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Radiation Oncology
Published in: Radiation Oncology 1/2016

Open Access 01-12-2016 | Research

Challenges in radiobiological modeling: can we decide between LQ and LQ-L models based on reviewed clinical NSCLC treatment outcome data?

Authors: Alina Santiago, Steffen Barczyk, Urszula Jelen, Rita Engenhart-Cabillic, Andrea Wittig

Published in: Radiation Oncology | Issue 1/2016

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Abstract

Aim

To study the dose-response of stage I non-small-cell lung cancer (NSCLC) in terms of long-term local tumor control (LC) after conventional and hypofractionated photon radiotherapy, modeled with the linear-quadratic (LQ) and linear-quadratic-linear (LQ-L) approaches and to estimate the clinical α/β ratio within the LQ frame.

Material and methods

We identified studies of curative radiotherapy as single treatment through MedLine search reporting 3-year LC as primary outcome of interest. Logistic models coupled with the biologically effective dose (BED) at isocenter and PTV edge according to both the LQ and LQ-L models with α/β = 10 Gy were fitted. Additionally, α/β was estimated from direct LQ fits.

Results

Thirty one studies were included reporting outcome of 2319 patients. The LQ-L fit yielded a significant value of 11.0 ± 5.2 Gy for the dose threshold (Dt) for BED10 at the isocenter. The LQ and LQ-L fits did not differ substantially. Concerning the estimation of α/β, the value obtained from the direct LQ fit for the complete fractionation range was 3.9 [68 % CI: 2.2–9.0] Gy (p > 0.05).

Conclusion

Both LQ and LQ-L fits can model local tumor control after conventionally and hypofractionated irradiation and are robust methods for predicting clinical effects. The observed dose-effect for local control in NSCLC is weaker at high doses due to data dispersion. For BED10 values of 100–150 Gy in ≥3 fractions, the differences in isoeffects predicted by both models can be neglected.
Appendix
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Metadata
Title
Challenges in radiobiological modeling: can we decide between LQ and LQ-L models based on reviewed clinical NSCLC treatment outcome data?
Authors
Alina Santiago
Steffen Barczyk
Urszula Jelen
Rita Engenhart-Cabillic
Andrea Wittig
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Radiation Oncology / Issue 1/2016
Electronic ISSN: 1748-717X
DOI
https://doi.org/10.1186/s13014-016-0643-5

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