Purpose We describe a treatment plan optimization method for intensity-modulated proton therapy (IMPT) that avoids high values of linear energy transfer (LET) in critical structures located within or near the target volume, while limiting degradation of the best possible physical dose distribution. Methods To allow fast optimization based on dose and LET, a GPU-based Monte-Carlo code was extended to provide dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of LET and physical dose (LETxD). To first approximation, LETxD represents a measure of the additional biological dose that is caused by high LET. Results The method is effective for treatments where serial critical structures with maximum dose constraints are located within or near the target. We report on 5 patients with intra-cranial tumors (high-grade meningiomas, base-of-skull chordomas, ependymomas) where the target volume overlaps with the brainstem and optic structures. In all cases, high LETxD in critical structures could be avoided while minimally compromising physical dose planning objectives. Conclusion LET-based reoptimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and RBE-based planning. The method makes IMPT treatments safer by mitigating a potentially increased risk of side effects due to elevated relative biological effectiveness (RBE) of proton beams near the end of range.

To develop an online plan adaptation algorithm for intensity modulated proton therapy (IMPT) based on fast Monte Carlo dose calculation and cone beam CT (CBCT) imaging. A cohort of ten head and neck cancer patients with an average of six CBCT scans were studied. To adapt the treatment plan to the new patient geometry, contours were propagated to the CBCTs with a vector field (VF) calculated with deformable image registration between the CT and the CBCTs. Within the adaptive planning algorithm, beamlets were shifted following the VF at their distal falloff and raytraced in the CBCT to adjust their energies, creating a geometrically adapted plan. Four geometric adaptation modes were studied: unconstrained geometric shifts (Free), isocenter shift (Iso), a range shifter (RS), or isocenter shift and range shifter (Iso-RS). After evaluation of the geometrical adaptation, the weights of a selected subset of beamlets were automatically tuned using MC-generated influence matrices to fulfill the original plan requirements. All beamlet calculations were done with a fast Monte Carlo running on a GPU (graphics processing unit). Geometrical adaptation alone only worked with small anatomy changes. The weight-tuned adaptation worked for every fraction, with the Free and Iso modes performing similarly and being superior than the two range shifters modes. The mean V95 and V107 were 99.4  ±  0.9 and 6.4%  ±  4.7% in the Free mode with weight tuning. The calculation time per fraction was ~5 min, but further task parallelization could reduce it to ~1–2 min for delivery adaptation right after patient setup. An online adaptation algorithm was developed that significantly improved the treatment quality for inter-fractional geometry changes. Clinical implementation of the algorithm would allow delivery adaptation right before treatment and thus allow planning margin reductions for IMPT.

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.