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Abstract #2722

Locating hypoxia-related tumour regions in NSCLC: utility and repeatability of data-driven segmentation of combined OE/DCE-MRI data

Adam K Featherstone1, Ahmed Salem1,2,3, Ross A Little1, Yvonne Watson1, Susan Cheung1, Corrine Faivre-Finn2,3, James PB O'Connor2,4, Julian C Matthews1, and Geoff JM Parker1,5

1Division of Informatics, Imaging & Data Sciences, The University of Manchester, Manchester, United Kingdom, 2Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom, 3Department of Clinical Oncology, Christie NHS Foundation Trust, Manchester, United Kingdom, 4Department of Radiology, Christie NHS Foundation Trust, Mancester, United Kingdom, 5Bioxydyn Ltd., Manchester, United Kingdom

There is a need to develop tumour hypoxia biomarkers for patient stratification and for tracking tumour response to therapy. We apply our preclinically-optimised, data-driven segmentation of combined OE-MRI/DCE-MRI data to a cohort of non small-cell lung cancer (NSCLC) patients, aiming to map tumour hypoxia non-invasively. Tissue classes with different oxygenation and perfusion characteristics are located, and we discuss challenges specific to use in the clinical setting. Further optimisation of the technique is needed to improve its repeatability and its ability to enable the identification of definitively hypoxic regions in these types of data.

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