Encrypted login | home

Program Information

Advanced Planning Tools


Q Wu

L Olsen

P Xia




Q Wu1*, L Olsen2*, P Xia3*, (1) Duke University Medical Center, Durham, NC, (2) Washington University School of Medicine, St. Louis, MO, (3) The Cleveland Clinic Foundation, Cleveland, OH

Presentations

TU-A-BRD-0 (Tuesday, March 10, 2015) 7:30 AM - 9:30 AM Room: Ballroom D


Treatment planning is an indispensable process for radiation therapy treatment but there can be significant variability in plan quality. Recently, there is rising interest in designing tools to improve plan quality consistency and plan efficiency by using more automation and less dependence on a planner’s experience. This session will introduce both knowledge-based planning and auto-planning techniques. Knowledge-based planning extracts past clinical planning experience to build mathematical models. We will describe the fundamental physics and informatics tools and discuss the importance of model training and validation. The process and benefits of knowledge-based treatment planning will be shown with clinical examples. Auto-planning incorporates many manual planning steps in an automatic routine to achieve the established dosimetric objectives for each specific cancer type. The advantages of this technique will also be discussed.

Learning Objectives:
1. To understand knowledge guided planning background: including extracting human planning knowledge into features, machine learning and modeling techniques
2. To learn how to implement knowledge-based planning techniques into clinical practice and to recognize the importance of model training and validation: including data size, data quality, modeling parameter statistics, and outlier analysis
3. To describe the underlining principle of the auto-planning module in a planning system
4. To learn how to use auto-planning tools and knowledge-based planning tools to improve plan quality and efficiency through clinical examples


Handouts


Contact Email: