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Abstract #0976

Automated identification of hypoxia-related regional variations in tumour microenvironment using dynamic contrast-enhanced MRI and oxygen-enhanced MRI

Adam K Featherstone1,2, James P B O'Connor2,3,4, Ross A Little1, Yvonne Watson1, Sue Cheung1, Muhammad Babur5, Victoria Tessyman2,5, Roben Gieling2,5, Kaye J Williams2,5, Julian C Matthews1,2, and Geoff J M Parker1,2,6

1Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, United Kingdom, 2CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and Manchester, United Kingdom, 3Division of Molecular and Clinical Cancer Studies, The University of Manchester, Manchester, United Kingdom, 4Department of Radiology, The Christie NHS Foundation Trust, Manchester, United Kingdom, 5Division of Pharmacy & Optometry, The University of Manchester, Manchester, United Kingdom, 6Bioxydyn Ltd., Manchester, United Kingdom

Hypoxia is an important prognostic indicator in most solid tumours. We present here automated, data-driven methods, using principal component analysis (PCA) and Gaussian mixture modelling (GMM), that consistently locate functionally distinct sub-regions in preclinical tumours, some of which are postulated to be relevant to hypoxia. Methods are based on dynamic contrast-enhanced (DCE)-MRI (reflecting perfusion) and oxygen-enhanced (OE)-MRI (reflecting oxygen delivery). We demonstrate the utility and stability of our methods through a combination of evaluation metrics, which may be incorporated in similar studies elsewhere.

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