Systematic analysis of 18F-FDG PET and metabolism, proliferation and hypoxia markers for classification of head and neck tumors
- Equal contributors
1 Department of Radiation Oncology, Radboud University Medical Center, P.O. Box 9101, Nijmegen 6500 HB, The Netherlands
2 Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, P.O. Box 616/23, Maastricht 6200 MD, The Netherlands
3 Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto MSG 0A3, Canada
4 Department of Medical Biophysics, University of Toronto, Toronto, Canada
5 Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
BMC Cancer 2014, 14:130 doi:10.1186/1471-2407-14-130Published: 26 February 2014
Quantification of molecular cell processes is important for prognostication and treatment individualization of head and neck cancer (HNC). However, individual tumor comparison can show discord in upregulation similarities when analyzing multiple biological mechanisms. Elaborate tumor characterization, integrating multiple pathways reflecting intrinsic and microenvironmental properties, may be beneficial to group most uniform tumors for treatment modification schemes. The goal of this study was to systematically analyze if immunohistochemical (IHC) assessment of molecular markers, involved in treatment resistance, and 18F-FDG PET parameters could accurately distinguish separate HNC tumors.
Several imaging parameters and texture features for 18F-FDG small-animal PET and immunohistochemical markers related to metabolism, hypoxia, proliferation and tumor blood perfusion were assessed within groups of BALB/c nu/nu mice xenografted with 14 human HNC models. Classification methods were used to predict tumor line based on sets of parameters.
We found that 18F-FDG PET could not differentiate between the tumor lines. On the contrary, combined IHC parameters could accurately allocate individual tumors to the correct model. From 9 analyzed IHC parameters, a cluster of 6 random parameters already classified 70.3% correctly. Combining all PET/IHC characteristics resulted in the highest tumor line classification accuracy (81.0%; cross validation 82.0%), which was just 2.2% higher (p = 5.2×10-32) than the performance of the IHC parameter/feature based model.
With a select set of IHC markers representing cellular processes of metabolism, proliferation, hypoxia and perfusion, one can reliably distinguish between HNC tumor lines. Addition of 18F-FDG PET improves classification accuracy of IHC to a significant yet minor degree. These results may form a basis for development of tumor characterization models for treatment allocation purposes.