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Abstract #5617

Quantitative analysis of hyperpolarized [1-13C]pyruvate metabolic kinetics using Bayesian Inference algorithms

Nikolaos Dikaios1, Charlie J. Daniels2, Ferdia A. Gallagher2, James O’Callaghan3, David Atkinson3, and Shonit Punwani3

1Electrical Engineering, University of Surrey, Guildford, United Kingdom, 2Radiology, University of Cambridge, 3Centre for Medical Imaging, University College London

Metabolic processes monitored by MRS precede the micro-structural changes visualised by MRI. It is well-recognised that cancer cells reprogram their metabolic pathways to meet their energy demands for abnormal proliferation. Pyruvate is produced through the breakdown of glucose in glycolysis, and is essential for providing cellular energy. Histological studies have shown increased exchange of pyruvate to lactate in prostate cancer, demonstrating a positive correlation with more aggressive disease. In regions of up-regulation of glucose metabolism, [1-13C]pyruvate is more readily converted to [1-13C]lactate, providing added value for diagnostic imaging. This work aims to robustly quantify the exchange rates between pyruvate and lactate using Bayesian Inference algorithms.

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