Radiobiological issues in proton therapy

Abstract

The relative biological effectiveness (RBE) for particle therapy is a complex function of particle type, radiation dose, linear energy transfer (LET), cell type, endpoint, etc. In the clinical practice of proton therapy, the RBE is assumed to have a fixed value of 1.1. This assumption, along with the effects of physical uncertainties, may mean that the biologically effective dose distributions received by the patient may be significantly different from what is seen on treatment plans. This may contribute to unforeseen toxicities and/or failure to control the disease. Variability of Proton RBE: It has been shown experimentally that proton RBE varies significantly along the beam path, especially near the end of the particle range. While there is now an increasing acceptance that proton RBE is variable, there is an ongoing debate about whether to change the current clinical practice. Clinical Evidence: A rationale against the change is the uncertainty in the models of variable RBE. Secondly, so far there is no clear clinical evidence of the harm of assuming proton RBE to be 1.1. It is conceivable, however, that the evidence is masked partially by physical uncertainties. It is, therefore, plausible that reduction in uncertainties and their incorporation in the estimation of dose actually delivered may isolate and reveal the variability of RBE in clinical practice. Nevertheless, clinical evidence of RBE variability is slowly emerging as more patients are treated with protons and their response data are analyzed. Modelling and Incorporation of RBE in the Optimization of Proton Therapy: The improvement in the knowledge of RBE could lead to better understanding of outcomes of proton therapy and in the improvement of models to predict RBE. Prospectively, the incorporation of such models in the optimization of intensity-modulated proton therapy could lead to improvements in the therapeutic ratio of proton therapy.

Figure 1

Depth-LET and depth-dose (Bragg) curves for a 79.7 MeV monoenergetic 20 cm × 20 cm scanned proton beam. The LET increases slowly up to about one cm before the Bragg peak and steeply at points beyond the peak. Reproduced from Guan et al. [1]

Figure 2

(A) Measured RBE for 10% surviving fraction of non-small cell lung cancer (NSCLC) H460 and H1437 cells as a function of LET for a monoenergetic 79.7 MeV scanned proton beam. The RBE values are lower than 1.1 for low LET values at the entrance of the beam in the medium and increase linearly up to the Bragg peak, and then rapidly and non-linearly at points beyond the Bragg peak. Reproduced from Guan et al. [4] (B) Relative biological effectiveness as a function of depth along monoenergetic and modulated 62-MeV proton beams for the radioresistant glioma U87 cells. RBE values are relative to 225 kVp x-rays using proton alpha and beta values obtained by the linear quadratic model. Reproduced from Chaudhary et al. [7]

Figure 3

Image change predictions of an analytical model fitted with clinical MR image response data. Voxel image change probability is displayed as a function of dose for constant LET values in panel A, and as a function of LET for constant dose values in panel B. Panel C shows physical dose that produces 50% probability of image change in voxels (“TD50”) as a function of LET calculated using the image change predictive model derived from clinical image response data. The equation for the fit of TD50 versus track-averaged LET (LET_t) is also shown. Reproduced from Peeler et al. [6]

Figure 4

Comparison of measured and model-predicted RBE values as a function of LET_d for the H460 lung cancer cell line. Predicted RBE values were computed using seven models of Wilkens and Oelfke, Wedenberg et al., Carabe-Fernandez et al., McNamara et al., Chen and Ahmad, and RMF, which are cited in the text. All models predict RBE to be essentially a linear function of LET, especially up to the Bragg peak. (C. Peeler, MD Anderson Cancer Center, unpublished.).

Figure 5

(A) Comparison of a glioblastoma IMPT plan optimized using criteria defined in terms of fixed RBE (RBE = 1.1) vs. the plan optimized using criteria defined in terms of variable RBE. The variable RBE was computed using the Wilkens and Oelfke model [17]. The top two panels display dose distributions in terms of variable RBE-weighted dose whereas the bottom two panels display biological effect in terms of (1-surviving fraction) calculated using the linear-quadratic model. Panels (B) and (C) compare a pediatric brain tumor IMPT plan optimized based on criteria defined in terms of RBE 1.1-weighted dose vs. a plan based on the same criteria plus additional terms that control LET in the target and normal structures. Panel (B) compares the RBE 1.1-weighted DVHs for the GTV, brainstem and normal brain, whereas panel (C) compares the corresponding LET-volume histograms. This figure demonstrates that significant increases in LET values in the tumor and significant reduction in normal structures is possible. Of course, the achievable biological effect gain depends on the geometric configuration of anatomic structures and may necessitate tradeoffs. (W. Cao, MD Anderson Cancer Center, unpublished.).