Prostate cancer (PCa) is one of the most prevalent and fatal malignancies for men worldwide. Standard screening and diagnostic tools, including prostate specific antigen (PSA) testing, digital rectal examinations (DRE), and 12-core transrectal ultrasound (TRUS) biopsy, suffer from a lack of PCa-specificity and sensitivity, resulting in overtreatment of indolent PCa. The combination of multiparametric magnetic resonance imaging (mpMRI) and ultrasound allows for improved visualization of anatomical structures, assessment of tumor aggressiveness, and probe-tracking capabilities; however, such a Fusion biopsy is still prone to false-positive and false-negative results due to the presence of various factors. Metabolomics, as a promising field for cancer biomarker discovery, can provide new perspectives for evaluating PCa prognosis, aggressiveness, and clinical significance. In this study of >15 years, we present the capacity for metabolomic analyses to not only distinguish between PCa and benign tissues, but also differentiate patients who are diagnosed with PCa years after initial negative biopsy. Over a period of 15 years and with 10 years of follow-up up till now, 432 biopsy cores from 332 patients with suspicious for PCa underwent TRUS (n=232) and Fusion (n=100) biopsies. One core from TRUS biopsies, and two cores (one target and one non-target core opposite the target) from Fusion biopsies underwent ex vivo MRS analysis. All cores were analyzed by high-resolution magic angle spinning (HRMAS) MRS on a Bruker 600MHz spectrometer at 4ºC with a rotor-synchronized Min(A,B) protocol with spinning at 600 and 700Hz. Spectra were curve-fit and transformed into statistical matrices using a MATLAB-based program. Metabolic spectral regions of interest (ROIs) (n=48) with >60% of samples presenting detectable values were analyzed with principal component analysis (PCA) and other statistical tools. Following the MRS analysis, all 432 cores returned to pathology for quantitative pathology evaluation and recorded in patient records. In this presentation, we will demonstrate the capability of measuring PCa metabolomics non-destructively from human biopsy cores using ex vivo HRMAS MRS, and present PCa metabolomic predictive potential measured from biopsy cores through patient follow-ups 5~15 years. Most notably, predictions of negative biopsy patients to be discovered with PCa within 5 years may be achieved with MRs-based metabolomics, while longer follow-ups are needed and continued in our laboratory.

This talk will focus on recent developments in the use of cardiovascular magnetic resonance (CMR) to evaluate cardiac function, perfusion, myocardial mechanics, and the role of artificial intelligence in research and clinical CMR applications.