Knowledge-based planning (KBP) is an automated approach to radiation therapy treatment planning that involves predicting desirable treatment plans before they are then corrected to deliverable ones. We propose a generative adversarial network (GAN) approach for predicting desirable 3D dose distributions that eschews the previous paradigms of site-specific feature engineering and predicting low-dimensional representations of the plan. Experiments on a dataset of oropharyngeal cancer patients show that our approach significantly outperforms previous methods on several clinical satisfaction criteria and similarity metrics.
@article{arxiv.1807.06489,
title = {Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks},
author = {Rafid Mahmood and Aaron Babier and Andrea McNiven and Adam Diamant and Timothy C. Y. Chan},
journal= {arXiv preprint arXiv:1807.06489},
year = {2018}
}
Comments
15 pages. Accepted for publication in PMLR. Presented at Machine Learning for Health Care