Abstract #3568

Segmenting Tumour Habitats Using Machine Learning and Saturation Transfer Imaging

Wilfred W Lam¹, Wendy Oakden¹, Elham Karami^1,2,3, Margaret M Koletar¹, Leedan Murray¹, Stanley K Liu^1,2,4, Ali Sadeghi-Naini^1,2,3,4, and Greg J Stanisz^1,2

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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