Pathological validation and prognostic potential of quantitative MRI in the characterization of pancreas cancer: Preliminary experience.

Patient stratification based on biological variation in pancreatic ductal adenocarcinoma (PDAC) subtypes could help to improve clinical outcome. However, non-invasive assessment of the entire tumor microenvironment remains challenging. In this study we investigate the biological basis of dynamic contrast-enhanced (DCE), intravoxel incoherent motion (IVIM) and R2* derived Magnetic Resonance Imaging (MRI) parameters for the non-invasive characterization of the PDAC tumor microenvironment and evaluate their prognostic potential in PDAC patients. Patients diagnosed with treatment-naive resectable PDAC underwent MRI. After resection, a whole mount tumor slice was analyzed for collagen fraction, vessel density and hypoxia and matched to the MRI parameter maps. MRI parameters were correlated to immunohistochemistry derived tissue characteristics and evaluated for prognostic potential. 30 patients were included of whom 21 underwent resection with whole mount histology available in 15 patients. DCE K(trans) and ve , ADC and IVIM D correlated with collagen fraction. DCE kep and IVIM f correlated with vessel density and R2* with tissue hypoxia. Based on MRI, two main PDAC phenotypes could be distinguished; a stroma-high phenotype demonstrating high vessel density and high collagen fraction and a stroma-low phenotype with low vessel density and low collagen fraction. Patients with the stroma-high phenotype (high kep and high IVIM D, n=8) showed longer OS (not reached vs. 14 months, p=0.001, HR=9.1, p=0.004) and DFS (not reached vs. 2 months, p<0.001, HR 9.3, p=0.003) compared to the other patients (n=22). Median follow-up was 41 (95% CI: 36-46) months. MRI was able to accurately characterize tumor collagen fraction, vessel density and hypoxia in PDAC. Based on imaging parameters, a subgroup of patients with significantly better prognosis could be identified. These first results indicate that stratification based MRI derived biomarkers could help to tailor treatment and improve clinical outcome and warrant further research.