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Abstract #2326

Improvement of Radiomics Prediction by Robustness Preselection 

Renee Cattell1, Shenglan Chen2, Jie Ding2,3, and Chuan Huang2,4,5
1Stony Brook University, Stony Brook, NY, United States, 2Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, 3Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong, 4Radiology, Stony Brook University, Stony Brook, NY, United States, 5Psychiatry, Stony Brook University, Stony Brook, NY, United States

Radiomic analysis has exponentially increased the amount of quantitative data extractable from a single medical image. However, the effect of various image acquisition conditions on the reproducibility or robustness of these features is understudied. Specifically, when generating a predictive model to be used in a multi-institutional setting, it must be robust to voxel size changes. This study aims to develop a task-specific robustness preselection step for incorporation into radiomics pipeline to improve the generalizability of a model applied to a testing set of dissimilar resolution.

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