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.
Highlights
Breast cancer is the most frequently diagnosed cancer among women, and it is the second leading cause of death in women [1]
The aim of this study is to evaluate the impact of several normalization methods to study the relationship between radiomic features and breast tumor molecular receptor estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and triple negative (TN)
Our results demonstrate that there are statistically significant associations between radiomic tumor features and breast cancer molecular receptor status
Summary
Breast cancer is the most frequently diagnosed cancer among women, and it is the second leading cause of death in women [1]. Based on the molecular receptor status, breast cancer can be classified into different subtypes with different response to therapy and prognosis. HER2-positive (HER2+) breast cancers are more aggressive and show a poorer prognosis than HER2-negative (HER2–) cancers. Triple negative (TN) tumor (negative for all three receptors) shows a high relapsing rate, and accounts for a large portion of breast cancer deaths. It becomes necessary to identify molecular receptor status and subsequently subtypes to select the appropriate therapy and predict the therapeutic response [8,9]
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