SU-E-T-259: A Statistical and Machine Learning-Based Tool for Modeling and Visualization of Radiotherapy Treatment Outcomes.

@article{Oh2012SUET259AS,
  title={SU-E-T-259: A Statistical and Machine Learning-Based Tool for Modeling and Visualization of Radiotherapy Treatment Outcomes.},
  author={Jung Hun Oh and Ya Wang and Aditya P. Apte and Joseph O. Deasy},
  journal={Medical physics},
  year={2012},
  volume={39 6Part13},
  pages={
          3763
        }
}
PURPOSE Effective radiotherapy outcomes modeling could provide physicians with better understanding of the underlying disease mechanism, enabling to early predict outcomes and ultimately allowing for individualizing treatment for patients at high risk. This requires not only sophisticated statistical methods, but user-friendly visualization and data analysis tools. Unfortunately, few tools are available to support these requirements in radiotherapy community. METHODS Our group has developed… CONTINUE READING