Hui Wang1, Yunting Hu1, Hui Li1, Yuanliang Xie1, Xiang Wang1, Weijia Wan2. 1. Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. 2. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Abstract
BACKGROUND: Currently, breast cancer is divided into Luminal A, Luminal B, HER-2 overexpression (HER-2) and basal cell at genetic level. However, the differential diagnosis of estrogen receptor (ER)-positive breast cancer subtypes is rare. Therefore, we aimed to investigate the feasibility of identifying the ER-positive breast cancer subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis. METHODS: A retrospective analysis was performed for clinical data of 51 patients with ER-positive breast invasive ductal carcinoma confirmed by surgery and pathology from January 20 to October 2018. FireVoxel texture analysis software was used to delineate the tumor boundary layer by layer. The differences in the above characteristics between Luminal A and Luminal B breast cancer were compared, and the diagnostic efficacy of statistically significant texture parameters for ER-positive breast cancer subtypes was analyzed. RESULTS: There were no significant differences in mean, standard deviation (SD), skewness and tumor size between Luminal A and Luminal B groups (P>0.05). The kurtosis, inhomogeneity and entropy could effectively distinguish between the two groups with statistically significant difference (P=0.001, P=0.000, and P=0.000). The area under the receiver operating characteristic (ROC) curve (AUC) of kurtosis, inhomogeneity and entropy diagnosed with malignant mass were 0.832, 0.859 and 0.891, respectively (P<0.01). In addition, the entropy was the best among the three indicators. When the entropy was ≤4.22, the sensitivity of the diagnosis Luminal B was 90.62% and the specificity was 78.95%. CONCLUSIONS: The texture analysis features based on DCE-MRI can help to identify ER-positive breast cancer subtypes. Entropy can be the best single texture indicator. 2020 Gland Surgery. All rights reserved.
BACKGROUND: Currently, breast cancer is divided into Luminal A, Luminal B, HER-2 overexpression (HER-2) and basal cell at genetic level. However, the differential diagnosis of estrogen receptor (ER)-positive breast cancer subtypes is rare. Therefore, we aimed to investigate the feasibility of identifying the ER-positive breast cancer subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis. METHODS: A retrospective analysis was performed for clinical data of 51 patients with ER-positive breast invasive ductal carcinoma confirmed by surgery and pathology from January 20 to October 2018. FireVoxel texture analysis software was used to delineate the tumor boundary layer by layer. The differences in the above characteristics between Luminal A and Luminal B breast cancer were compared, and the diagnostic efficacy of statistically significant texture parameters for ER-positive breast cancer subtypes was analyzed. RESULTS: There were no significant differences in mean, standard deviation (SD), skewness and tumor size between Luminal A and Luminal B groups (P>0.05). The kurtosis, inhomogeneity and entropy could effectively distinguish between the two groups with statistically significant difference (P=0.001, P=0.000, and P=0.000). The area under the receiver operating characteristic (ROC) curve (AUC) of kurtosis, inhomogeneity and entropy diagnosed with malignant mass were 0.832, 0.859 and 0.891, respectively (P<0.01). In addition, the entropy was the best among the three indicators. When the entropy was ≤4.22, the sensitivity of the diagnosis Luminal B was 90.62% and the specificity was 78.95%. CONCLUSIONS: The texture analysis features based on DCE-MRI can help to identify ER-positive breast cancer subtypes. Entropy can be the best single texture indicator. 2020 Gland Surgery. All rights reserved.
Entities:
Keywords:
Breast cancer; dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI); estrogen receptor (ER); texture analysis
Authors: Derek F Amanatullah; John S Tamaresis; Pauline Chu; Michael H Bachmann; Nhat M Hoang; Deborah Collyar; Aaron T Mayer; Robert B West; William J Maloney; Christopher H Contag; Bonnie L King Journal: Breast Cancer Res Date: 2017-11-15 Impact factor: 6.466