Quantifying the Dynamics of Field Cancerization in Tobacco-Related Head and Neck Cancer: A Multiscale Modeling Approach.
Journal Article (Journal Article)
High rates of local recurrence in tobacco-related head and neck squamous cell carcinoma (HNSCC) are commonly attributed to unresected fields of precancerous tissue. Because they are not easily detectable at the time of surgery without additional biopsies, there is a need for noninvasive methods to predict the extent and dynamics of these fields. Here, we developed a spatial stochastic model of tobacco-related HNSCC at the tissue level and calibrated the model using a Bayesian framework and population-level incidence data from the Surveillance, Epidemiology, and End Results (SEER) registry. Probabilistic model analyses were performed to predict the field geometry at time of diagnosis, and model predictions of age-specific recurrence risks were tested against outcome data from SEER. The calibrated models predicted a strong dependence of the local field size on age at diagnosis, with a doubling of the expected field diameter between ages at diagnosis of 50 and 90 years, respectively. Similarly, the probability of harboring multiple, clonally unrelated fields at the time of diagnosis was found to increase substantially with patient age. On the basis of these findings, we hypothesized a higher recurrence risk in older than in younger patients when treated by surgery alone; we successfully tested this hypothesis using age-stratified outcome data. Further clinical studies are needed to validate the model predictions in a patient-specific setting. This work highlights the importance of spatial structure in models of epithelial carcinogenesis and suggests that patient age at diagnosis may be a critical predictor of the size and multiplicity of precancerous lesions. Cancer Res; 76(24); 7078-88. ©2016 AACR.
- Ryser, MD; Lee, WT; Ready, NE; Leder, KZ; Foo, J
- December 15, 2016
Volume / Issue
- 76 / 24
Start / End Page
- 7078 - 7088
Pubmed Central ID
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)
- United States