Modeling the cellular response of lung cancer to radiation therapy for a broad range of fractionation schedules

Jeho Jeong, Jung Hun Oh, Jan Jakob Sonke, Jose Belderbos, Jeffrey D. Bradley, Andrew N. Fontanella, Shyam S. Rao, Joseph O. Deasy

    Research output: Contribution to journalArticlepeer-review

    28 Scopus citations


    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 We applied the model to 36 published and two unpublished early-stage patient cohorts, totaling 2,701 patients.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: a [0.305 Gy1; 95% confidence interval (CI), 0.120–0.365], the a/b 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 x2 P value, indicating consistency with the fitted model. The analysis was further validated with an additional 23 patient cohorts (n ¼ 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.

    Original languageEnglish
    Pages (from-to)5469-5479
    Number of pages11
    JournalClinical Cancer Research
    Issue number18
    StatePublished - Sep 15 2017


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