Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks
This work addresses the problem of automating treatment planning in radiation therapy for cancer patients, representing an incremental improvement over existing knowledge-based planning methods.
The paper tackled automated radiation therapy treatment planning by predicting 3D dose distributions using a generative adversarial network (GAN), and it significantly outperformed previous methods on clinical satisfaction criteria and similarity metrics for oropharyngeal cancer patients.
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.