Effectiveness of Multi-Criteria Optimization-based Trade-Off exploration in combination with RapidPlan for head & neck radiotherapy planning

The challenge in radiotherapy planning is to manage the compromise between tumour irradiation and sparing of healthy tissue. The planning objectives involved can be inherently contradictory, which restricts an optimal result for all objectives at the same time. This situation is reflected in the irradiation of head and neck cancer (HNC) where there are many organs at risk (OAR) and their close proximity to the target volume makes it difficult to achieve clinically acceptable target coverage without radiation-induced toxicity and decreased of quality of life [1‐3].

For the treatment of HNC, volumetric-modulated arc therapy (VMAT) has shown good dose conformity and sparing of OAR with a decrease in treatment delivery time compared to other techniques [4‐7]. For this inverse planning technique, the desired dose is prescribed for the planning target volume (PTV) and for the surrounding structures the tolerated dose is defined; then through an iterative optimisation process a plan is computed. The optimisation result is the plan which best achieves the stated objectives by minimising a cost function associated with them. However, this result is often not satisfactory since the compromise between the planning goals might not be clinically acceptable and the optimisation objectives may not adequately describe the clinical situation. Different objective settings are tried until an acceptable solution is found, which is time consuming and may result in sub-optimal treatment plans. For the solution of this inverse problem, a Multi-Criteria Optimisation (MCO) approach has been proposed as a more efficient method [8‐15]. Whereas single objective function optimisation problems have one best solution, in MCO a vector of objective functions is optimised resulting in many best-compromised solutions that describe a mathematical construct, an approximation of the Pareto surface. In practice this is composed of alternative plans that are created, where each of them will prioritise a specific optimisation objective over all others. Therefore, each alternative plan is optimal in that one objective can only be improved by deteriorating others [16].

Eclipse treatment planning system (TPS) Varian Medical Systems, Palo Alto, CA, version 15.5 introduces the tools for a MCO approach based on trade-offs exploration (TO). A copy of an initial plan optimisation, called the balanced plan, is used together with the chosen N optimisation objectives as the base for the subsequent generation of the 3 N + 1 alternative plans for objective. The user can then explore the trade-offs along the possible solutions and select the plan that best fulfils the treatment goals. The initial plan influences the subsequent Pareto frontier approximation, therefore, trade-offs exploration around a starting promising plan is desirable [16, 17].

Recently, knowledge-based radiotherapy treatment planning has been incorporated into clinical practice. RapidPlan™ (RP) (Eclipse v15.5) operates with a knowledge-based treatment planning algorithm that is able to generate dose volume histogram (DVH) estimates for a new patient geometry by analysis of a database of previous plans. The DVH estimates are then translated into optimisation objectives parameters for intensity modulated radiotherapy (IMRT) or VMAT plans. The resulting plans have shown to reduce planner interaction while improving the quality and consistency [18, 19]. However, the quality of the plans generated for new patients depends highly on the quality and robustness of the plans in the training cohort [20].

In this study the key question addressed was how to maximise the performance of RP and TO for the radiotherapy treatment of HNC. We suggested a combination of both approaches, RP and TO used simultaneously during the optimisation process of a plan. Three different workflows were proposed for the VMAT planning of HNC patients and an inter-comparison of the resulting plans was performed. At the same time, these were compared with the plans optimised using RP and TO on their own and the clinical plans for reference.

In this study, the combined use of RP and TO was proposed focusing on HNC radiotherapy VMAT planning. Three workflows were followed using RP together with TO during the optimisation process: the use of an in- house RP model built with TO VMAT plans (RP_TO); the use of an in-house RP model, built with clinical VMAT plans, to obtain the starting optimisation objectives parameters and then further TO (RP_1 + TO); and the use of RP_TO to generate the initial objectives parameters and further TO (RP_TO⁺). The dosimetric impact was assessed on the resulting plans for a set of 20 patients. In addition, for each patient, two more plans were created using TO and RP on its own during the optimisation stage (Clin+TO, RP_1). For the five optimisation strategies and the clinical plans, a dosimetric comparison across the resulting plans was carried out. Additionally, investigation of complexity and deliverability of the plans was performed.

Although the use of RP and TO separately presents advantages over the clinical plans [11, 19], it was shown that for this treatment the combined use of both optimisation methods improves the relationship between balancing OAR doses and PTV coverage, i.e. while maintaining comparable clinically acceptable target coverage, the resulting plans reduced the OAR doses. It was demonstrated that the RP models are able to provide a semi-automatic suggestion of a plan that is a good start for TO. The OAR doses of the plans generated by the RP_TO model, built with previously TO optimised plans, reflect its benefit over the RP_1 model. Therefore, this approach suggests being convenient for centres that does not have TO but that can implement shared RP models. When comparing RP_TO and RP_1 + TO plans no significant difference was reported on OAR sparing, but the first one reduced active planner interaction. Furthermore, when RP_TO is used as the initial plan with subsequent use of TO (RP_TO⁺) this led to the largest dose reduction for all OAR. This reveals that for a new patient, the knowledge-based model that was trained with the plans that were already optimised by TO helped to obtain optimisation parameters that are closer to the desired trade-offs, providing the best starting point for TO. The results are in agreement with Wang J et al. [17] who showed for nasopharyngeal treatments that choosing better optimisation parameters for a plan with the aid of automated planning prior to use MCO led to improved plans.

RP_ TO⁺ plans achieved the lowest average OAR doses for all the dose-volume parameters investigated. Most notably, a significant reduction in mean dose of 7.9 Gy and 7.7 Gy for the left and right parotids respectively was accomplished with respect to the clinical plans. Despite the dose improvement for the OAR, the CI results showed that the dose to normal tissue was slightly compromised. Further work with NTO and the inclusion of a structure, considering normal tissue not being recognised as OAR, as part of the trade-offs exploration objectives is of our interest to investigate improvement [19]. On the contrary, OAR doses with RP_TO plans are higher than with RP_TO⁺ but this plans provided an OAR dose improvement respect to clinical plans while maintaining comparable CI and HI to them, being this influenced by the upper PTVLR objectives used in the RP model. The results from both workflows point to the capacity of TO to decide on the trade-offs pursued for this cohort of patients and agree with ongoing HNC trials that have shown decreased toxicity to the surrounding organs as a result of dose de-escalation to nodal PTV. The implementation of RP and TO for the treatment of this cohort of patients could lead to an improved patient quality of life [34, 35].

The results displayed in Table 2 support the value of TO as a tool for attaining further individualised plan improvements. It is shown that the application of TO as the final step in planning (Clin+TO, RP_1 + TO and RP_TO⁺) resulted in a consistent reduced number of plans failing to meet the OAR constraints. The reason three plan parameters failed with RP_TO⁺ as opposed to two with Clin+TO is that the additional failing plan had the largest PTV1 in the set (1069.0 cc) and this lay outside the range of the plans that constitute the RP models used; therefore, following our trade-offs management, PTV1 D₉₉ and PTVLR D₉₉ were marginally below the constraint (94.9 and 74.7% respectively). This patient was re-planned with different trade-offs exploration management and the constraints of the PTVs were met at the expense of a 2.4Gy increase in the left parotid mean dose. This example points to the fact that the success of a RP model is dependent upon the quality and robustness of the model itself [20]. Similarly, in the use of TO there is an inherent element of user influence.

The time required to create these plans is equally of relevance. The estimated time in our clinics to plan a HNC case, including optimisation and dose calculation until the final plan is produced, ranges between 50 and 300 min depending on the complexity and the experience of the planner. The use of RP and MCO in clinical practice has proven to reduce planning time [10, 36]. Following the proposed workflows, with the combined use of both approaches, the approximate time taken remained always under 80 min, being affected mostly by the speed of the calculations and the number of optimisation objectives selected rather than by the human time needed or the planner experience. Therefore, using both RP and TO at the same time did not increase our human time at the console, and overall is expected to reduce it as less interaction to reach a clinically acceptable solution is needed.

Kyroudi, et al. [37] reported dosimetric differences between the exploration of the alternative plans generated with MCO and the deliverable plans that are created after this, which take into account physical treatment unit restrictions. In this study, small discrepancies were noticed as well but they were followed by a repetition of the trade-off exploration only in a few cases when we were already in the limit of the dose required to meet the PTVs constraints. Furthermore, an evaluation of the dosimetric delivery parameters was performed to warranty plan deliverability. It was found that the complexity of the plans increased when using RP and TO together. This could be expected as the sparing of the OAR has been improved providing higher quality plans [38]. The highest mean MU value and lowest mean MF and ALPO values were observed for the RP_TO plans suggesting that these plans are more complex than the RP_1 + TO and RP_TO⁺. Although RP_TO⁺ showed better relationship between OAR sparing and PTV coverage, it can be observed that the homogeneity of the targets is slightly better when using RP_TO which has proved to have relation with an increase in complexity [39]. Despite the higher complexity of the plans that used RP and TO, the observed values were within the range of the clinical plans. Further, for the extreme values of the three proposed workflows, in most cases the results from the gamma evaluation showed improvement on the agreement of the delivered and planned dose to the OAR, and concluded that the plans are deliverable clinically.

Future work will focus on the implementation of the RP_TO and RP_TO⁺ workflows in clinical practice and analysis of the physicians’ preference on the resulting trade-offs. The use of the proposed optimisation approaches for other treatment sites is of interest and the opportunity to use RP and TO in combination to escalate the dose delivered to the tumour while maintaining or reducing OAR doses.

The use of RP combined with TO improved OAR sparing on VMAT HNC radiotherapy plans while maintaining clinically acceptable target coverage. The simultaneous use of both approaches reflects in the resulting plans the individual benefit that they provide. The best OAR sparing was obtained when planning starting by generating a plan with a RP model built with plans optimised with TO and then further individualised TO optimisation. For this workflow, the trade-off between a minor deterioration of the nodal PTV, while maintaining clinically acceptable PTVs coverage, allowed the most significant reduction in OAR doses. RP_TO plans, created with the RP model optimised with TO plans, also provided an OAR dose improvement respect to clinical plans while maintaining comparable conformity and target homogeneity. Plans optimised with RP and TO were proven to be deliverable despite an increase in plan modulation.