Optimal dose and fraction number in SBRT of lung tumours: A radiobiological analysis

Published:January 24, 2017DOI:


      • The LQ model of cell survival is appropriate for use in TCP modelling of lung SBRT.
      • Uncertainty affects the dose-threshold above which 90% of local control exists.
      • Tumour hypoxia modelling supports the use of 5–8 fractions instead of 3 fractions.


      The efficacy of Stereotactic Body Radiation Therapy (SBRT) in early-stage non-small cell lung cancer for severely hypofractionated schedules is clinically proven. Tumour control probability (TCP) modelling might further optimize prescription dose and number of treatment fractions (n).
      To this end, we will discuss the following controversial questions. Which is the most plausible cell-survival model at doses per fraction (d) as high as 20 Gy? Do clinical data support a dose-response relationship with saturation over some threshold-dose? Given the reduced re-oxygenation for severe hypofractionation, is the inclusion of tumour hypoxia in TCP modelling relevant? Can iso-effective schedules be derived by assuming a homogeneous tumour-cell population with α/β ≈ 10 Gy, or should distinct cell subpopulations, with different α/β values, be taken into account? Is there scope for patient-specific individualization of n?
      Despite the difficulty of providing definite answers to the above questions, reasonable suggestions for lung SBRT can be derived from the literature. The LQ model appears to be the best-fitting model of cell-survival even at such large d, and is therefore the preferred choice for TCP modelling. TCP increases with dose, reaching saturation above 90% local control, but there is still uncertainty on the threshold-dose. In silico simulations accounting for variations in tumour oxygenation are consistent with an improved therapeutic ratio at 5–8 fractions instead of the current 3-fraction reference schedules. Tumour hypoxia modelling might also explain how α/β changes with n, identifying the clonogen subpopulation which determines tumour response. Finally, an optimal patient-specific n can be derived from the planned lung dose distribution.


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