Encrypted login | home

Program Information

Decision Support for Radiation Therapy

F Yin
no image available
Y Ge

Y Xiao

Q Wu

M Phillips

F Yin1*, (1) Duke University Medical Center, Durham, NC

WE-C-213CD-1 Wednesday 10:30:00 AM - 12:30:00 PM Room: 213CD

Informatics in radiation therapy is less understood. Decision support for, or knowledge-guided, radiation therapy, is a major component of informatics, where the data is mined, modeled and turned into computable knowledge. It is critically important for radiation therapy as it employs highly complex procedures, such as IMRT and IGRT, where radiation related normal tissue/organ damages and clinical outcomes require delicate balance.

Decision support can be provided in passive, active, and cooperative modes. The passive mode focuses on improved presentation of available data and knowledge to minimize cognitive burden in decision making. The active mode applies machine learning algorithms, such as support vector machines, to generate decision suggestions based on existing knowledge. The cooperative mode allows users to interact with the decision support system to iteratively reach an optimal decision. Decision support for the execution of Clinical Practice Guidelines (CPG) is another important area in which significant improvement can be achieved by helping physicians following best clinical practices specified in the guidelines.

In radiation therapy, significant data, algorithms, knowledge, and guidelines have been developed in the past two decades. Various modes of decision support systems have been proposed in recent years that incorporate different data, optimization algorithms, knowledge representations, and machine learning algorithms.

In this session, we will first review concepts and systems of clinical decision support in general; followed by specific applications of decision support in radiation therapy. The basic principles of making decisions when the outcomes of any available actions are uncertain are discussed. Methods for applying these principles in medical environments are described. We will conclude our discussion with a number of state-of-the-art techniques for decision support in radiation treatment planning and quality assurance.

Learning Objectives:
1. To discuss the need for decision support in health care and specifically in radiation therapy
2. To review major concepts and approaches in clinical decision support systems
3. To describe example systems for decision support in radiation therapy
4. To present a number of new techniques for decision support in intensity modulated radiation treatment planning

Contact Email