title: A comparative analysis of radiobiological models for cell surviving fractions at high doses.
abstract: For many years the linear-quadratic (LQ) model has been widely used to describe the effects of total dose and dose per fraction at low-to-intermediate doses in conventional fractionated radiotherapy. Recent advances in stereotactic radiosurgery (SRS) and stereotactic radiotherapy (SRT) have increased the interest in finding a reliable cell survival model, which will be accurate at high doses, as well. Different models have been proposed for improving descriptions of high dose survival responses, such as the Universal Survival Curve (USC), the Kavanagh-Newman (KN) and several generalizations of the LQ model, e.g. the Linear-Quadratic-Linear (LQL) model and the Pade Linear Quadratic (PLQ) model. The purpose of the present study is to compare a number of models in order to find the best option(s) which could successfully be used as a fractionation correction method in SRT. In this work, six independent experimental data sets were used: CHOAA8 (Chinese hamster fibroblast), H460 (non-small cell lung cancer, NSLC), NCI-H841 (small cell lung cancer, SCLC), CP3 and DU145 (human prostate carcinoma cell lines) and U1690 (SCLC). By detailed comparisons with these measurements, the performance of nine different radiobiological models was examined for the entire dose range, including high doses beyond the shoulder of the survival curves. Using the computed and measured cell surviving fractions, comparison of the goodness-of-fit for all the models was performed by means of the reduced χ (2)-test with a 95% confidence interval. The obtained results indicate that models with dose-independent final slopes and extrapolation numbers generally represent better choices for SRT. This is especially important at high doses where the final slope and extrapolation numbers are presently found to play a major role. The PLQ, USC and LQL models have the least number of shortcomings at all doses. The extrapolation numbers and final slopes of these models do not depend on dose. Their asymptotes for the cell surviving fractions are exponentials at low as well as high doses, and this is in agreement with the behaviour of the corresponding experimental data. This is an important improvement over the LQ model which predicts a Gaussian at high doses. Overall and for the highlighted reasons, it was concluded that the PLQ, USC and LQL models are theoretically well-founded. They could prove useful compared to the other proposed radiobiological models in clinical applications for obtaining uniformly accurate cell surviving fractions encountered in stereotactic high-dose radiotherapy as well as at medium and low doses.
authors.affiliation: Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden.
authors.forename: B
authors.initials: B
authors.lastname: Andisheh
authors.forename: M
authors.initials: M
authors.lastname: Edgren
authors.forename: Dž
authors.initials: D
authors.lastname: Belkić
authors.forename: P
authors.initials: P
authors.lastname: Mavroidis
authors.forename: A
authors.initials: A
authors.lastname: Brahme
authors.forename: B K
authors.initials: BK
authors.lastname: Lind
mesh_terms: D000465:Algorithms; D000818:Animals; D016466:CHO Cells; D045744:Cell Line, Tumor / Q000528:radiation effects; D002470:Cell Survival / Q000528:radiation effects*; D006224:Cricetinae; D019583:Dose Fractionation, Radiation; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008954:Models, Biological; D009369:Neoplasms / Q000532:radiotherapy*; D012062:Relative Biological Effectiveness