A major challenge in cancer biology is to monitor and understand cancer metabolism in vivo with the goal of improved diagnosis and perhaps therapy. Because of the complexity of biochemical pathways, tracer methods are required for detecting specific enzyme-catalyzed reactions. Stable isotopes such as (13)C or (15)N with detection by nuclear magnetic resonance provide the necessary information about tissue biochemistry, but the crucial metabolites are present in low concentration and therefore are beyond the detection threshold of traditional magnetic resonance methods. A solution is to improve sensitivity by a factor of 10,000 or more by temporarily redistributing the populations of nuclear spins in a magnetic field, a process termed hyperpolarization. Although this effect is short-lived, hyperpolarized molecules can be generated in an aqueous solution and infused in vivo where metabolism generates products that can be imaged. This discovery lifts the primary constraint on magnetic resonance imaging for monitoring metabolism-poor sensitivity-while preserving the advantage of biochemical information. The purpose of this report was to briefly summarize the known abnormalities in cancer metabolism, the value and limitations of current imaging methods for metabolism, and the principles of hyperpolarization. Recent preclinical applications are described. Hyperpolarization technology is still in its infancy, and current polarizer equipment and methods are suboptimal. Nevertheless, there are no fundamental barriers to rapid translation of this exciting technology to clinical research and perhaps clinical care.

An extraordinary new technique using hyperpolarized ¹³C-labeled pyruvate and taking advantage of increased glycolysis in cancer has the potential to improve the way magnetic resonance imaging is used for detection and characterization of prostate cancer. The aim of this study was to quantify, for the first time, differences in hyperpolarized [1-¹³C] pyruvate and its metabolic products between the various histologic grades of prostate cancer using the transgenic adenocarcinoma of mouse prostate (TRAMP) model. Fast spectroscopic imaging techniques were used to image lactate, alanine, and total hyperpolarized carbon (THC = lactate + pyruvate + alanine) from the entire abdomen of normal mice and TRAMP mice with low- and high-grade prostate tumors in 14 s. Within 1 week, the mice were dissected and the tumors were histologically analyzed. Hyperpolarized lactate SNR levels significantly increased (P < 0.05) with cancer development and progression (41 ± 11, 74 ± 17, and 154 ± 24 in normal prostates, low-grade primary tumors, and high-grade primary tumors, respectively) and had a correlation coefficient of 0.95 with the histologic grade. In addition, there was minimal overlap in the lactate levels between the three groups with only one of the seven normal prostates overlapping with the low-grade primary tumors. The amount of THC, a possible measure of substrate uptake, and hyperpolarized alanine also increased with tumor grade but showed more overlap between the groups. In summary, elevated hyperpolarized lactate and potentially THC and alanine are noninvasive biomarkers of prostate cancer presence and histologic grade that could be used in future three-dimensional ¹³C spectroscopic imaging studies of prostate cancer patients. [Cancer Res 2008;68(20):8607–15]

This white paper discusses prospects for advancing hyperpolarization technology to better understand cancer metabolism, identify current obstacles to HP (hyperpolarized) 13C magnetic resonance imaging's (MRI's) widespread clinical use, and provide recommendations for overcoming them. Since the publication of the first NIH white paper on hyperpolarized 13C MRI in 2011, preclinical studies involving [1-13C]pyruvate as well a number of other 13C labeled metabolic substrates have demonstrated this technology's capacity to provide unique metabolic information. A dose-ranging study of HP [1-13C]pyruvate in patients with prostate cancer established safety and feasibility of this technique. Additional studies are ongoing in prostate, brain, breast, liver, cervical, and ovarian cancer. Technology for generating and delivering hyperpolarized agents has evolved, and new MR data acquisition sequences and improved MRI hardware have been developed. It will be important to continue investigation and development of existing and new probes in animal models. Improved polarization technology, efficient radiofrequency coils, and reliable pulse sequences are all important objectives to enable exploration of the technology in healthy control subjects and patient populations. It will be critical to determine how HP 13C MRI might fill existing needs in current clinical research and practice, and complement existing metabolic imaging modalities. Financial sponsorship and integration of academia, industry, and government efforts will be important factors in translating the technology for clinical research in oncology. This white paper is intended to provide recommendations with this goal in mind.

The goal of this study was to investigate the use of lactate and alanine as metabolic biomarkers of prostate cancer using ¹H high‐resolution magic angle spinning (HR‐MAS) spectroscopy of snap‐frozen transrectal ultrasound (TRUS)‐guided prostate biopsy tissues. A long‐echo‐time rotor‐synchronized Carr‐Purcell‐Meiboom‐Gill (CPMG) sequence including an electronic reference to access in vivo concentrations (ERETIC) standard was used to determine the concentrations of lactate and alanine in 82 benign and 16 malignant biopsies (mean 26.5% ± 17.2% of core). Low concentrations of lactate (0.61 ± 0.28 mmol/kg) and alanine (0.14 ± 0.06 mmol/kg) were observed in benign prostate biopsies, and there was no significant difference between benign predominantly glandular (N = 54) and stromal (N = 28) biopsies between patients with (N = 38) and without (N = 44) a positive clinical biopsy. In biopsies containing prostate cancer there was a highly significant (P < 0.0001) increase in lactate (1.59 ± 0.61 mmol/kg) and alanine (0.26 ± 0.07 mmol/kg), and minimal overlap with lactate concentrations in benign biopsies. This study demonstrates for the first time very low concentrations of lactate and alanine in benign prostate biopsy tissues. The significant increase in the concentration of both lactate and alanine in biopsy tissue containing as little as 5% cancer could be exploited in hyperpolarized ¹³C spectroscopic imaging (SI) studies of prostate cancer patients. Magn Reson Med 60:510–516, 2008.

Purpose of review The purpose of this article is to review the current status of advanced MRI techniques based on anatomic, metabolic and physiologic properties of prostate cancer with a focus on their impact in managing prostate cancer patients. Recent findings Prostate cancer can be identified based on reduced T₂ signal intensity on MRI, increased choline and decreased citrate and polyamines on magnetic resonance spectroscopic imaging (MRSI), decreased diffusivity on diffusion tensor imaging (DTI), and increased uptake on dynamic contrast enhanced (DCE) imaging. All can be obtained within a 60-min 3T magnetic resonance exam. Each complementary method has inherent advantages and disadvantages: T₂ MRI has high sensitivity but poor specificity; magnetic resonance spectroscopic imaging has high specificity but poor sensitivity; diffusion tensor imaging has high spatial resolution, is the fastest, but sensitivity/specificity needs to be established; dynamic contrast enhanced imaging has high spatial resolution, but requires a gadolinium based contrast agent injection, and sensitivity/specificity needs to be established. Summary The best characterization of prostate cancer in individual patients will most likely result from a multiparametric (MRI/MRSI/DTI/DCE) exam using 3T magnetic resonance scanners but questions remain as to how to analyze and display this large amount of imaging data, and how to optimally combine the data for the most accurate assessment of prostate cancer. Histological correlations or clinical outcomes are required to determine sensitivity/specificity for each method and optimal combinations of these approaches.