Abstract

Objectives To evaluate the value of the whole volume apparent diffusion coefficient (ADC) histogram in distinguishing between benign and malignant breast lesions and differentiating different molecular subtypes of breast cancers and to assess the correlation between ADC histogram parameters and Ki-67 expression in breast cancers. Methods The institutional review board approved this retrospective study. Between September 2016 and February 2019, 189 patients with 84 benign lesions and 105 breast cancers underwent magnetic resonance imaging (MRI). Volumetric ADC histograms were created by placing regions of interest (ROIs) on the whole lesion. The relationships between the ADC parameters and Ki-67 were analysed using Spearman's correlation analysis. Results Of the 189 breast lesions included, there were significant differences in patient age (P < 0.001) and lesion size (P = 0.006) between the benign and malignant lesions. The results also demonstrated significant differences in all ADC histogram parameters between benign and malignant lesions (all P < 0.001). The median and mean ADC histogram parameters performed better than the other ADC histogram parameters (AUCs were 0.943 and 0.930, respectively). The receiver operating characteristic (ROC) analysis revealed that the 10th percentile ADC value and entropy could determine the human epidermal growth factor receptor 2 (HER-2) status (both P = 0.001) and estrogen receptor (ER)/progesterone receptor (PR) status (P = 0.020 and P = 0.041, respectively). Among all breast cancer lesions, 35 tumours in the low-proliferation group (Ki − 67 < 14%) and 70 tumours in the high-proliferation group (Ki − 67 ≥ 14) were analysed with ROC curves and correlation analyses. The ROC analysis revealed that entropy and skewness could determine the Ki-67 status (P = 0.007 and P < 0.001, respectively), and there were weak correlations between ADC entropy (r = 0.383) and skewness (r = 0.209) and the Ki-67 index. Conclusion The volumetric ADC histogram could serve as an imaging marker to determine breast lesion characteristics and may be a supplemental method in predicting tumour proliferation in breast cancer.

Highlights

  • Breast cancer is a heterogeneous disease, and the different subtypes can be defined by the immunohistochemical (IHC) approach based on estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) and Ki-67 expression levels [1,2,3]

  • There was a significant difference in patient age (P < 0:001) and lesion size (P = 0:006), but there were no significant differences in lesion position (P = 0:974), lesion type (P = 0:170), and menopausal status (P = 0:499) between the benign and malignant breast lesions

  • We examined whether the apparent diffusion coefficient (ADC) histogram analysis of the whole lesion was reliable and helpful in determining the breast lesion characteristics and whether the ADC histogram parameters were correlated with the Ki-67 index in breast cancer

Read more

Summary

Introduction

Breast cancer is a heterogeneous disease, and the different subtypes can be defined by the immunohistochemical (IHC) approach based on estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) and Ki-67 expression levels [1,2,3]. The procedure for ADC measurements in breast lesions has not been standardized, and size and positioning of the region of interest (ROI) affect both the ADC levels and reproducibility of the measurements [7]. The ADC measurements in the previous study were mostly based on traditional 2D regions of interest (ROIs) manually drawn from a single representative slice of the breast lesion, which might limit these ADC measurements in their ability to reflect whole tumour characteristics [4, 8,9,10,11]. Assessments with whole volume histogram analyses of the ADC might provide more reliable results to reflect the biological characteristics of the heterogeneous breast lesions [3, 8,9,10,11,12]

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call