Novel MRI Assessment of Prostate Cancer VALIDATE-PRO

PURPOSE AND DESIGN The purpose of this study is to assess the ability of novel MRI techniques and their derived metrics in the diagnosis and prognostication of prostate cancer. Improving the diagnostic accuracy and investigating biomarkers with non- invasive MRI techniques have the promise of reducing unnecessary biopsies or radical treatment, as well as detecting those likely to progress at an earlier stage. Vascular, Extracellular and Restricted Cytometry in Tumours (VERDICT) MRI Diffusion-weighted imaging (DWI) is one of the most important sequences in mp-MRI of the prostate which reflects the diffusivity of water in cells and differs in cancerous tumour due to changes in cellular density, size, shape and arrangement. VERDICT is a novel framework which uses a more complex 3-compartment tissue model for diffusion of water; 1. Water trapped in cells, 2. Water in the vascular network and 3. Interstitial water. This allows us to make estimates of specific tissue properties such as the size and packing density of the cells, the vascular extracellular- extravascular space (EES) volume fractions. This methods is more biologically specific compared to conventional DWI. Studies in tumour xenograft models of colorectal cancer showed its ability to detect such known differences in the microstructure. At UCL we have applied VERDICT to the prostates of 78 men and were able to successfully differentiate between benign or clinically insignificant cancer and clinically significant tumours. This technique can be applied on commercially available MRI scanners and requires no additional contrast agents or requirements for the patient compared to conventional mp-MRI of the prostate. Luminal Index (LI) MRI Using a multi-echo T2 sequence we can differentiate between the relatively long T2 values of the luminal space and the short T2 values of the stromal and epithelial cells to estimate the fractional volume of water in each MR voxel, the luminal water fraction (LWF). Studies have shown a good correlation between LWF and histologically measure luminal fractional volume and hence promise to detect prostate cancer and predict Gleason score. Our refined local LI MRI sequence has been very good at differentiating clinically significant and non-significant tumours. This technique also requires no additional contrast agents or patient requirements to be completed. Hyperpolarised \[1-13C\]-pyruvate MRI (HYP-MRI) Cancer cell rely on enhanced glycolysis for their energy supply, a phenomenon known as the Warburg effect. This is a key discriminator of malignancy and normal tissues and has been successfully targeted with well-established imaging techniques such as Fluorodeoxyglucose (FDG) - Positron Emission Tomography (PET). HYP-MRI is able to image a part of this metabolic process in real-time. An injectable solution containing 13C labelled pyruvate which is hyperpolarised onsite in order to increase the eventual detectable signal. The naturally occurring metabolise pyruvate is converted to lactate with the attached 13C tag persisting enabling us to track the presence and conversion of pyruvate to lactate after the injection. It heralds the potential to differentiate tumours which are more likely to grow and metastasise from those which won't. This sequence requires an additional injection compared to standard mp-MRI and also uses an endorectal coil which is place inside the rectum for the duration of the scan. The main objectives of this study are as follow and are addressed in three sub-studies: * To biologically validate HYP-MRI with correlation against biological measurements in men undergoing radical prostatectomy (BioVal: 44 patients) * Clinical validation of novel MRI techniques (VERDICT/LI/HYP-MRI) derived measurements for differentiating patients with aggressive (progressive) and indolent (non-progressive) prostate cancer in men entering active surveillance (ProVal: 120 patients) * Technical validation of novel MRI techniques (VERDICT/LI/HYP-MRI) through assessment of repeatability metrics from derived parameters (TecVal: 20 patients)