Abstract
Radiobiology has progressed sufficiently to provide suitable understanding of the radiation response of a wide spectrum of cell lines, and this principal provides a degree of certainty in the capability to describe this radiation response using mathematical models. The focus of this project was to examine the ability of a mechanistic Tumor Control Probability (TCP) model to predict treatment outcomes for a wide range of treatment strategies for Non-Small Cell Lung Cancer (NSCLC), such as hypo-fractionation, standard fractionation, and hyper-fractionation. A fully heterogeneous population-averaged TCP model was fit to clinical outcome data accumulated from the literature for NSCLC using optimized radiosensitivity values produced by the simplex algorithm. The clinical data reported the two-year local tumor control rate for Stage I–II NSCLC for dose prescription varies between 1.5–15 Gy per fraction. The biological parameters α, β, σα, and σβ were obtained with the simplex algorithm’s search of the radiosensitivity solution space of the outcome data described above. These values were obtained through a robust fitting procedure using the simplex algorithm based on the best available clinical data for dose response, total initial clonogen number, clonogen density, clonogen distribution, and hypoxia status of the average in NSCLC patients. The TCP model achieves an excellent level of fit, R2value of 0.92, and RMSE of 2.4% for the clinical outcome data for hyper, standard, and hypofractionated treatments using realistic values for biological input parameters. Residuals ⩽3% are produced by the TCP model when compared to clinical outcome data for both standard fractionation and SABR.
Published Version
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