The Impact of Heterogeneity and Uncertainty on Prediction of Response to Therapy Using Dynamic MRI Data

Manav Bhushan^{1, 2}, Julia A. Schnabel¹, Michael Chappell¹, Fergus Gleeson³, Mark Anderson³, Jamie Franklin³, Sir Michael Brady⁴, and Mark Jenkinson²

¹Institute of Biomedical Engineering (Department of Engineering Science), University of Oxford, UK

²The Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK

³Department of Radiology, Churchill Hospital, Oxford, UK

⁴Department of Oncology, University of Oxford, UK

Abstract. 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 K^trans with the bolus arrival time as a biomarker.

LNCS 8149, p. 316 ff.

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