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A comprehensive framework for predicting response to therapy on the basis of heterogeneity in dceMRI parameter maps is presented. A motion-correction method for dceMRI sequences is extended to incorporate uncertainties in the pharmacokinetic parameter maps using a variational Bayes framework. Simple measures of heterogeneity (with and without uncertainty) in parameter maps for colorectal cancer tumours imaged before therapy are computed, and tested for their ability to distinguish between responders and non-responders to therapy. The statistical analysis demonstrates the importance of using the spatial distribution of parameters, and their uncertainties, when computing heterogeneity measures and using them to predict response on the basis of the pre-therapy scan. The results also demonstrate the benefits of using the ratio of Ktrans with the bolus arrival time as a biomarker.


Journal article


Med Image Comput Comput Assist Interv

Publication Date





316 - 323


Algorithms, Colorectal Neoplasms, Computer Simulation, Contrast Media, Data Interpretation, Statistical, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Meglumine, Models, Biological, Organometallic Compounds, Outcome Assessment (Health Care), Prognosis, Reproducibility of Results, Sensitivity and Specificity, Treatment Outcome