Intensity Normalization Techniques and Their Effect on the Robustness and Predictive Power of Breast MRI Radiomics

TL;DR

This study evaluates how different intensity normalization techniques impact the robustness and reliability of radiomics features extracted from breast MRI scans, emphasizing the need for standardization to improve cancer diagnosis accuracy.

Contribution

It systematically compares normalization methods and identifies a combination that enhances feature robustness, advancing standardization in breast MRI radiomics analysis.

Findings

Bias Field correction with histogram normalization improves feature robustness

Normalization methods significantly influence radiomics feature stability

Standardization of pre-processing enhances reproducibility across studies

Abstract

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.