Tumor-volume simulation during radiotherapy for head-and-neck cancer using a four-level cell population model.
OBJECTIVE
To develop a fast computational radiobiologic model for quantitative analysis of tumor volume during fractionated radiotherapy. The tumor-volume model can be useful for optimizing image-guidance protocols and four-dimensional treatment simulations in proton therapy that is highly sensitive to physiologic changes.


METHODS
The analysis is performed using two approximations: (1) tumor volume is a linear function of total cell number and (2) tumor-cell population is separated into four subpopulations: oxygenated viable cells, oxygenated lethally damaged cells, hypoxic viable cells, and hypoxic lethally damaged cells. An exponential decay model is used for disintegration and removal of oxygenated lethally damaged cells from the tumor.


RESULTS
We tested our model on daily volumetric imaging data available for 14 head-and-neck cancer patients treated with an integrated computed tomography/linear accelerator system. A simulation based on the averaged values of radiobiologic parameters was able to describe eight cases during the entire treatment and four cases partially (50% of treatment time) with a maximum 20% error. The largest discrepancies between the model and clinical data were obtained for small tumors, which may be explained by larger errors in the manual tumor volume delineation procedure.


CONCLUSIONS
Our results indicate that the change in gross tumor volume for head-and-neck cancer can be adequately described by a relatively simple radiobiologic model. In future research, we propose to study the variation of model parameters by fitting to clinical data for a cohort of patients with head-and-neck cancer and other tumors. The potential impact of other processes, like concurrent chemotherapy, on tumor volume should be evaluated.
Chvetsov
Alexei V
AV
University of Florida Proton Therapy Institute, Jacksonville, FL 32206, USA. chvetsov@ufl.edu
Dong
Lei
L
Palta
Jantinder R
JR
Amdur
Robert J
RJ
D000465:Algorithms; D002452:Cell Count; D016923:Cell Death / Q000502:physiology; D015687:Cell Hypoxia; D049109:Cell Proliferation; D002470:Cell Survival; D019583:Dose Fractionation, Radiation; D006258:Head and Neck Neoplasms / Q000473:pathology* / Q000532:radiotherapy*; D006801:Humans; D008954:Models, Biological; D019736:Prostheses and Implants; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012074:Remission Induction / Q000379:methods; D047368:Tumor Burden