived from analysis of DCE- MRI data correlates with response to therapy in rectal carcinoma. In: Proceedings of the 12th International Society for Magnetic Resonance in Medicine, Kyoto, Japan, 2004: p 2503.
- Haase A. Applications to T1, T2, and chemical-shift imaging. Magn Reson Med 1990;13:77-89.
- Authors
- Rose, C. J., Mills, S. J., O'Connor, J. P. B., Buonaccorsi, G. A., Roberts, C., Watson, Y., Cheung, S., Zhao, S., Whitcher, B., Jackson, A., & Parker, G. J. M.
- Affiliations
- University of Manchester (GB)
- GlaxoSmithKline (United Kingdom) (GB)
- Corresponding author
- Rose, Chris J.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2009-05-22
- Volume
- 62
- Issue
- 2
- Pages
- 488-499
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2032079626
- Cited by
- 125
- References
- 20
- Citations by year
- 2026: 3 · 2023: 5 · 2022: 4 · 2021: 5 · 2020: 6 · 2019: 7 · 2018: 6 · 2017: 9 · 2016: 14 · 2015: 8 · 2014: 12 · 2013: 13 · 2012: 13
- FWCI
- 8.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Funders
- GlaxoSmithKline
- Cancer Research UK (C19221/A6086, C237/A6295, C21247/A7473)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- dynamic contrast-enhanced MRI, spatial heterogeneity, geometrical properties, glioma grading
- MeSH terms
- Algorithms, Brain, Brain Neoplasms, Contrast Media, Glioma, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Fractals, Phantoms, Imaging, Gadolinium DTPA
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