TY - JOUR
T1 - Metabolomics of breast cancer using high-resolution magic angle spinning magnetic resonance spectroscopy
T2 - Correlations with 18f-fdg positron emission tomography-computed tomography, dynamic contrast-enhanced and diffusion-weighted imaging mri
AU - Yoon, Haesung
AU - Yoon, Dahye
AU - Yun, Mijin
AU - Choi, Ji Soo
AU - Park, Vivian Youngjean
AU - Kim, Eun Kyung
AU - Jeong, Joon
AU - Koo, Ja Seung
AU - Yoon, Jung Hyun
AU - Moon, Hee Jung
AU - Kim, Suhkmann
AU - Kim, Min Jung
N1 - 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.
PY - 2016/7
Y1 - 2016/7
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0159949
DO - 10.1371/journal.pone.0159949
M3 - Article
C2 - 27459480
AN - SCOPUS:84979993763
VL - 11
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 7
M1 - e0159949
ER -