MATLAB®-based fitting method to evaluate survival fractions after multimodal treatment

Abstract

To easily analyse and visualize cell kill dynamics measured by survival fraction after single or combined treatments a MATLAB®-based application was developed. A statistical analysis with different options of visualisation of single and combined treatment effects can be performed in a few steps not requiring advanced knowledge of statistical programs.

Keywords: LQ-Model; MATLAB®-based fitting method; Multimodal treatment; Survival fraction.

Fig. 1

Curve visualization and statistics. A) “Main” window shows selectable evaluation tools. B) The calculated statistics is displayed in the “Command window”. C) In case of choosing “show additional statistics” the calculations are also displayed in the “Command window”, too.

Fig. 2

Visualization of log-additive, supra-log-additive effects and raw data. A) Normalization to each control (i.e. with and without drug): The difference at 0 Gy between both curves demonstrates the log-additive effect of a drug and is indicated by an arrow. A significant log-additive effect is indicated by the dark blue area until the crossing (second arrow) of the confidence interval of both curves (red/green CIs). B) Normalization to 10⁰ = 1 (classical analysis of radiosensitivity without a drug): A significant supra-log-additive effect is measured until crossing of both CIs of the radiation response curves with and without drug in a semi-logarithmic scale. In the example, the CI (red/green CIs) cross before the first applied radiation dose at ∼1.5 Gy (indicated by the blue area). Therefore, no supra-log-additive effect was measured since no radiation dose has been tested between 0 and 1.5 Gy. C) Raw data can also be visualized in a semi-logarithmic scale (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).