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Abstract #2388

Improving the repeatability of radiomic analysis of the prostate through deep normalization of T2w MRI inputs

Stephanie Alley1, Andrey Fedorov2,3, Cynthia Menard4, and Samuel Kadoury1,4
1Polytechnique Montréal, Montréal, QC, Canada, 2Brigham and Women’s Hospital, Boston, MA, United States, 3Harvard Medical School, Boston, MA, United States, 4Centre Hospitalier de l’Université de Montréal, Montréal, QC, Canada

Radiomics analyses are being increasingly employed to investigate tissue heterogeneity present within the prostate gland. We present a method for improving the repeatability of radiomics features extracted from T2-weighted images using a deep normalization technique based on fully convolutional networks (FCNs). We test the repeatability of select radiomics features on a previously published test-retest prostate dataset. We demonstrate that the intraclass correlation coefficient of first-order statistics features extracted from images normalized using the FCN-based pre-processor is consistently higher than for features extracted from non-normalized images.

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