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Abstract #2750

The Detection of Tumor Sub-Regions Based on T1 and ADC Clustering

Caleb Roberts1,2, Chris Rose1,2, Josephine H. Naish1,2, Yvonne Watson1,2, Sue Cheung1,2, Gio A. Buonaccorsi1,2, Gordon C. Jayson3, John C. Waterton, 2,4, Jean Tessier4, Geoff J. Parker1,2

1Imaging Science and Biomedical Engineering, School of Cancer and Imaging Sciences, The University of Manchester, Manchester, United Kingdom; 2The University of Manchester Biomedical Imaging Institute, The University of Manchester, Manchester, United Kingdom; 3Cancer Research UK Dept Medical Oncology, Christie Hospital and University of Manchester, Manchester, United Kingdom; 4AstraZeneca, Alderley Park, Macclesfield, Cheshire, United Kingdom

Tracer kinetic model-based analyses of dynamic contrast-enhanced (DCE)-MRI data typically report summary statistics that treat tumors as being homogeneous. However, since anti-angiogenic therapies often preferentially affect certain parts of heterogeneous tumors there is interest in the development of methods to provide insight into regional changes. We present a method that uses k-means clustering of T1 and the apparent water diffusion coefficient (ADC) measured in a group of ovarian tumors to sub-divide tumors into distinct regions and demonstrate that differences in tracer kinetic parameters exist between these regions and the overall tumor median statistic.