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Cancer Imaging

Open Access

Oxygen-enhanced MRI can accurately identify, quantify and map tumour hypoxia in preclinical models

  • JPB O'Connor1Email author,
  • JKR Boult1,
  • Y Jamin1,
  • M Babur1,
  • KG Finegan1,
  • KJ Williams1,
  • AR Reynolds1,
  • RA Little1,
  • A Jackson1,
  • GJM Parker1,
  • JC Waterton1 and
  • SP Robinson1
Cancer Imaging201515(Suppl 1):P9

Published: 2 October 2015


HydralazineRenal CancerTumour HypoxiaChronic HypoxiaAcute Change


There is need for non-invasive methods to identify, quantify and map tumour hypoxia. In this study we used an emerging technology – R1 oxygen enhanced MRI (OE-MRI) – to distinguish those tumour sub-regions that respond to hyperoxic gas challenge from refractory sub-regions. We hypothesised that the proportion of refractory tumour tissue (Oxy-R) would be a robust biomarker of tumour hypoxia across multiple models with different vascular and hypoxic phenotypes.

Methods: OE-MRI signal precision, stability and relationship to tissue pO2 were evaluated in well vascularised renal cancer 786-O xenografts. Dynamic sensitivity of proportional Oxy-R to acute changes in hypoxia was evaluated using hydralazine challenge. Relationship of proportional Oxy-R to tissue immunohistochemistry and gadolinium DCE-MRI were explored in parental and drug-resistant 786-O models and in SW620 xenografts.


Phantom and in vivo experiments demonstrated the accuracy, precision and stability of R1 measurement. The proportion of tumour Oxy-R increased significantly following hydralazine challenge (p=0.045) relative to control. The proportion of tumour with perfused Oxy-R voxels was correlated to chronic hypoxia in well perfused 786-O-R xenografts(rho 0.810, p=0.028) and in relatively necrotic SW620 xenografts (rho 0.929, p=0.002).


The proportion of tumour perfused Oxy-R is a robust biomarker of tumour hypoxia. Voxel-wise analysis of dual oxygen and gadolinium challenge has potential to quantify and map tumour hypoxia as prognostic, predictive and pharmacodynamic biomarkers that could facilitate personalised healthcare.

Authors’ Affiliations

University of Manchester, Manchester, UK


© O'Connor et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.