Highlights
- •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.
Abstract
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.
Keywords
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Article info
Publication history
Published online: January 24, 2017
Accepted:
December 14,
2016
Received in revised form:
December 2,
2016
Received:
September 28,
2016
Identification
Copyright
© 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
