Human-like automated radiotherapy treatment planning via imitation learning

PROJECT SUMMARY Radiation therapy is one of the major approaches for cancer treatment. Treatment planning, the process of designing the optimal treatment plan for each patient, is one of the most critical steps. If a treatment is poorly designed, a satisfactory outcome cannot be achieved, regardless of the quality of other treatment steps.

Treatment planning in modern radiotherapy is formulated as a mathematical optimization problem defined by a set of hyperparameters. While there exists several quantifiable metrics to quantify plan quality and guide the

planning process, these are simplified representations that cannot fully describe the physician’s intent. In addition, these metrics only measure plan quality from a population-based perspective, and cannot guide treatment planning to achieve the patient-specific best treatment plans. Hence, the best physician-preferred solution often

sits in a gray area, only achievable by an extensive trial-and-error hyperparameter tuning process and interactions between the planner and physician. Consequently, planning time can take up to a week for complex cases and plan quality may be poor, if the planner is inexperienced and/or under heavy time constraints. These

consequences substantially deteriorate treatment outcomes, as having been clearly demonstrated in clinical studies. Recently, the advancement in artificial intelligence (AI), particularly in imitation learning allows human- like decision making by observing a human expert’s actions and internally building its own decision-making

system. In response to PAR-18-530, the goal of this project is to develop and translate an AI planner that mimics human experts’ behavior to generate a high quality plan. The AI planner will not replace human planners. Instead, the AI plan will be used as a starting point in the current planning process to improve plan quality and planning

efficiency. The human planner’s actions on further plan improvement can feed back to the AI planner through continuous learning for its continuous evolution. We will pursue this goal using prostate cancer as the test bed through an academic-industrial partnership, jointing strong research and clinical expertise at UT Southwestern

Medical Center with extensive commercial product development experience at Varian Medical Systems Inc. The following specific aims are defined. Aim 1: Model and algorithm development. We will collect experts’ behavior data in routine treatment planning and train the AI planner. Aim 2: System validation and translation. We will

integrate the AI planner into Varian Eclipse treatment planning system and validate the system in a clinically realistic setting. The innovations include the use of a state-of-the-art AI imitation learning algorithm to solve a clinically important problem, the novel technological capabilities enabled by the developed system, as well as

coherent translation activities to deliver new capabilities to end users. Deliverability is ensured by extensive preliminary studies and the partnership integrating complementary expertise and resources. Clinical translation of the AI planner will bring substantial impacts to radiotherapy by providing high-quality and efficient treatment

planning to benefit patients, especially those in resource-limited regions.