Metabolomics of breast cancer using high-resolution magic angle spinning magnetic resonance spectroscopy: Correlations with 18f-fdg positron emission tomography-computed tomography, dynamic contrast-enhanced and diffusion-weighted imaging mri

Haesung Yoon, Dahye Yoon, Mijin Yun, Ji Soo Choi, Vivian Youngjean Park, Eun Kyung Kim, Joon Jeong, Ja Seung Koo, Jung Hyun Yoon, Hee Jung Moon, Suhkmann Kim, Min Jung Kim

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15 Citations (Scopus)

Abstract

Purpose Our goal in this study was to find correlations between breast cancer metabolites and conventional quantitative imaging parameters using high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) and to find breast cancer subgroups that show high correlations between metabolites and imaging parameters. Materials and methods Between August 2010 and December 2013, we included 53 female patients (mean age 49.6 years; age range 32-75 years) with a total of 53 breast lesions assessed by the Breast Imaging Reporting and Data System. They were enrolled under the following criteria: breast lesions larger than 1 cm in diameter which 1) were suspicious for malignancy on mammography or ultrasound (US), 2) were pathologically confirmed to be breast cancer with USguided core-needle biopsy (CNB) 3) underwent 3 Tesla MRI with dynamic contrastenhanced (DCE) and diffusion-weighted imaging (DWI) and positron emission tomographycomputed tomography (PET-CT), and 4) had an attainable immunohistochemistry profile from CNB. We acquired spectral data by HR-MAS MRS with CNB specimens and expressed the data as relative metabolite concentrations. We compared the metabolites with the signal enhancement ratio (SER), maximum standardized FDG uptake value (SUV max), apparent diffusion coefficient (ADC), and histopathologic prognostic factors for correlation. We calculated Spearman correlations and performed a partial least squares-discriminant analysis (PLS-DA) to further classify patient groups into subgroups to find correlation differences between HR-MAS spectroscopic values and conventional imaging parameters. Results In a multivariate analysis, the PLS-DA models built with HR-MAS MRS metabolic profiles showed visible discrimination between high and low SER, SUV, and ADC. In luminal subtype breast cancer, compared to all cases, high SER, ADV, and SUV were more closely clustered by visual assessment. Multiple metabolites were correlated with SER and SUV in all cases. Multiple metabolites showed correlations with SER and SUV in the ER positive, HER2 negative, and Ki-67 negative groups. Conclusion High levels of PC, choline, and glycine acquired from HR-MAS MRS using CNB specimens were noted in the high SER group via DCE MRI and the high SUV group via PET-CT, with significant correlations between choline and SER and between PC and SUV. Further studies should investigate whether HR-MAS MRS using CNB specimens can provide similar or more prognostic information than conventional quantitative imaging parameters.

Original languageEnglish
Article numbere0159949
JournalPloS one
Volume11
Issue number7
DOIs
Publication statusPublished - 2016 Jul

Bibliographical note

Funding Information:
This study was supported by the Basic Science Research Program of the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning, Republic of Korea (grant 2013R1A1A3013165); by a faculty research grant of Yonsei University College of Medicine for 2015(6-2015-0050); and by Research Institute of Radiological Science, Yonsei University College of Medicine. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2016 Yoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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