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Last Updated: 09/21/17
Quantitative Imaging Network (QIN)

Princess Margaret Cancer Centre
Toronto, Canada

Image-based quantitative assessment of tumor hypoxia

David Jaffray, Ph.D

Cancer is a major cause of death and reduced quality of life, with roughly 1.6 million new cases in Canada and the US per year and a mortality rate of over 37%. Individual tumour diversity, which is sensitive to the local tumour microenvironment, poses significant challenges to selecting personalized treatment. The overall hypothesis of this research is that the tumor micro-environment is a significant factor in patient outcomes and response to therapy. One of the indicators of poor outcome is hypoxia that develops in solid malignant tumours when the metabolic demand for oxygen exceeds availability and strongly influences gene expression and cell phenotype, resulting in the creation of sub-populations with aggressive characteristics including genetic instability, increased metastasis, and resistance to all current forms of treatment.

Hypoxia is in particular a factor that has potential therapeutics in clinical trials, while positron-emission tomography (PET) agents can aid in non-invasively imaging of tumor hypoxia.

We seek to develop quantitative, multi-parametric approaches to hypoxia imaging to increase the predictive capacity of the hypoxia markers and improve the stratification of patients for hypoxia-targeted treatment strategies.

This work includes several aims, including the development of standardized acquisition methodology, integrating perfusion imaging methods to create a more robust tracer kinetic model for hypoxia imaging, and developing a software application to solve these models and produce quantitative metrics of hypoxia.

Thereafter, these developments will be validated in on-going clinical trials, some of which include oral pimonidazole to produce a histology gold standard against which to compare the imaging results.

Our aim to standardize hypoxia imaging protocols will provide a guideline for the imaging community to design clinical studies in hypoxia imaging with PET tracers. The development of advanced hypoxia tracer models coupled with perfusion will provide an understanding of the interplay between hypoxia and perfusion in tumors. These models can be readily adapted by other researchers in the imaging community. In addition, analysis of clinical studies of different anatomical sites will provide baseline data which can also be used for the design of future clinical trials.


  1. Coolens C, Driscoll B, Foltz W, Pellow C, Menard C, Chung C Comparison of Voxel-Wise Tumor Perfusion Changes Measured With Dynamic Contrast-Enhanced (DCE) MRI and Volumetric DCE CT in Patients With Metastatic Brain Cancer Treated with Radiosurgery. Tomography (2016). Vol.2, #4:325-333. DOI: 10.18383/j.tom.2016.00178.

  2. Coolens C, Driscoll B, Moseley J, Brock KK, Dawson AL Feasibility of 4D perfusion CT imaging for the assessment of liver treatment response following SBRT and sorafenib. Advances in Radiation Oncology (2016). Vol. 1, # 3:194-203. DOI: 10.1016/j.adro.2016.06.004.

  3. Han K, Croke J, Foltz W, Metser U, Xie J, Shek T, Driscoll B, Menard C, Vines D, Coolens C, Simeonov A, Beiki-Ardakani A, Leung E, Levin W, Fyles A, Milosevic M A prospective study of DWI, DCE-MRI and FDG PET imaging for target delineation in brachytherapy for cervical cancer. Radiotherapy and Oncology (2016). Vol. 120, #3:519-525. PMID: 27528120.

  4. Taylor E, Yeung I, Keller H, Wouters GB, Milosevic M, Hedley WD, Jaffray DA Quantifying hypoxia in human cancers using static PET imaging. Physics in Medicine and Biology (2016). Volume 61, Number 22. PMID: 27779123.

  5. Kueng R, Driscoll B, Manser P, Fix M, Stampanoni M, Keller H Quantification of local image noise variation in PET images for standardization of noise-dependent analysis metrics. Biomedical Physics & Engineering Express, Vol. 3, # 2. DOI: 10.1088/2057-1976/3/2/025007.

  6. Metran-Nascente C, Yeung I, Vines CV, Metser U, Dhani CN, Green D, Milosevic M, Jaffray DA, Hedley WD Measurement of Tumor Hypoxia in Patients with Advanced Pancreatic Cancer Based on 18F-Fluoroazomyin Arabinoside Uptake. Journal of Nuclear Medicine (2016). Vol. 57:361-366. PMID: 26769863.

  7. Kalpathy-Cramer J, Mamomov A, Zhao B, et al. Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. Tomography (2016), Vol. 2, # 4:430-437. DOI: 10.18383/j.tom.2016.00235.

  8. Driscoll B, Jaffray D, Coolens C (2014). Development of a Multi-Centre Clinical Trial Data Archiving and Analysis Platform for Functional Imaging. Journal of Physics: Conference Series, 489(1), 012089. DOI: 10.1088/1742-6596/489/1/012089.

  9. Coolens C, Driscoll B, Chung C, Shek T, Gorjizadeh A, Ménard C, Jaffray DA (2014). Automated Voxel-Based Analysis of Volumetric Dynamic Contrast-Enhanced CT Data Improves Measurement of Serial Changes in Tumor Vascular Biomarkers. Int J Radiat Oncol Biol Phys. S0360-3016(14) 04177-7. PMID: 25446606.

Weblink to Quantitative Imaging for Personalized Cancer Medicine (QIPCM) which supports QIN activities in University Health Network in Toronto, Canada:

Princess Margaret Hospital.
Princess Margaret Hospital.