Proton therapy is becoming increasingly available, so it is important to apply objective and individualized patient selection to identify those who are expected to benefit most from proton therapy compared to conventional intensity modulated radiation therapy (IMRT). Comparative treatment planning using normal tissue complication probability (NTCP) evaluation has recently been proposed. This work investigates the impact of NTCP model and dose uncertainties on model-based patient selection. We used IMRT and intensity modulated proton therapy (IMPT) treatment plans of 78 oropharyngeal cancer patients, which were generated based on automated treatment planning and evaluated based on three published NTCP models. A reduction in NTCP of more than a certain threshold (e.g. 10% lower NTCP) leads to patient selection for IMPT, referred to as 'nominal' selection. To simulate the effect of uncertainties in NTCP-model coefficients (based on reported confidence intervals) and planned doses on the accuracy of model-based patient selection, the Monte Carlo method was used to sample NTCP-model coefficients and doses from a probability distribution centered at their nominal values. Patient selection accuracy within a certain sample was defined as the fraction of patients which had similar selection in both the 'nominal' and 'sampled' scenario. For all three NTCP models, the median patient selection accuracy was found to be above 70% when only NTCP-model uncertainty was considered. Selection accuracy decreased with increasing uncertainty resulting from differences between planned and delivered dose. In case of excessive dose uncertainty, selection accuracy decreased to 60%. Model and dose uncertainty highly influence the accuracy of model-based patient selection for proton therapy. A reduction of NTCP-model uncertainty is necessary to reach more accurate model-based patient selection.

Many approaches for automated treatment planning (autoplanning) have been proposed and investigated. Autoplanning can enhance plan quality compared to 'manual' trial-and-error planning, and decrease routine planning workload. A few approaches have been implemented in commercial treatment planning systems (TPSs). We performed a pre-clinical validation of a new system ('NovelATP') that is based on fully-automated multi-criterial optimization (MCO). The aim of NovelATP is to automatically generate for each patient a single high-quality, Pareto-optimal plan without manual Pareto navigation. Validation was performed by generating VMAT/IMRT plans for conventional treatment of prostate cancer (101 pts), prostate SBRT (20 pts), bilateral head-and-neck cancer (50 pts) and rectal cancer treated at an MR-Linac (23 pts). NovelATP autoplans were compared to plans that were generated with our in-house autoplanning system. In many previous validation studies, the latter system consistently showed enhanced plan quality when compared to manual planning. Dosimetrical differences between NovelATP and benchmark plans were on average small and presumably not clinically relevant, pointing at high NovelATP dosimetric plan quality. MUs were 11-19% higher with NovelATP. NovelATP delivery times were up to 12% longer. Overall, there was a slight disadvantage for NovelATP regarding gamma analyses. Calculation times for NovelATP plans were between 29 and 151 min with no overall differences with the benchmark plans. The new autoplanning system was able to produce high-quality plans for four highly different planning protocols/treatment sites with a total of 194 patients investigated.

The impact of treatment accuracy on NTCP-based patient selection for proton therapy is currently unknown. This study investigates this impact for oropharyngeal cancer patients. Data of 78 patients was used to automatically generate treatment plans for a simultaneously integrated boost prescribing 70 Gy_RBE/54.25 Gy_RBE in 35 fractions. IMRT treatment plans were generated with three different margins; intensity modulated proton therapy (IMPT) plans for five different setup and range robustness settings. Four NTCP models were evaluated. Patients were selected for proton therapy if NTCP reduction was ≥10% or ≥5% for grade II or III complications, respectively. The degree of robustness had little impact on patient selection for tube feeding dependence, while the margin had. For other complications the impact of the robustness setting was noticeably higher. For high-precision IMRT (3 mm margin) and high-precision IMPT (3 mm setup/3% range error), most patients were selected for proton therapy based on problems swallowing solid food (51.3%) followed by tube feeding dependence (37.2%), decreased parotid flow (29.5%), and patient-rated xerostomia (7.7%). Treatment accuracy has a significant impact on the number of patients selected for proton therapy. Therefore, it cannot be ignored in estimating the number of patients for proton therapy.

Background and purpose: In this study we developed a workflow for fully-automated generation of deliverable IMRT plans for a 1.5 T MR-Linac (MRL) based on contoured CT scans, and we evaluated automated MRL planning for rectal cancer. Methods: The Monte Carlo dose calculation engine used in the clinical MRL TPS (Monaco, Elekta AB, Stockholm, Sweden), suited for high accuracy dose calculations in a 1.5 T magnetic field, was coupled to our in-house developed Erasmus-iCycle optimizer. Clinically deliverable plans for 23 rectal cancer patients were automatically generated in a two-step process, i.e., multi-criterial fluence map optimization with Erasmus-iCycle followed by a conversion into a deliverable IMRT plan in the clinical TPS. Automatically generated plans (AUTOplans) were compared to plans that were manually generated with the clinical TPS (MANplans). Results: With AUTOplanning large reductions in planning time and workload were obtained; 4–6 h mainly hands-on planning for MANplans vs ∼1 h of mainly computer computation time for AUTOplans. For equal target coverage, the bladder and bowel bag D_mean was reduced in the AUTOplans by 1.3 Gy (6.9%) on average with a maximum reduction of 4.5 Gy (23.8%). Dosimetric measurements at the MRL demonstrated clinically acceptable delivery accuracy for the AUTOplans. Conclusions: A system for fully automated multi-criterial planning for a 1.5 T MR-Linac was developed and tested for rectal cancer patients. Automated planning resulted in major reductions in planning workload and time, while plan quality improved. Negative impact of the high magnetic field on the dose distributions could be avoided.

Background: Currently, radiation-oncologists generally evaluate a single treatment plan for each patient that is possibly adapted by the planner prior to final approval. There is no systematic exploration of patient-specific trade-offs between planning aims, using a set of treatment plans with a-priori defined (slightly) different balances. To this purpose, we developed an automated workflow and explored its use for prostate cancer. Materials and Methods: For each of the 50 study patients, seven plans were generated, including the so-called clinical plan, with currently clinically desired ≥99% dose coverage for the low-dose planning target volume (PTV_Low). The six other plans were generated with different, reduced levels of PTV_Low coverage, aiming at reductions in rectum dose and consequently in predicted grade≥2 late gastro-intestinal (GI) normal tissue complication probabilities (NTCPs), while keeping other dosimetric differences small. The applied NTCP model included diabetes as a non-dosimetric predictor. All plans were generated with a clinically applied, in-house developed algorithm for automated multi-criterial plan generation. Results: With diabetes, the average NTCP reduced from 24.9 ± 4.5% for ≥99% PTV_Low coverage to 17.3 ± 2.6% for 90%, approaching the NTCP (15.4 ± 3.0%) without diabetes and full PTV_Low coverage. Apart from intended differences in PTV_Low coverage and rectum dose, other differences between the clinical plan and the six alternatives were indeed minor. Obtained NTCP reductions were highly patient-specific (ranging from 14.4 to 0.1%), depending on patient anatomy. Even for patients with equal NTCPs in the clinical plan, large differences were found in NTCP reductions. Conclusions: A clinically feasible workflow has been proposed for systematic exploration of patient-specific trade-offs between various treatment aims. For each patient, automated planning is used to generate a limited set of treatment plans with well-defined variations in the balances between the aims. For prostate cancer, trade-offs between PTV_Low coverage and predicted GI NTCP were explored. With relatively small coverage reductions, significant NTCP reductions could be obtained, strongly depending on patient anatomy. Coverage reductions could also make up for enhanced NTCPs related to diabetes as co-morbidity, again dependent on the patient. The proposed system can play an important role in further personalization of patient care.

Background: With the large-scale introduction of volumetric modulated arc therapy (VMAT), selection of optimal beam angles for coplanar static-beam IMRT has increasingly become obsolete. Due to unavailability of VMAT in current MR-linacs, the problem has re-gained importance. An application for automated IMRT treatment planning with integrated, patient-specific computer-optimization of beam angles (BAO) was used to systematically investigate computer-aided generation of beam angle class solutions (CS) for replacement of computationally expensive patient-specific BAO. Rectal cancer was used as a model case.Materials and Methods: 23 patients treated at a Unity MR-linac were included. BAO_x plans (x=7-12 beams) were generated for all patients. Analyses of BAO₁₂ plans resulted in CS_x class solutions. BAO_x plans, CS_x plans, and plans with equi-angular setups (EQUI_x, x=9-56) were mutually compared.Results: For x>7, plan quality for CS_x and BAO_x was highly similar, while both were superior to EQUI_x. E.g. with CS₉, bowel/bladder D_mean reduced by 22% [11%, 38%] compared to EQUI₉ (p<0.001). For equal plan quality, the number of EQUI beams had to be doubled compared to BAO and CS.Conclusions: Computer-generated beam angle CS could replace individualized BAO without loss in plan quality, while reducing planning complexity and calculation times, and resulting in a simpler clinical workflow. CS and BAO largely outperformed equi-angular treatment. With the developed CS, time consuming beam angle re-optimization in daily adaptive MR-linac treatment could be avoided. Further systematic research on computerized development of beam angle class solutions for MR-linac treatment planning is warranted.