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A successful proton beam therapy (PBT) center relies heavily on the proper function and maintenance of a proton beam therapy machine. However, when a PBT machine is non-operational, a proton facility is hindered with delays that can potentially lead to inferior treatment outcome due to treatment interruption. This article reports a viable solution for a photon back-up plan in a proton down event.The implementation of a workflow for which proton plans are converted to photon plans so that patients can be treated using photons has been a successful strategy to reduce delays and mitigate its effect on patient care. This workflow was established through collaboration of physicians, physicists, dosimetrists, therapists, nurses, and schedulers.A tiered system established by disease site, number of fractions, and individualized circumstances is used to prioritize patients. Proton-photon backup planning strategy and physics check details were described. This article provides an overview of workflow of conversion of PBT to photon when the PBT machine is down. Specific needs of patients undergoing proton beam therapy are addressed.

Purpose Radiation therapy for mesothelioma remains challenging, as normal tissue toxicity limits the amount of radiation that can be safely delivered to the pleural surfaces, especially radiation dose to the contralateral lung. The physical properties of proton therapy result in better sparing of normal tissues when treating the pleura, both in the postpneumonectomy setting and the lung-intact setting. Compared with photon radiation, there are dramatic reductions in dose to the contralateral lung, heart, liver, kidneys, and stomach. However, the tissue heterogeneity in the thorax, organ motion, and potential for changing anatomy during the treatment course all present challenges to optimal irradiation with protons. Methods The clinical data underlying proton therapy in mesothelioma are reviewed here, including indications, advantages, and limitations. Results The Particle Therapy Cooperative Group Thoracic Subcommittee task group provides specific guidelines for the use of proton therapy for mesothelioma. Conclusions This consensus report can be used to guide clinical practice, insurance approval, and future research.

Background To compare plan robustness of volumetric modulated arc therapy (VMAT) with intensity modulated radiation therapy (IMRT) and to compare the effectiveness of 3 plan robustness quantification methods. Methods and materials The VMAT and IMRT plans were created for 9 head and neck cancer patients. For each plan, 6 new perturbed dose distributions were computed using ±3 mm setup deviations along each of the 3 orientations. Worst-case analysis (WCA), dose-volume histogram (DVH) band (DVHB), and root-mean-square dose-volume histogram (RVH) were used to quantify plan robustness. In WCA, a shaded area in the DVH plot bounded by the DVHs from the lowest and highest dose per voxel was displayed. In DVHB, we displayed the envelope of all DVHs in band graphs of all the 7 dose distributions. The RVH represents the relative volume on the vertical axis and the root-mean-square-dose on the horizontal axis. The width from the first 2 methods at different target DVH indices (such as D 95% and D 5% ) and the area under the RVH curve for the target were used to indicate plan robustness. Results were compared using Wilcoxon signed-rank test. Results The DVHB showed that the width at D 95% of IMRT was larger than that of VMAT (unit Gy) (1.59 vs 1.18) and the width at D 5% of IMRT was comparable to that of VMAT (0.59 vs 0.54). The WCA showed similar results between IMRT and VMAT plans (D 95% : 3.28 vs 3.00; D 5% : 1.68 vs 1.95). The RVH showed the area under the RVH curve of IMRT was comparable to that of VMAT (1.13 vs 1.15). No statistical significance was found in plan robustness between IMRT and VMAT. Conclusions The VMAT is comparable to IMRT in terms of plan robustness. For the 3 quantification methods, WCA and DVHB are DVH parameter–dependent, whereas RVH captures the overall effect of uncertainties.

Purpose We compared conventionally optimized intensity modulated proton therapy (IMPT) treatment plans against worst-case scenario optimized treatment plans for lung cancer. The comparison of the 2 IMPT optimization strategies focused on the resulting plans' ability to retain dose objectives under the influence of patient setup, inherent proton range uncertainty, and dose perturbation caused by respiratory motion. Methods and materials For each of the 9 lung cancer cases, 2 treatment plans were created that accounted for treatment uncertainties in 2 different ways. The first used the conventional method: delivery of prescribed dose to the planning target volume that is geometrically expanded from the internal target volume (ITV). The second used a worst-case scenario optimization scheme that addressed setup and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of changes in patient anatomy attributable to respiratory motion were investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the 2 groups were compared with 2-sided paired Student t tests. Results Without respiratory motion considered, we affirmed that worst-case scenario optimization is superior to planning target volume–based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, worst-case scenario optimization still achieved more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality (D 95% ITV, 96.6% vs 96.1% [ P = .26]; D 5% − D 95% ITV, 10.0% vs 12.3% [ P = .082]; D 1% spinal cord, 31.8% vs 36.5% [ P = .035]). Conclusions Worst-case scenario optimization led to superior solutions for lung IMPT. Despite the fact that worst-case scenario optimization did not explicitly account for respiratory motion, it produced motion-resistant treatment plans. However, further research is needed to incorporate respiratory motion into IMPT robust optimization.