Quantification of Prostate Cancer Metabolism Using 3D Multiecho bSSFP and Hyperpolarized [1-13 C] Pyruvate: Metabolism Differs Between Tumors of the Same Gleason Grade.

BACKGROUND: Three-dimensional (3D) multiecho balanced steady-state free precession (ME-bSSFP) has previously been demonstrated in preclinical hyperpolarized (HP) 13 C-MRI in vivo experiments, and it may be suitable for clinical metabolic imaging of prostate cancer (PCa). PURPOSE: To validate a signal simulation framework for the use of sequence parameter optimization. To demonstrate the feasibility of ME-bSSFP for HP 13 C-MRI in patients. To evaluate the metabolism in PCa measured by ME-bSSFP. STUDY TYPE: Retrospective single-center cohort study. PHANTOMS/POPULATION: Phantoms containing aqueous solutions of [1-13 C] lactate (2.3 M) and [13 C] urea (8 M). Eight patients (mean age 67 ± 6 years) with biopsy-confirmed Gleason 3 + 4 (n = 7) and 4 + 3 (n = 1) PCa. FIELD STRENGTH/SEQUENCES: 1 H MRI at 3 T with T2 -weighted turbo spin-echo sequence used for spatial localization and spoiled dual gradient-echo sequence used for B0 -field measurement. ME-bSSFP sequence for 13 C MR spectroscopic imaging with retrospective multipoint IDEAL metabolite separation. ASSESSMENT: The primary endpoint was the analysis of pyruvate-to-lactate conversion in PCa and healthy prostate regions of interest (ROIs) using model-free area under the curve (AUC) ratios and a one-directional kinetic model (kP ). The secondary objectives were to investigate the correlation between simulated and experimental ME-bSSFP metabolite signals for HP 13 C-MRI parameter optimization. STATISTICAL TESTS: Pearson correlation coefficients with 95% confidence intervals and paired t-tests. The level of statistical significance was set at P < 0.05. RESULTS: Strong correlations between simulated and empirical ME-bSSFP signals were found (r > 0.96). Therefore, the simulation framework was used for sequence optimization. Whole prostate metabolic HP 13 C-MRI, observing the conversion of pyruvate into lactate, with a temporal resolution of 6 seconds was demonstrated using ME-bSSFP. Both assessed metrics resulted in significant differences between PCa (mean ± SD) (AUC = 0.33 ± 012, kP  = 0.038 ± 0.014) and healthy (AUC = 0.15 ± 0.10, kP  = 0.011 ± 0.007) ROIs. DATA CONCLUSION: Metabolic HP 13 C-MRI in the prostate using ME-bSSFP allows for differentiation between aggressive PCa and healthy tissue. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

First-in-human in-vivo depiction of paraganglioma metabolism by hyperpolarised 13C-magnetic resonance.

Phaeochromocytomas (PCC) and paragangliomas (PGL), cumulatively referred to as PPGLs, are neuroendocrine tumours arising from neural crest-derived cells in the sympathetic and parasympathetic nervous systems. Predicting future tumour behaviour and the likelihood of metastatic disease remains problematic as genotype-phenotype correlations are limited, the disease has variable penetrance and, to date, no reliable molecular, cellular or histological markers have emerged. Tumour metabolism quantification can be considered as a method to delineating tumour aggressiveness by utilising hyperpolarised 13 C-MR (HP-MR). The technique may provide an opportunity to non-invasively characterise disease behaviour. Here, we present the first instance of the analysis of PPGL metabolism via HP-MR in a single case.

A reproducible dynamic phantom for sequence testing in hyperpolarised 13C-magnetic resonance.

OBJECTIVE: To develop a phantom system which can be integrated with an automated injection system, eliminating the experimental variability that arises with manual injection; for the purposes of pulse sequence testing and metric derivation in hyperpolarised 13C-MR. METHODS: The custom dynamic phantom was machined from Ultem and filled with a nicotinamide adenine dinucleotide and lactate dehydrogenase mixture dissolved in phosphate buffered saline. Hyperpolarised [1-13C]-pyruvate was then injected into the phantom (n = 8) via an automated syringe pump and the conversion of pyruvate to lactate monitored through a 13C imaging sequence. RESULTS: The phantom showed low coefficient of variation for the lactate to pyruvate peak signal heights (11.6%) and dynamic area-under curve ratios (11.0%). The variance for the lactate dehydrogenase enzyme rate constant (kP) was also seen to be low at 15.6%. CONCLUSION: The dynamic phantom demonstrates high reproducibility for quantification of 13C-hyperpolarised MR-derived metrics. Establishing such a phantom is needed to facilitate development of hyperpolarsed 13C-MR pulse sequenced; and moreover, to enable multisite hyperpolarised 13C-MR clinical trials where assessment of metric variability across sites is critical. ADVANCES IN KNOWLEDGE: The dynamic phantom developed during the course of this study will be a useful tool in testing new pulse sequences and standardisation in future hyperpolarised work.

Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly.

Imaging plays a fundamental role in all aspects of the cancer management pathway. However, conventional imaging techniques are largely reliant on morphological and size descriptors that have well-known limitations, particularly when considering targeted-therapy response monitoring. Thus, new imaging methods have been developed to characterise cancer and are now routinely implemented, such as diffusion-weighted imaging, dynamic contrast enhancement, positron emission technology (PET) and magnetic resonance spectroscopy. However, despite the improvement these techniques have enabled, limitations still remain. Novel imaging methods are now emerging, intent on further interrogating cancers. These techniques are at different stages of maturity along the biomarker pathway and aim to further evaluate the cancer microstructure (vascular, extracellular and restricted diffusion for cytometry in tumours) magnetic resonance imaging (MRI), luminal water fraction imaging] as well as the metabolic alterations associated with cancers (novel PET tracers, hyperpolarised MRI). Finally, the use of machine learning has shown powerful potential applications. By using prostate cancer as an exemplar, this Review aims to showcase these potentially potent imaging techniques and what stage we are at in their application to conventional clinical practice.

Hyperpolarised 13C MRI: a new horizon for non-invasive diagnosis of aggressive breast cancer.

Hyperpolarised 13C MRI (HP-MRI) is a novel imaging technique that allows real-time analysis of metabolic pathways in vivo.1 The technology to conduct HP-MRI in humans has recently become available and is starting to be clinically applied. As knowledge of molecular biology advances, it is increasingly apparent that cancer cell metabolism is related to disease outcomes, with lactate attracting specific attention. 2 Recent reviews of breast cancer screening programs have raised concerns and increased public awareness of over treatment. The scientific community needs to shift focus from improving cancer detection alone to pursuing novel methods of distinguishing aggressive breast cancers from those which will remain indolent. HP-MRI offers the opportunity to identify aggressive tumour phenotypes and help monitor/predict therapeutic response. Here we report one of the first cases of breast cancer imaged using HP-MRI alongside correlative conventional imaging, including breast MRI.

First-in-human in vivo non-invasive assessment of intra-tumoral metabolic heterogeneity in renal cell carcinoma.

Intratumoral genetic heterogeneity and the role of metabolic reprogramming in renal cell carcinoma (RCC) have been extensively documented. However, the distribution of these metabolic changes within the tissue has not been explored. We report on the first-in-human in vivo non-invasive metabolic interrogation of RCC using hyperpolarized carbon-13 (13C) magnetic resonance imaging (HP-MRI) and describe the validation of in vivo lactate metabolic heterogeneity against multi-regional ex vivo mass spectrometry. HP-MRI provides an in vivo assessment of metabolism and provides a novel opportunity to safely and non-invasively assess cancer heterogeneity.