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Abstract #3568

Segmenting Tumour Habitats Using Machine Learning and Saturation Transfer Imaging

Wilfred W Lam1, Wendy Oakden1, Elham Karami1,2,3, Margaret M Koletar1, Leedan Murray1, Stanley K Liu1,2,4, Ali Sadeghi-Naini1,2,3,4, and Greg J Stanisz1,2
1Sunnybrook Research Institute, Toronto, ON, Canada, 2University of Toronto, Toronto, ON, Canada, 3York University, Toronto, ON, Canada, 4Sunnybrook Health Sciences Centre, Toronto, ON, Canada

Saturation transfer-weighted images along with T1 and T2 maps at 7 T for 31 tumour xenografts in mice were used to automatically segment 1) tumour, 2) necrosis/apoptosis, 3) edema, and 4) muscle. Independent component analysis and Gaussian mixture modeling were used to segment these regions. Qualitatively excellent agreement was found between MRI and histopathology. An nine-image subset was identified that resulted in a 96% match in voxel labels compared to those found using the entire 24-image dataset. This subset had positive and negative predictive values of 96% and 97%, respectively, for tumour and 88% and 97%, respectively, for necrosis/apoptosis voxels.

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