The proton arc therapy treatment planning problem is NP-Hard.

Proton arc therapy (PAT) is an advanced radiotherapy technique using charged particles in which the radiation device rotates continuously around the patient while irradiating the tumor. Compared to conventional, fixed-angle beam delivery mode, proton arc therapy has the potential to further improve the quality of cancer treatment by delivering accurate radiation dose to tumors while minimizing damage to surrounding healthy tissues. However, the computational complexity of treatment planning in PAT raises challenges as to its effective implementation. In this paper, we demonstrate that designing a PAT plan through algorithmic methods is a NP-hard problem (in fact, NP-complete), where the problem size is determined by the number of discrete irradiation angles from which the radiation can be delivered. This finding highlights the inherent complexity of PAT treatment planning and emphasizes the need for efficient algorithms and heuristics to address the challenges associated with optimizing the delivery of radiation doses in this context.

Adequacy of in-mission training to treat tibial shaft fractures in mars analogue testing.

Long bone fractures are a concern in long-duration exploration missions (LDEM) where crew autonomy will exceed the current Low Earth Orbit paradigm. Current crew selection assumptions require extensive complete training and competency testing prior to flight for off-nominal situations. Analogue astronauts (n = 6) can be quickly trained to address a single fracture pattern and then competently perform the repair procedure. An easy-to-use external fixation (EZExFix) was employed to repair artificial tibial shaft fractures during an inhabited mission at the Mars Desert Research Station (Utah, USA). Bone repair safety zones were respected (23/24), participants achieved 79.2% repair success, and median completion time was 50.04 min. Just-in-time training in-mission was sufficient to become autonomous without pre-mission medical/surgical/mechanical education, regardless of learning conditions (p > 0.05). Similar techniques could be used in LDEM to increase astronauts' autonomy in traumatic injury treatment and lower skill competency requirements used in crew selection.

An Easy-To-Use External Fixator for All Hostile Environments, from Space to War Medicine: Is It Meant for Everyone's Hands?

Long bone fractures in hostile environments pose unique challenges due to limited resources, restricted access to healthcare facilities, and absence of surgical expertise. While external fixation has shown promise, the availability of trained surgeons is limited, and the procedure may frighten unexperienced personnel. Therefore, an easy-to-use external fixator (EZExFix) that can be performed by nonsurgeon individuals could provide timely and life-saving treatment in hostile environments; however, its efficacy and accuracy remain to be demonstrated. This study tested the learning curve and surgical performance of nonsurgeon analog astronauts (n = 6) in managing tibial shaft fractures by the EZExFix during a simulated Mars inhabited mission, at the Mars Desert Research Station (Hanksville, UT, USA). The reduction was achievable in the different 3D axis, although rotational reductions were more challenging. Astronauts reached similar bone-to-bone contact compared to the surgical control, indicating potential for successful fracture healing. The learning curve was not significant within the limited timeframe of the study (N = 4 surgeries lasting <1 h), but the performance was similar to surgical control. The results of this study could have important implications for fracture treatment in challenging or hostile conditions on Earth, such as war or natural disaster zones, developing countries, or settings with limited resources.

Bi-criteria Pareto optimization to balance irradiation time and dosimetric objectives in proton arc therapy.

Objective. Proton arc therapy (PAT) is a new delivery technique that exploits the continuous rotation of the gantry to distribute the therapeutic dose over many angular windows instead of using a few static fields, as in conventional (intensity-modulated) proton therapy. Although coming along with many potential clinical and dosimetric benefits, PAT has also raised a new optimization challenge. In addition to the dosimetric goals, the beam delivery time (BDT) needs to be considered in the objective function. Considering this bi-objective formulation, the task of finding a good compromise with appropriate weighting factors can turn out to be cumbersome.Approach. We have computed Pareto-optimal plans for three disease sites: a brain, a lung, and a liver, following a method of iteratively choosing weight vectors to approximate the Pareto front with few points. Mixed-integer programming (MIP) was selected to state the bi-criteria PAT problem and to find Pareto optimal points with a suited solver.Main results. The trade-offs between plan quality and beam irradiation time (staticBDT) are investigated by inspecting three plans from the Pareto front. The latter are carefully picked to demonstrate significant differences in dose distribution and delivery time depending on their location on the frontier. The results were benchmarked against IMPT and SPArc plans showing the strength of degrees of freedom coming along with MIP optimization.Significance. This paper presents for the first time the application of bi-criteria optimization to the PAT problem, which eventually permits the planners to select the best treatment strategy according to the patient conditions and clinical resources available.

Treatment planning in arc proton therapy: Comparison of several optimization problem statements and their corresponding solvers.

Arc proton therapy (ArcPT) is an emerging modality in cancer treatments. It delivers the proton beams following a sequence of irradiation angles while the gantry is continuously rotating around the patient. Compared to conventional proton treatments (intensity modulated proton therapy, IMPT), the number of beams is significantly increased bringing new degrees of freedom that leads to potentially better cancer care. However, the optimization of such treatment plans becomes more complex and several alternative statements of the problem can be considered and compared in order to solve the ArcPT problem. Three such problem statements, distinct in their mathematical formulation and properties, are investigated and applied to solving the ArcPT optimization problem. They make use of (i) fast iterative shrinkage-thresholding algorithm (FISTA), (ii) local search (LS) and (iii) mixed-integer programming (MIP). The treatment plans obtained with those methods are compared among them, but also with IMPT and an existing state-of-the-art method: Spot-Scanning Proton Arc (SPArc). MIP stands out at low scale problems both in terms of dose quality and time delivery efficiency. FISTA shows high dose quality but experiences difficulty to optimize the energy sequence while LS is mostly the antagonist. This detailed study describes independent approaches to solve the ArcPT problem and depending on the clinical case, one should be cautiously picked rather than the other. This paper gives the first formal definition of the problem at stake, as well as a first reference benchmark. Finally, empirical conclusions are drawn, based on realistic assumptions.