The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status

Abstract

In breast cancer studies, combining quantitative radiomic with genomic signatures can help identifying and characterizing radiogenomic phenotypes, in function of molecular receptor status. Biomedical imaging processing lacks standards in radiomic feature normalization methods and neglecting feature normalization can highly bias the overall analysis. This study evaluates the effect of several normalization techniques to predict four clinical phenotypes such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and triple negative (TN) status, by quantitative features. The Cancer Imaging Archive (TCIA) radiomic features from 91 T1-weighted Dynamic Contrast Enhancement MRI of invasive breast cancers were investigated in association with breast invasive carcinoma miRNA expression profiling from the Cancer Genome Atlas (TCGA). Three advanced machine learning techniques (Support Vector Machine, Random Forest, and Naïve Bayesian) were investigated to distinguish between molecular prognostic indicators and achieved an area under the ROC curve (AUC) values of 86%, 93%, 91%, and 91% for the prediction of ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative, respectively. In conclusion, radiomic features enable to discriminate major breast cancer molecular subtypes and may yield a potential imaging biomarker for advancing precision medicine.

MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies

“…Data normalization methods are important in both radiomic and genomic field, due to their inner differences in range, scale and statistical distributions. Non-normalized features might present high levels of skewness, resulting in misleading low p values in statistical investigation 33 , 34 . Lately, normalization techniques are of great interest in data preprocessing especially for machine learning algorithms 35 .…”

New Roadmaps for Non-muscle-invasive Bladder Cancer With Unfavorable Prognosis

“…In the last decades, the increasing number of biobanks infrastructures (Coppola et al, 2019 ) has allowed the prompt availability of quality-controlled biological samples to be processed using next-generation sequencing (NGS) technologies (Chakraborty et al, 2018 ). The advent of NGS technologies and radiomics has revolutionized the approach to study disease biology, augmenting precision oncology (Incoronato et al, 2017 ; Zanfardino et al, 2019a , b ; Castaldo et al, 2020 ). In BC research, the integration of NGS technologies with cutting-edge approaches might help to decipher also other tumor-relevant aspects such as immune and stromal component, microbiome, and urobiome modifications.…”

What Is the Role of Imaging in Cancers?

“…Table 1 shows a summary of the topics and the papers included for each topic [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. The majority of papers are focused on prostate cancer (PCa) and radiomics.…”

“…In the last years, a large amount of data has been published, such as in the current issue of Cancers , in order to apply this in diverse settings of disease. Castaldo et al [ 30 ] and Schiano et al [ 32 ] evaluated the role of radiomics in breast cancer. The first group of authors concluded the ability of radiomic features by MRI to discriminate major breast cancer molecular subtypes, thus potentially guiding a personalized treatment [ 30 ].…”

“…Castaldo et al [ 30 ] and Schiano et al [ 32 ] evaluated the role of radiomics in breast cancer. The first group of authors concluded the ability of radiomic features by MRI to discriminate major breast cancer molecular subtypes, thus potentially guiding a personalized treatment [ 30 ]. The second group by Schiano et al [ 32 ] showed that the combination of radiomic features by FDG PET/MRI and molecular data are able to predict the synchronous metastatic disease more accurately than a single information.…”