Table S1. Excluded cohorts in the analysis and the reason for exclusion; Table S2. The datasets used for this analysis in three different groups based on Mehta, et al.'s data (2012) and the estimated EQD22.8 (Gy) of the best-fit by the model simulation; Table S3. The additional datasets used for external validation of the model analysis; Figure S1. Deviations of estimated EQD210,model from BED; Figure S2. Dose-response curves estimated from maximum likelihood method based on the EQD210,model values; Figure S4. Dose-response curves obtained by applying other models to the dataset of current study: (A) Shuryak et al.'s model (69); and (B) Tai et al.'s model (70).
ARTICLE ABSTRACT
Purpose: To demonstrate that a mathematical model can be used to quantitatively understand tumor cellular dynamics during a course of radiotherapy and to predict the likelihood of local control as a function of dose and treatment fractions.Experimental Design: We model outcomes for early-stage, localized non–small cell lung cancer (NSCLC), by fitting a mechanistic, cellular dynamics-based tumor control probability that assumes a constant local supply of oxygen and glucose. In addition to standard radiobiological effects such as repair of sub-lethal damage and the impact of hypoxia, we also accounted for proliferation as well as radiosensitivity variability within the cell cycle. We applied the model to 36 published and two unpublished early-stage patient cohorts, totaling 2,701 patients.Results: Precise likelihood best-fit values were derived for the radiobiological parameters: α [0.305 Gy−1; 95% confidence interval (CI), 0.120–0.365], the α/β ratio (2.80 Gy; 95% CI, 0.40–4.40), and the oxygen enhancement ratio (OER) value for intermediately hypoxic cells receiving glucose but not oxygen (1.70; 95% CI, 1.55–2.25). All fractionation groups are well fitted by a single dose–response curve with a high χ2 P value, indicating consistency with the fitted model. The analysis was further validated with an additional 23 patient cohorts (n = 1,628). The model indicates that hypofractionation regimens overcome hypoxia (and cell-cycle radiosensitivity variations) by the sheer impact of high doses per fraction, whereas lower dose-per-fraction regimens allow for reoxygenation and corresponding sensitization, but lose effectiveness for prolonged treatments due to proliferation.Conclusions: This proposed mechanistic tumor-response model can accurately predict overtreatment or undertreatment for various treatment regimens. Clin Cancer Res; 23(18); 5469–79. ©2017 AACR.
