학술논문

Systematic analysis of 18F-FDG PET and metabolism, proliferation and hypoxia markers for classification of head and neck tumors
Document Type
article
Source
BMC Cancer. 14(1)
Subject
Biomedical and Clinical Sciences
Clinical Sciences
Oncology and Carcinogenesis
Cancer
Rare Diseases
Biomedical Imaging
Animals
Biomarkers
Tumor
Cell Hypoxia
Cell Line
Tumor
Cell Proliferation
Female
Fluorodeoxyglucose F18
Head and Neck Neoplasms
Humans
Mice
Mice
Inbred BALB C
Mice
Nude
Positron-Emission Tomography
Xenograft Model Antitumor Assays
Head and neck cancer
Tumor characterization
F-18-FDG PET
Immunohistochemistry
Public Health and Health Services
Oncology & Carcinogenesis
Oncology and carcinogenesis
Epidemiology
Language
Abstract
BackgroundQuantification 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.MethodsSeveral 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.ResultsWe 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.ConclusionsWith 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.