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Abstract #4507

A probabilistic approach to automated classification of distinct pathological regions in soft tissue sarcoma using diffusion and T2 relaxation

Shu Xing1,2, Carolyn Freeman3, Sungmi Jung4, and Ives Levesque1,2,5

1Physics, McGill University, Montreal, QC, Canada, 2Medical Physics Unit, McGill University, Montreal, QC, Canada, 3Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada, 4Pathology, McGill University Health Centre, Montreal, QC, Canada, 5Research Institute of the McGill University Health Centre, Montreal, QC, Canada

In this work, we propose a novel probabilistic reference-region-based segmentation method to automatically distinguish various pathological tissue regions within soft tissue sarcoma, including high cellularity, high T2 and necrosis. The classification is based on a calculation of the probability that a tumour voxel belongs to a given class using the quantitative diffusion and T2 information when compared to a reference tissue. The probabilistic approach provides a more realistic classification of the complex tumour microenvironment compared to the previous proposed binary classification method.

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