Abstract #4811

Target-class-agnostic feature rejection for radiomics analyses based on variations of tumor segmentation mask

Balthasar Schachtner¹, Michael Ingrisch¹, Gresser Eva¹, Moritz Schneider¹, Andrea Schreier¹, Olga Solyanik¹, Guiseppe Magistro², and Dominik Nörenberg¹

¹Department of Radiology, Munich University Hospitals, LMU, Munich, Germany, ²Department of Urology, Munich University Hospitals, LMU, Munich, Germany

Feature selection is a key aspect to radiomics analyses. An approach to remove features which are not stable with respect to small variations of the segmented mask is presented. The rejection works target-class agnostic and can be used in combination with target-class-based selections. An increase of about 5 percentage points can be seen when using the proposed approach in a simple machine learning setup on prostate MRI of prostate cancer patients.

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