SU-D-BRB-06: Toward Big-Data Driven Treatment Planning: VMAT/IMRT Inverse Planning Autopiloted by Population-Based Prior Data

Authors


Abstract

Purpose:

Current treatment planning remains a costly and labor intensive procedure and requires multiple trial-and-error adjustments of system parameters such as weighting factors and prescriptions. The purpose of this work is to develop an autonomous treatment planning technique with effective use of population-based prior knowledge.

Methods:

Our autopiloted planning tool consists of three major components: (i) a commercial treatment planning system (TPS) (EclipseTM, Varian Medical Systems, CA); (ii) a statistical formulation of prior knowledge, which takes an assemble of prior treatment plans similar to the patient under planning and provides a priori variation range of final solution with an assigned preference level; and (iii) a decision-making or outer-loop optimization that assesses plan generated by Eclipse and drives the search toward a solution consistent with population-based prior knowledge. To query and interact with TPS, Microsoft (MS) Visual Studio Coded UI is applied to record some common planner-TPS interactions as subroutines. These subroutines are called back in the autopilot program written in C# to navigate through the solution space with an iterative algorithm. The utility of the approach is demonstrated by using a prostate IMRT case and three head and neck VMAT case.

Results:

An autonomous inverse planning formalism of using population-based prior knowledge for autopiloting the VMAT/IMRT treatment planning process is implemented successfully in platform of a commercial TPS. The process mimics the decision-making process of autopiloted automobile and provides a clinically sensible treatment plan automatically, thus effectively eliminating the tedious manual trial-and-errors process of treatment planning. It is found that the prostate and head and neck treatment plans compare favorably with that used for patients’ actual treatments.

Conclusion:

Clinical inverse treatment planning process can be autopiloted effectively with the use of population-based prior knowledge. The strategy lays foundation for future development of big data-driven inverse planning of various disease sites.

Research grant and speak honoraria from Varian Medical Systems

Ancillary