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SU‐FF‐T‐368: A Biophysical Model for Adaptive Radiotherapy Based On Tumor Volume Regression During Radiation Treatment
Author(s) -
Guerrero M,
Li X
Publication year - 2005
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1998097
Subject(s) - radiation therapy , dosimetry , nuclear medicine , medicine , clonogenic assay , regression , cervical cancer , regression analysis , linear regression , volume (thermodynamics) , radiation oncology , cancer , mathematics , statistics , chemistry , physics , cell , biochemistry , quantum mechanics
Purpose: Tumor volume regression is frequently seen during the course of radiotherapy (RT). Adaptive RT (ART) considered so far is mainly geometric, (adapting radiation fields to the changing geometry of the tumor). Biological ART, (adapting radiation dose for individual patients), is the ultimate goal. The purpose of this work is to develop a biophysical model that can be used to biologically adapt RT based on tumor volume regression during the RT treatment course. Method and Materials: The survival fraction after n fractions can be calculated as (SF 221.4/Tpot) n, where SF 2 is the surviving fraction at 2 Gy and T pot is the potential doubling time. Based on this expression and other considerations, a formula to relate SF2 with the residual tumor volume during RT was developed. Two previously reported clinical data sets on tumor volume measurements during RT for cervical were used to validate the model. Results: The SF 2 can be related to the tumor volume regression during treatment as( SF 221.4 / T pot) n , where ν n is the relative tumor volume after n fractions, ρ 2 is the relative clonogenic cell density after a 2Gy fraction. The calculation of TCP versus volume regression is consistent with clinical results from two published cervical cancer studies. The calculation of TCP as a function of SF 2 indicates that certain group of patients could benefit from dose escalation. Based on the model developed, we have calculated the extra dose needed to compensate for a poor response to radiation according to tumor volume regression. Conclusion: A biophysical model that predicts the treatment effectiveness based on the measured tumor volume during the course of RT is developed. The model prediction is consistent with that observed in the clinic for cervical cancer. The model can predict whether the patient would benefit from dose escalation. More clinical data are required to validate our model.