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Clinical Outcomes Modeling

M Guerrero1*, V Moiseenko2*, (1) University of Maryland School of Medicine, Baltimore, MD, (2) UC San Diego, La Jolla, CA



Presentations

(Sunday, 7/12/2020) 4:30 PM - 5:30 PM [Eastern Time (GMT-4)]

Room: Track 5

In an era of increased treatment individualization and personalized medicine, accurate predictive models of dose-response are needed to guide treatment decisions and determine treatment effectiveness. In this session, the speakers will provide an overview of radiobiological models used to predict clinical outcomes in radiation oncology. Strategies to enhance tumor control and reduce the risk of acute and late normal tissue effects will be discussed. An overview of key biological and physical factors correlated with treatment outcomes will be presented. Tumor Control Probability (TCP) models incorporating the 5 R’s of Radiobiology will be reviewed, including methodologies used to analyze patient datasets and derive radiobiological parameters. Examples of changes in clinical practice based on TCP modeling, such as hypofractionation for prostate cancer and stereotactic body radiation therapy (SBRT) for lung cancer, will be highlighted. Strategies to address the challenge of using outcome models for biologically adaptive therapy based on advanced imaging technologies, e.g., dose painting and dose escalation to compensate for tumor hypoxia in head and neck cancers, will be reviewed. Traditional normal tissue complication probability (NTCP) models describing dose-volume-response, and methods for fitting models to the clinical data will be presented. Approaches to account for regional effects, with emphasis on late response, will be discussed. Specifically, quantitative methods using functional imaging to guide treatment planning will be addressed. Finally, the use of advanced imaging, for example diffusion tensor imaging to identify structures which may govern observed response, will also be discussed.

Learning Objectives:
1. To review current research on radiobiological models used to predict clinical outcomes in radiation oncology
2. To move towards a practical understanding and implementation of biological models of tumor and normal tissue response in radiation therapy
3. To discuss strategies to enhance tumor control and reduce the risk of acute and late normal tissue effects

Handouts

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