Development and clinical application of a probabilistic robustness evaluation tool for pencil beam scanning proton therapy treatments.

Purpose: to implement a probabilistic-Robustness-Evaluation (pRE) tool for proton therapy treatments and to correlate these results with the worst-case approach (wRE) implemented in commercial TPS for clinical applications.

Materials And Methods: 12 skull base patients were planned with a robust multiple field optimization (MFO) approach. 10 years of machine QA were analysed to derive the uncertainties of our treatment system (beam delivery and patient positioning system).

Static proton arc therapy: Comprehensive plan quality evaluation and first clinical treatments in patients with complex head and neck targets.

Background: Proton Arc Treatment (PAT) has shown potential over Multi-Field Optimization (MFO) for out-of-target dose reduction in particular for head and neck (H&N) patients. A feasibility test, including delivery in a clinical environment is still missing in the literature and a necessary requirement before clinical application of PAT.

Purpose: To perform a comprehensive comparison between clinically delivered MFO plans and static PAT plans for H&N treatments, followed by end-to-end commissioning of the system to prepare for clinical treatments.

Dosimetric and NTCP advantages of robust proton therapy over robust VMAT for Stage III NSCLC in the immunotherapy era.

Aims: To assess the robustness and to define the dosimetric and NTCP advantages of pencil-beam-scanning proton therapy (PBSPT) compared with VMAT for unresectable Stage III non-small lung cancer (NSCLC) in the immunotherapy era.

Material And Methods: 10 patients were re-planned with VMAT and PBSPT using: 1) ITV-based robust optimization with 0.5 cm setup uncertainties and (for PBSPT) 3.

Robustness evaluation of pencil beam scanning proton therapy treatment planning: A systematic review.

Compared to conventional radiotherapy using X-rays, proton therapy, in principle, allows better conformity of the dose distribution to target volumes, at the cost of greater sensitivity to physical, anatomical, and positioning uncertainties. Robust planning, both in terms of plan optimization and evaluation, has gained high visibility in publications on the subject and is part of clinical practice in many centers. However, there is currently no consensus on the methods and parameters to be used for robust optimization or robustness evaluation.