Adaptive proton therapy

Abstract

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

Keywords: adaptive radiation therapy; online adaptation; proton therapy.

Figure 1:

Adaptation workflow: A: Adaptation at constant intervals using the initial plan assessed on the new image; B: Adaptation triggered by on-line imaging based on the initial plan; C: Daily adaptation using the initial plan assessed; D: Frequent (e.g., daily) adaptation using the accumulated dose and previous day image and adapted plan as reference. Figure adapted from Heukelom and Fuller (Heukelom and Fuller, 2019).

Figure 2:

Frameworks for adaptive radiation therapy (RT). Off-line imaging is used for standard radiation therapy as well as off-line adaptive therapy for a selected number of fractions (left). Using image registration, on-line imaging also allows off-line adaptive therapy as well as image-guided therapy and in-line adaptation (middle) On-line adaptive therapy, the focus of this review, is shown on the right (dark colors) and aims at complete re-planning or plan adjustment.

Figure 3:

Uncorrected CBCT (left) is unsuitable for proton dose calculation because of large HU errors. This may be corrected by deforming the planning CT to match the CBCT (center), or removing scatter from CBCT projection images prior to reconstruction (right). From (Landry et al., 2019), with permission.

Figure 4:

Indirect imaging of proton dose using PET (left) and proton range verification using prompt gamma (right). From (Min et al., 2013) and (Richter et al., 2016), with permission.

Figure 5

a: Evolution of the target conformity (high-risk CTV and low-risk CTV) over the full course of treatment for a three head and neck cancer patients. The target coverage and overdose are evaluated using the D₉₈ and D₂, respectively (both in % of the prescription dose). The red dots represent which fractions were adapted in a weekly adaptation scenario (OA_W). Overall, daily adaptation (OA_D) achieved the best target coverage, while both weekly and daily outperform treatment based on the base plan (BP). ((Bobic et al., 2021); with permission). b: DVHs for the three patients shown in figure 5a, comparing the accumulated doses over the course of treatment, delivered by BP (dotted line), OA_W (dashed line), and OA_D (solid line). OA_W yields remarkably similar performances to OA_D for patients A and C, both in terms of target coverage and OAR sparing. For patient B, a clear difference regarding the high-risk CTV coverage can be observed between OA_W and OA_D. In terms of target coverage, patient B exhibits the worst result for OA_W, which barely reached the clinical objective for the high-risk CTV. (Bobic et al., 2021); with permission).

Figure 5

a: Evolution of the target conformity (high-risk CTV and low-risk CTV) over the full course of treatment for a three head and neck cancer patients. The target coverage and overdose are evaluated using the D₉₈ and D₂, respectively (both in % of the prescription dose). The red dots represent which fractions were adapted in a weekly adaptation scenario (OA_W). Overall, daily adaptation (OA_D) achieved the best target coverage, while both weekly and daily outperform treatment based on the base plan (BP). ((Bobic et al., 2021); with permission). b: DVHs for the three patients shown in figure 5a, comparing the accumulated doses over the course of treatment, delivered by BP (dotted line), OA_W (dashed line), and OA_D (solid line). OA_W yields remarkably similar performances to OA_D for patients A and C, both in terms of target coverage and OAR sparing. For patient B, a clear difference regarding the high-risk CTV coverage can be observed between OA_W and OA_D. In terms of target coverage, patient B exhibits the worst result for OA_W, which barely reached the clinical objective for the high-risk CTV. (Bobic et al., 2021); with permission).