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Magnetic Resonance Imaging-Based Assessment of Breast Cancer-Related Lymphoedema Tissue Composition.

Borri, M; Gordon, KD; Hughes, JC; Scurr, ED; Koh, D-M; Leach, MO; Mortimer, PS; Schmidt, MA (2017) Magnetic Resonance Imaging-Based Assessment of Breast Cancer-Related Lymphoedema Tissue Composition. Invest Radiol, 52 (9). pp. 554-561. ISSN 1536-0210 https://doi.org/10.1097/RLI.0000000000000386
SGUL Authors: Gordon, Kristiana

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Abstract

OBJECTIVES: The aim of this study was to propose a magnetic resonance imaging acquisition and analysis protocol that uses image segmentation to measure and depict fluid, fat, and muscle volumes in breast cancer-related lymphoedema (BCRL). This study also aims to compare affected and control (unaffected) arms of patients with diagnosed BCRL, providing an analysis of both the volume and the distribution of the different tissue components. MATERIALS AND METHODS: The entire arm was imaged with a fluid-sensitive STIR and a 2-point 3-dimensional T1W gradient-echo-based Dixon sequences, acquired in sagittal orientation and covering the same imaging volume. An automated image postprocessing procedure was developed to simultaneously (1) contour the external volume of the arm and the muscle fascia, allowing separation of the epifacial and subfascial volumes; and to (2) separate the voxels belonging to the muscle, fat, and fluid components. The total, subfascial, epifascial, muscle (subfascial), fluid (epifascial), and fat (epifascial) volumes were measured in 13 patients with unilateral BCRL. Affected versus unaffected volumes were compared using a 2-tailed paired t test; a value of P < 0.05 was considered to be significant. Pearson correlation was used to investigate the linear relationship between fat and fluid excess volumes. The distribution of fluid, fat, and epifascial excess volumes (affected minus unaffected) along the arm was also evaluated using dedicated tissue composition maps. RESULTS: Total arm, epifascial, epifascial fluid, and epifascial fat volumes were significantly different (P < 0.0005), with greater volume in the affected arms. The increase in epifascial volume (globally, 94% of the excess volume) constituted the bulk of the lymphoedematous swelling, with fat comprising the main component. The total fat excess volume summed over all patients was 2.1 times that of fluid. Furthermore, fat and fluid excess volumes were linearly correlated (Pearson r = 0.75), with the fat excess volume being greater than the fluid in 11 subjects. Differences in muscle compartment volume between affected and unaffected arms were not statistically significant, and contributed only 6% to the total excess volume. Considering the distribution of the different tissue excess volumes, fluid accumulated prevalently around the elbow, with substantial involvement of the upper arm in only 3 cases. Fat excess volume was generally greater in the upper arm; however, the relative increase in epifascial volume, which considers the total swelling relative to the original size of the arm, was in 9 cases maximal within the forearm. CONCLUSIONS: Our measurements indicate that excess of fat within the epifascial layer was the main contributor to the swelling, even when a substantial accumulation of fluid was present. The proposed approach could be used to monitor how the internal components of BCRL evolve after presentation, to stratify patients for treatment, and to objectively assess treatment response. This methodology provides quantitative metrics not currently available during the standard clinical assessment of BCRL and shows potential for implementation in clinical practice.

Item Type: Article
Additional Information: Copyright© 2017 The Author(s). Published byWolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: breast cancer-related lymphoedema, tissue composition analysis, image segmentation, magnetic resonance imaging, Nuclear Medicine & Medical Imaging, 1103 Clinical Sciences
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Academic Structure > Molecular and Clinical Sciences Research Institute (MCS) > Cell Sciences (INCCCS)
Journal or Publication Title: Invest Radiol
ISSN: 1536-0210
Language: eng
Dates:
DateEvent
September 2017Published
4 April 2017Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
UNSPECIFIEDCancer Research UKhttp://dx.doi.org/10.13039/501100000289
UNSPECIFIEDEngineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
UNSPECIFIEDMedical Research Councilhttp://dx.doi.org/10.13039/501100000265
PubMed ID: 28538023
Web of Science ID: WOS:000407485100008
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/109141
Publisher's version: https://doi.org/10.1097/RLI.0000000000000386

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