Session Item

Commonly used interactive (‘manual’) inverse plan optimization for IMRT (Intensity Modulated Radiation Therapy) and VMAT (Volumetric Modulated Arc Therapy) is workload and time intensive, while plan quality may heavily depend on planner’s expertise.
In the last few years, automatic planning (AP) has been introduced with the aim of reducing planning time and inter-operator variability and improve plan quality. Different approaches to automated planning can be used, the most common methods being knowledge-based planning (KBP), protocol-based automatic iterative optimization (PB-AIO), and multicriteria optimization (MCO) which can be pareto-navigation driven or automated. Some kind of solution is now available in most commercial treatment planning systems.
The process of introducing automatic planning in clinical practice is generally laborious, requiring algorithm configuration and validation. KBP configuration includes creation of OAR DVH prediction models, based on a library of high-quality previous plans, PB-AIO requires the creation of an optimization template where the right parameters (clinical requests and other planning parameters) must be set, and MCO needs a careful definition of planning constraints and objectives functions, which has to be supplemented with objectives priorities and goal values in case of wish-list driven automated MCO.
Since a suboptimal configuration translates into a plan quality systematically lower than feasible, a validation of any AP approach prior to clinical introduction is mandatory, comparing automatically generated plans with high-quality manually generated plans.
Typically, AP configurations are for specific delivery approaches, clinical sites, clinical protocols (doses to targets and OARs) and TPS versions, and the actual manual planning quality is used as a starting point. But radiation therapy is a rapidly evolving field, where new prescription strategies, new fractionation schemes, new volume definitions, new dose constraints and new delivery approaches are regularly introduced. Moreover, changes in treatment planning systems, e.g. implementation of new optimizers, new cost functions or new configurations may give rise to opportunities for enhancing quality of automatically generated plans. Also advances in manual plan quality may be a stimulus to further enhance AP plan quality.  In this presentation we will discuss continuing efforts to keep automated planning up to date.