  The de facto standard of dynamic histogram binning for radiomic feature
extraction leads to an elevated sensitivity to fluctuations in annotated
regions. This may impact the majority of radiomic studies published recently
and contribute to issues regarding poor reproducibility of radiomic-based
machine learning that has led to significant efforts for data harmonization;
however, we believe the issues highlighted here are comparatively neglected,
but often remedied by choosing static binning.
  The field of radiomics has improved through the development of community
standards and open-source libraries such as PyRadiomics. But differences in
image acquisition, systematic differences between observers' annotations, and
preprocessing steps still pose challenges. These can change the distribution of
voxels altering extracted features and can be exacerbated with dynamic binning.
