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Abstract #1719

Comparison of radiomics-based and deep learning techniques for predicting nuclei density in brain cancer patients from MRI

Samuel Bobholz1, Allison Lowman2, Alexander Barrington3, Michael Brehler2, Jennifer Connelly4, Elizabeth Cochran5, Anjishnu Banerjee6, and Peter LaViolette2,3
1Biophysics, Medical College of Wisconsin, Wauwatosa, WI, United States, 2Radiology, Medical College of Wisconsin, Wauwatosa, WI, United States, 3Biomedical Engineering, Medical College of Wisconsin, Wauwatosa, WI, United States, 4Neurology, Medical College of Wisconsin, Wauwatosa, WI, United States, 5Pathology, Medical College of Wisconsin, Wauwatosa, WI, United States, 6Biostatistics, Medical College of Wisconsin, Wauwatosa, WI, United States

This study sought to compare localized predictions of cellular density via radiomics-based and neural network-based modeling, using co-registered autopsy tissue samples from 16 brain cancer patients as ground truth. We found that radiomics models tended to slightly outperform the neural networks, despite evidence of overfitting in all radiomics-based models and an Alexnet-based transfer learning model. These results suggest that radiomics models tend to perform at least as well as neural network when applied to this dataset, but the propensity of these models for overfitting highlights further needs to be addressed with modelling on larger data sets.

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