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Abstract Purpose We consider the following scenario: A radiotherapy clinic has a limited number of proton therapy slots available each day to treat cancer patients of a given tumor site. The clinic's goal is to minimize the expected number of complications in the cohort of all patients of that tumor site treated at the clinic, and thereby maximize the benefit of its limited proton resources.
Methods To address this problem, we extend the normal tissue complication probability (NTCP) model–based approach to proton therapy patient selection to the situation of limited resources at a given institution. We assume that, on each day, a newly diagnosed patient is scheduled for treatment at the clinic with some probability and with some benefit from protons over photons, which is drawn from a probability distribution. When a new patient is scheduled for treatment, a decision for protons or photons must be made, and a patient may wait only for a limited amount of time for a proton slot becoming available. The goal is to determine the thresholds for selecting a patient for proton therapy, which optimally balance the competing goals of making use of all available slots while not blocking slots with patients with low benefit. This problem can be formulated as a Markov decision process (MDP) and the optimal thresholds can be determined via a value‐policy iteration method.
Results The optimal thresholds depend on the number of available proton slots, the average number of patients under treatment, and the distribution of values. In addition, the optimal thresholds depend on the current utilization of the facility. For example, if one proton slot is available and a second frees up shortly, the optimal threshold is lower compared to a situation where all but one slot remain blocked for longer.
Conclusions MDP methodology can be used to augment current NTCP model–based patient selection methods to the situation that, on any given day, the number of proton slots is limited. The optimal threshold then depends on the current utilization of the proton facility. Although, the optimal policy yields only a small nominal benefit over a constant threshold, it is more robust against variations in patient load.
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Abstract Purpose The radiobiological benefits afforded by spatially fractionated (GRID) radiation therapy pairs well with the dosimetric advantages of proton therapy. Inspired by the emergence of energy‐layer specific collimators in pencil beam scanning (PBS), this work investigates how the spot spacing and collimation can be optimized to maximize the therapeutic gains of a GRID treatment while demonstrating the integration of a dynamic collimation system (DCS) within a commercial beamline to deliver GRID treatments and experimentally benchmark Monte Carlo calculation methods.
Methods GRID profiles were experimentally benchmarked using a clinical DCS prototype that was mounted to the nozzle of the IBA‐dedicated nozzle system. Integral depth dose (IDD) curves and lateral profiles were measured for uncollimated and GRID‐collimated beamlets. A library of collimated GRID dose distributions were simulated by placing beamlets within a specified uniform grid and weighting the beamlets to achieve a volume‐averaged tumor cell survival equivalent to an open field delivery. The healthy tissue sparing afforded by the GRID distribution was then estimated across a range of spot spacings and collimation widths, which were later optimized based on the radiosensitivity of the tumor cell line and the nominal spot size of the PBS system. This was accomplished by using validated models of the IBA universal and dedicated nozzles.
Results Excellent agreement was observed between the measured and simulated profiles. The IDDs matched above 98.7% when analyzed using a 1%/1‐mm gamma criterion with some minor deviation observed near the Bragg peak for higher beamlet energies. Lateral profile distributions predicted using Monte Carlo methods agreed well with the measured profiles; a gamma passing rate of 95% or higher was observed for all in‐depth profiles examined using a 3%/2‐mm criteria. Additional collimation was shown to improve PBS GRID treatments by sharpening the lateral penumbra of the beamlets but creates a trade‐off between enhancing the valley‐to‐peak ratio of the GRID delivery and the dose‐volume effect. The optimal collimation width and spot spacing changed as a function of the tumor cell radiosensitivity, dose, and spot size. In general, a spot spacing below 2.0 cm with a collimation less than 1.0 cm provided a superior dose distribution among the specific cases studied.
Conclusions The ability to customize a GRID dose distribution using different collimation sizes and spot spacings is a useful advantage, especially to maximize the overall therapeutic benefit. In this regard, the capabilities of the DCS, and perhaps alternative dynamic collimators, can be used to enhance GRID treatments. Physical dose models calculated using Monte Carlo methods were experimentally benchmarked in water and were found to accurately predict the respective dose distributions of uncollimated and DCS‐collimated GRID profiles.
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.ijrobp.2021.03.054
