  Radiomics analysis has emerged as a promising approach for extracting
quantitative features from medical images to aid in cancer diagnosis and
treatment. However, radiomics research currently lacks standardization, and
radiomics features can be highly dependent on the acquisition and
pre-processing techniques used. In this study, we aim to investigate the effect
of various intensity normalization techniques on the robustness of radiomics
features extracted from MRI scans of breast cancer patients. The images used
are from the publicly available I-SPY TRIAL dataset, which contains MRI scans
of stage 2 or 3 breast cancer patients and from the Platinum and PARP inhibitor
for Neoadjuvant treatment of Triple Negative and / or BRCA positive breast
cancer (PARTNER) trial. We compared the effect of commonly used intensity
normalization techniques on the robustness of radiomics features using
Intraclass Correlation Coefficient (ICC) between multiple combinations of
normalization approaches, identified categories that are robust and therefore
could be compared between studies regardless of the pre-processing used. We
were able to show that while systematic differences between MRI scanners can
significantly affect many radiomics features, a combination of Bias Field
correction with piecewise linear histogram normalization can mitigate some of
the effects compared to other normalization methods investigated in this paper.
We were able to demonstrate the importance of carefully selecting and
standardizing normalization methods for accurate and reliable radiomics
analysis in breast MRI scans.
