Radiotherapy treatment planning studies contribute significantly to advances and improvements in radiation treatment of cancer patients. They are a pivotal step to support and facilitate the introduction of novel techniques into clinical practice, or as a first step before clinical trials can be carried out. There have been numerous examples published in the literature that demonstrated the feasibility of such techniques as IMRT, VMAT, IMPT, or that compared different treatment methods (e.g. non-coplanar vs coplanar treatment), or investigated planning approaches (e.g. automated planning). However, for a planning study to generate trustworthy new knowledge and give confidence in applying its findings, then its design, execution and reporting all need to meet high scientific standards. This paper provides a 'quality framework' of recommendations and guidelines that can contribute to the quality of planning studies and resulting publications. Throughout the text, questions are posed and, if applicable to a specific study and if met, they can be answered positively in the provided 'RATING' score sheet. A normalised weighted-sum score can then be calculated from the answers as a quality indicator. The score sheet can also be used to suggest how the quality might be improved, e.g. by focussing on questions with high weight, or by encouraging consideration of aspects given insufficient attention. Whilst the overall aim of this framework and scoring system is to improve the scientific quality of treatment planning studies and papers, it might also be used by reviewers and journal editors to help to evaluate scientific manuscripts reporting planning studies.

In a recent paper, we have published a new algorithm, designated 'iCycle', for fully automated multi-criterial optimization of beam angles and intensity profiles. In this study, we have used this algorithm to investigate the relationship between plan quality and the extent of the beam direction search space, i.e. the set of candidate beam directions that may be selected for generating an optimal plan. For a group of ten prostate cancer patients, optimal IMRT plans were made for stereotactic body radiation therapy (SBRT), mimicking high dose rate brachytherapy dosimetry. Plans were generated for five different beam direction input sets: a coplanar (CP) set and four non-coplanar (NCP) sets. For CP treatments, the search space consisted of 72 orientations (5° separations). The NCP CyberKnife (CK) space contained all directions available in the robotic CK treatment unit. The fully non-coplanar (F-NCP) set facilitated the highest possible degree of freedom in selecting optimal directions. CK(+) and CK(++) were subsets of F-NCP to investigate some aspects of the CK space. For each input set, plans were generated with up to 30 selected beam directions. Generated plans were clinically acceptable, according to an assessment of our clinicians. Convergence in plan quality occurred only after around 20 included beams. For individual patients, variations in PTV dose delivery between the five generated plans were minimal, as aimed for (average spread in V(95): 0.4%). This allowed plan comparisons based on organ at risk (OAR) doses, with the rectum considered most important. Plans generated with the NCP search spaces had improved OAR sparing compared to the CP search space, especially for the rectum. OAR sparing was best with the F-NCP, with reductions in rectum D(Mean), V(40Gy), V(60Gy) and D(2%) compared to CP of 25%, 35%, 37% and 8%, respectively. Reduced rectum sparing with the CK search space compared to F-NCP could be largely compensated by expanding CK with beams with relatively large direction components along the superior-inferior axis (CK(++)). Addition of posterior beams (CK(++) → F-NCP) did not lead to further improvements in OAR sparing. Plans with 25 beams clearly performed better than 11-beam plans. For CP plans, an increase from 11 to 25 involved beams resulted in reductions in rectum D(Mean), V(40Gy), V(60Gy) and D(2%) of 39%, 57%, 64% and 13%, respectively.

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.

Background: For conventional radiotherapy treatment units, automated planning can significantly improve plan quality. For robotic radiosurgery, systems for automatic generation of clinically deliverable plans do not yet exist. For prostate stereotactic body radiation therapy (SBRT), few studies have systematically compared VMAT with robotic treatment. Material and methods: The multi-criteria autoplanning optimizer, developed at our institute, was coupled to the commercial treatment planning system of our robotic treatment unit, for fully automated generation of clinically deliverable plans (autoROBOT). The system was then validated by comparing autoROBOT plans with manually generated plans. Next, the autoROBOT system was used for systematic comparisons between autoROBOT plans and VMAT plans, that were also automatically generated (autoVMAT). CTV-PTV margins of 3 mm were used for autoROBOT (clinical routine) and autoVMAT plan generation. For autoVMAT, an extra plan was generated with 5 mm margin (often applied for VMAT). Plans were generated for a 4 × 9.5 Gy fractionation scheme. Results: Compared to manual planning, autoROBOT improved rectum D (16%), V (75%) and D (41%), and bladder D (37%) (all p .002), with equal PTV coverage. In the autoROBOT and autoVMAT comparison, both with 3 mm margin, rectum doses were lower for autoROBOT by 5% for rectum D (p=.002), 33% for V (p=.001) and 4% for D (p=.05), with comparable PTV coverage and other OAR sparing. With 5 mm margin for VMAT, 18/20 plans had a PTV coverage lower than requested (<95%) and all plans had higher rectum doses than autoROBOT (mean percentage differences of 13% for D , 69% for V and 32% for D (all p<.001)). Conclusions: The first system for fully automated generation of clinically deliverable robotic plans was built. Autoplanning did largely enhance robotic plan quality, compared to manual planning. Using autoplanning for both the robotic system and VMAT, superiority of non-coplanar robotic treatment compared to coplanar VMAT for prostate SBRT was demonstrated.