  Identifying image features that are robust with respect to segmentation
variability and domain shift is a tough challenge in radiomics. So far, this
problem has mainly been tackled in test-retest analyses. In this work we
analyze radiomics feature stability based on probabilistic segmentations. Based
on a public lung cancer dataset, we generate an arbitrary number of plausible
segmentations using a Probabilistic U-Net. From these segmentations, we extract
a high number of plausible feature vectors for each lung tumor and analyze
feature variance with respect to the segmentations. Our results suggest that
there are groups of radiomic features that are more (e.g. statistics features)
and less (e.g. gray-level size zone matrix features) robust against
segmentation variability. Finally, we demonstrate that segmentation variance
impacts the performance of a prognostic lung cancer survival model and propose
a new and potentially more robust radiomics feature selection workflow.
