Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation

Abstract

Purpose To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (i.e., luminal A/B or basal) of breast cancer. Materials and Methods 84 patients from one institution and 126 patients from the cancer genome atlas (TCGA) were used for discovery and external validation, respectively. 35 quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control false discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort. Results On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus non-luminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P<0.05, FDR<0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively. Conclusion DCE MR imaging characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer.

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors

“…While we have tried to build a comprehensive set of features describing breast cancer MRI, this set can always be extended and more features can be included as the breast MRI radiomics is constantly expanding. [60][61][62] To be comprehensive, we included features of the breast as whole, FGT, and tumors, and considered features that quantify volume, shape, enhancement, and heterogeneity. We included both, features that we proposed in our laboratory and features proposed by other groups.…”

Artificial intelligence in the interpretation of breast cancer on MRI

“…Prior studies have reported significant correlations between MRI enhancement kinetics and molecular breast cancer subtypes . In a multiinstitutional National Cancer Institute research study, the relationships between computer‐extracted radiomic MRI features and various clinical, molecular, and genomic markers were investigated . Statistically significant associations were seen between the enhancement texture radiomic features and molecular subtypes (such as luminal A, luminal B, HER2‐enriched, basal‐like).…”

“…Statistically significant associations were seen between the enhancement texture radiomic features and molecular subtypes (such as luminal A, luminal B, HER2‐enriched, basal‐like). Wu et al studied the relationship between tumor and background parenchymal enhancement with breast cancer molecular subtypes, with area under the curve (AUC) values between 0.66 and 0.79 obtained in distinguishing different molecular subtypes of breast cancer . Grimm et al included 278 breast cancer patients and found significant correlations between molecular subtypes and DCE‐MRI Breast Imaging‐Reporting and Data System (BIRADS) features .…”

Deep learning for identifying radiogenomic associations in breast cancer

“…Recently, the field of radiogenomics or imaging genomics has emerged, which aims at finding correlations between the imaging characteristics of cancer and its genomic composition. A specific area that has garnered significant attention is the prediction of genomics in breast cancer using MRI [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] . Previous work on this topic utilized either imaging features manually extracted by radiologists, which is a very time consuming and subjective process, or features automatically extracted by computer algorithms.…”