MRS measured fatty acid composition of periprostatic adipose tissue correlates with pathological measures of prostate cancer aggressiveness.

JOURNAL OF MAGNETIC RESONANCE IMAGING(2015)

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摘要
PurposeTo investigate the association between magnetic resonance (MR) spectroscopically measured fatty acid composition of periprostatic adipose tissue and pathological markers of prostate cancer aggressiveness. Materials and MethodsPeriprostatic adipose (PPA) and subcutaneous adipose (SQA) tissue from prostate cancer patients undergoing radical prostatectomy were examined ex vivo by proton MR spectroscopy at 14.1T (n=31). Fractions of monounsaturated, polyunsaturated, total unsaturated, and saturated fatty acids, as well as T-2 relaxation times were measured from the spectra. Univariate and multivariate analyses based on receiver operating characteristic (ROC) and support vector machines (SVM) were used to evaluate the association between differential measures of fatty acid levels in the PPA and SQA tissues and Gleason score and extracapsular extension (ECE), which are pathological measures of prostate cancer aggressiveness. ResultsBoth pathological markers for aggressive prostate cancer have separable patterns in the MRS features space. The association between ECE and PPA tissue fatty acid composition is linear (area under receiver operating characteristic curve (AROC) and 95% confidence intervals [CIs]: 1.00, [1.00, 1.00]), along the (f(M)/f(S)) measure, and is marked by elevated monounsaturated and reduced saturated fatty acids in the PPA tissue relative to SQA. In contrast, the association between Gleason score and PPA tissue fatty acid composition is nonlinear (classifier AROC and 95% CIs: 0.86, [0.71, 1.00]). ConclusionFatty acid composition is altered in the PPA tissue of patients with aggressive prostate cancer. Ex vivo MR spectroscopy may be a useful tool in studying the altered fatty acid metabolism in prostate cancer. J. Magn. Reson. Imaging 2015;42:651-657.
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关键词
MR spectroscopy,periprostatic adipose tissue,prostate cancer,fatty acid composition,multivariate analysis,support vector machines
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