Roadmap: proton therapy physics and biology

Abstract

The treatment of cancer with proton radiation therapy was first suggested in 1946 followed by the first treatments in the 1950s. As of 2020, almost 200 000 patients have been treated with proton beams worldwide and the number of operating proton therapy (PT) facilities will soon reach one hundred. PT has long moved from research institutions into hospital-based facilities that are increasingly being utilized with workflows similar to conventional radiation therapy. While PT has become mainstream and has established itself as a treatment option for many cancers, it is still an area of active research for various reasons: the advanced dose shaping capabilities of PT cause susceptibility to uncertainties, the high degrees of freedom in dose delivery offer room for further improvements, the limited experience and understanding of optimizing pencil beam scanning, and the biological effect difference compared to photon radiation. In addition to these challenges and opportunities currently being investigated, there is an economic aspect because PT treatments are, on average, still more expensive compared to conventional photon based treatment options. This roadmap highlights the current state and future direction in PT categorized into four different themes, 'improving efficiency', 'improving planning and delivery', 'improving imaging', and 'improving patient selection'.

Keywords: dosimetry; imaging; proton radiation therapy.

Figure 1.

Illustration of stochastic programming applied to parameter uncertainty in RBE models. A simple model for RBE-weighted dose, RBE · d = (c₁ + c₂ LET) · d, is considered. RBE uncertainty is modeled via 3 scenarios: (1) a constant RBE of 1.1 (c₁^s = 1.1, c₂^s = 0), (2) a variable RBE with (c₁^s = 1.0, c₂^s = 0.04 μm keV⁻¹). This corresponds to the assumption that the RBE of a proton pencil beam is 1.0–1.1 in the entrance region, 1.2–1.3 near the Bragg peak, and 1.5–1.6 in the falloff region. (3) an intermediate scenario (c₁^s = 1.05, c₂^s = 0.02 μm keV⁻¹). An RBE-weighted dose of 54 Gy(RBE) is prescribed to the target volume, and 57 Gy(RBE) was allowed in parts not overlapping OARs. (a) (Bottom row) demonstrates the problems with conventional planning based on a RBE of 1.1. When evaluated for variable RBE, hot spots >60 Gy(RBE) in OARs overlaying the target can be observed, resulting from high LET. (b) (Top row) shows issues with IMPT optimization based on a fixed RBE model. The method leads to lower physical doses in parts of the target, potentially leading to under dosage (<50 Gy(RBE)) if the LET effect on RBE is overestimated by the model. (c) Shows that robust optimization incorporating RBE uncertainty yields adequate target dose distributions in both situations.

Figure 2.

Adaptive proton therapy is an interventional process that requires imaging and algorithms to detect changes and identify improvements, and then a subsequent corrective strategy.

Figure 3.

Scoring sheet for common online/offline correction strategies and their corresponding uncertainties. WET:water-equivalent-thickness; OAR: organ-at-risk.

Figure 4.

Example of the dedicated in-beam PET scanner in treatment position at CNAO (a) and the dynamically reconstructed PET activation data in comparison to the simulated predictions (b) in two different time windows during proton beam delivery. The PET images (color wash) are superimposed onto the planning x-ray CT (gray scale). Adapted from Fiorina et al (2018), Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Figure 5.

(a) Schematic of the knife-edge slit camera, as deployed in the first clinical study of (Xie et al 2017), projecting the PG signal (green) from the proton beam (blue) onto the position sensitive scintillators beyond the collimator. The corresponding analysis results in the spot-by-spot (with aggregation) range difference comparison in beam-eye-view (b) as well as PG-based estimation of the measured (green) and predicted (red) Bragg peak depth overlaid with the planning CT (c) for a given energy layer and treatment fraction. Adapted from Xie et al (2017), Copyright © 2017 Elsevier Inc. All rights reserved.

Figure 6.

Schematic of the spectroscopic system of Hueso-Gonzalez et al (2018) integrated in the proton beam gantry for a representative treatment position (a), along with the details of the energy- and time-resolved detector components beyond the collimator (b). The results enable quantifying the range difference from a prediction model for each applied spot (c) along with carbon (d) and oxygen concentrations, in this example obtained when inserting a slab phantom on the left of the beam path in water (with spot aggregation). Adapted from Hueso-Gonzalez et al (2018). © 2018 Institute of Physics and Engineering in Medicine. All rights reserved.

Figure 7.

Proton RBE for clonogenic cell survival as predicted by an empirical model (McNamara, Schuemann et al 2015). Left: RBE as a function of LET_d at 2 Gy for (α/β)_x = 2 Gy (solid) and 10 Gy (dashed). Middle: RBE as a function of dose for LET_d = 2.5 keV μm⁻¹ for (α/β)_x = 2 Gy (solid) and 10 Gy (dashed). Right: RBE as a function of (α/β)_x for a photon dose of 2 Gy and LET_d values of 2 keV μm⁻¹ (solid) and 10 keV μm⁻¹ (dashed). The gray areas and projection lines highlight the clinically most relevant regions for standard fractionation. (α/β)_x refers to the ratio of α and β for the x-ray dose-response curve.

Figure 8.

Potential advances in imaging for proton treatment planning, starting from the current situation (bottom). They can be grouped in two tracks—improvements for range prediction accuracy and improvements for tissue segmentation. The proposed periods correspond to the broad clinical application of the respective techniques. (autoSEG = auto segmentation).

Figure 9.

Schematic overview of model-based selection procedure.

Figure 10.

Left: significance map (−log p) of BED differences between IMRT and PSPT patients (spared regions), Right: significance map (−log p) of BED differences between patients who developed radiation pneumonitis and who did not (sensitive regions) (adapted from Palma et al 2019a, , © 2019 Elsevier Inc. All rights reserved).

Figure 11.

Illustration of a genomic biomarker framework to predict tumor response to proton therapy.

Figure 12.

(a) left: Treatment plans for an intracranial tumor (left: IMRT; right: proton therapy). (b) Right: schematic illustration of the dose rate during a typical treatment for passively scattered proton therapy (red) and photon therapy (blue).

Figure 13.

Lymphocyte sparing in pancreatic cancer using conformal treatments (Reprinted from Wild et al 2016, Copyright © 2016. Published by Elsevier Inc).