Year: 2015
Type: article
Abstract: PURPOSE: This study investigated the impact of arms/hands and body position on the measurement of breast density using MRI. METHODS: Noncontrast-enhanced T1-weighted images were acquired from 32 healthy women. Each subject received four MR scans using different experimental settings, including a high resolution hands-up, a low resolution hands-up, a high resolution hands-down, and finally, another high resolution hands-up after repositioning. The breast segmentation was performed using a fully automatic chest template-based method. The breast volume (BV), fibroglandular tissue volume (FV), and percent density (PD) measured from the four MR scan settings were analyzed. RESULTS: A high correlation of BV, FV, and PD between any pair of the four MR scans was noted (r > 0.98 for all). Using the generalized estimating equation method, a statistically significant difference in mean BV among four settings was noted (left breast, score test p = 0.0056; right breast, score test p = 0.0016), adjusted for age and body mass index. Despite differences in BV, there were no statistically significant differences in the mean PDs among the four settings (p > 0.10 for left and right breasts). Using Bland-Altman plots, the smallest mean difference/bias and standard deviations for BV, FV, and PD were noted when comparing hands-up high vs low resolution when the breast positions were exactly the same. CONCLUSIONS: The authors' study showed that BV, FV, and PD measurements from MRI of different positions were highly correlated. BV may vary with positions but the measured PD did not differ significantly between positions. The study suggested that the percent density analyzed from MRI studies acquired using different arms/hands and body positions from multiple centers can be combined for analysis.
Source: Medical Physics
DOI: 10.1118/1.4917083
Authors: Jeon‐Hor Chen, Siwa Chan, Yi‐Ting Tang, Jia Shen Hon, Po‐Chuan Tseng, Angela T. Cheriyan, Nikita Rakesh Shah, Dah‐Cherng Yeh, San‐Kan Lee, Wen‐Pin Chen, Christine E. McLaren, Min‐Ying Su
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Awards: Institute for Clinical and Translational Science, G41642134, Predicting Neoadjuvant Chemo Response/Prognosis Using Imaging Biomarkers, Evaluation of 3D MRI-Based Quantitative Breast Density for Chemoprevention, Volume and Morphology of Fibroglandular Tissue for Breast Cancer Risk Prediction, G848111702, G9000094784, Cancer Center Support Grant
OpenAlex ID: W2081829445
