Image normalization techniques and their effect on the robustness and predictive power of breast MRI radiomics.
Authors
Affiliations (7)
Affiliations (7)
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Rathausplatz 1, AT-3500 Krems-Stein, Austria. Electronic address: [email protected].
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Rathausplatz 1, AT-3500 Krems-Stein, Austria. Electronic address: [email protected].
- Department of Radiology, University of Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. Electronic address: [email protected].
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Rathausplatz 1, AT-3500 Krems-Stein, Austria. Electronic address: [email protected].
- Cancer Research UK Cambridge Centre, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; Precision Breast Cancer Institute, Department of Oncology, University of Cambridge, Cambridge CB2 0QQ, UK. Electronic address: [email protected].
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Rathausplatz 1, AT-3500 Krems-Stein, Austria; Department of Radiology, University of Cambridge, UK. Electronic address: [email protected].
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Rathausplatz 1, AT-3500 Krems-Stein, Austria; Austrian Center for Medical Innovation and Technology (ACMIT), Viktor Kaplan-Straße 2/1, 2700 Wiener Neustadt, Austria. Electronic address: [email protected].
Abstract
Radiomics analysis has emerged as a promising approach to aid in cancer diagnosis and treatment. However, radiomics research currently lacks standardization, and radiomics features can be highly dependent on acquisition and pre-processing techniques used. In this study, we aim to investigate the effect of various image normalization techniques on robustness of radiomics features extracted from breast cancer patient MRI scans. MRI scans from the publicly available MAMA-MIA dataset and an internal breast MRI test set depicting triple negative breast cancer (TNBC) were used. We compared the effect of commonly used image normalization techniques on radiomics feature robustnessusing Concordance-Correlation-Coefficient (CCC) between multiple combinations of normalization approaches. We also trained machine learning-based prediction models of pathologic complete response (pCR) on radiomics after different normalization techniques were used and compared their areas under the receiver operating characteristic curve (ROC-AUC). For predicting complete pathological response from pre-treatment breast cancer MRI radiomics, the highest overall ROC-AUC was achieved by using a combination of three different normalization techniques indicating their potentially powerful role when working with heterogeneous imaging data. The effect of normalization was more pronounced with smaller training data and normalization may be less important with increasing abundance of training data. Additionally, we observed considerable differences between MRI data sets and their feature robustness towards normalization. Overall, we were able to demonstrate the importance of selecting and standardizing normalization methods for accurate and reliable radiomics analysis in breast MRI scans especially with small training data sets.