Investigation of analysis methods for hyperpolarized 13C‐pyruvate metabolic MRI in prostate cancer patients

Peder E. Z. Larson(University of California, San Francisco), Hsin‐Yu Chen(University of California, San Francisco), Jeremy W. Gordon(University of California, San Francisco), Natalie Korn(University of California, San Francisco), John Maidens(University of California, Berkeley), Murat Arcak(University of California, Berkeley), Shuyu Tang(University of California, San Francisco), Mark Van Criekinge(University of California, San Francisco), Lucas Carvajal(University of California, San Francisco), Daniele Mammoli(University of California, San Francisco), Robert Bok(University of California, San Francisco), Rahul Aggarwal(University of California, San Francisco), Marcus Ferrone(University of California, San Francisco), James B. Slater(University of California, San Francisco), Sarah J. Nelson(University of California, San Francisco), John Kurhanewicz(University of California, San Francisco), Daniel B. Vigneron(University of California, San Francisco)
NMR in Biomedicine
September 19, 2018
Cited by 109Open Access
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Abstract

MRI using hyperpolarized (HP) carbon‐13 pyruvate is being investigated in clinical trials to provide non‐invasive measurements of metabolism for cancer and cardiac imaging. In this project, we applied HP [1‐ 13 C]pyruvate dynamic MRI in prostate cancer to measure the conversion from pyruvate to lactate, which is expected to increase in aggressive cancers. The goal of this work was to develop and test analysis methods for improved quantification of this metabolic conversion. In this work, we compared specialized kinetic modeling methods to estimate the pyruvate‐to‐lactate conversion rate, k P L , as well as the lactate‐to‐pyruvate area‐under‐curve (AUC) ratio. The kinetic modeling included an “inputless” method requiring no assumptions regarding the input function, as well as a method incorporating bolus characteristics in the fitting. These were first evaluated with simulated data designed to match human prostate data, where we examined the expected sensitivity of metabolism quantification to variations in k P L , signal‐to‐noise ratio (SNR), bolus characteristics, relaxation rates, and B 1 variability. They were then applied to 17 prostate cancer patient datasets. The simulations indicated that the inputless method with fixed relaxation rates provided high expected accuracy with no sensitivity to bolus characteristics. The AUC ratio showed an undesired strong sensitivity to bolus variations. Fitting the input function as well did not improve accuracy over the inputless method. In vivo results showed qualitatively accurate k P L maps with inputless fitting. The AUC ratio was sensitive to bolus delivery variations. Fitting with the input function showed high variability in parameter maps. Overall, we found the inputless k P L fitting method to be a simple, robust approach for quantification of metabolic conversion following HP [1‐ 13 C]pyruvate injection in human prostate cancer studies. This study also provided initial ranges of HP [1‐ 13 C]pyruvate parameters (SNR, k P L , bolus characteristics) in the human prostate.


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