# Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1807.06489 · HTML: https://mlsanity.com/paper/1807.06489/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 11 of 11)

- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [DiffDP: Radiotherapy Dose Prediction via a Diffusion Model](https://mlsanity.com/paper/2307.09794/) (2023)
- [Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding](https://mlsanity.com/paper/2311.02991/) (2023)
- [Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans](https://mlsanity.com/paper/2206.02912/) (2022)
- [Improved α-GAN architecture for generating 3D connected volumes with an application to radiosurgery treatment planning](https://mlsanity.com/paper/2207.11223/) (2022)
- [SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction Based on Multi-Scale Fusion of Anatomical Structures, Guided by SwinTransformer and Projector](https://mlsanity.com/paper/2312.06187/) (2023)
- [Triplet-constraint Transformer with Multi-scale Refinement for Dose Prediction in Radiotherapy](https://mlsanity.com/paper/2402.04566/) (2024)
- [Site-Agnostic 3D Dose Distribution Prediction with Deep Learning Neural Networks](https://mlsanity.com/paper/2106.07825/) (2021)
- [Polymerized Feature-based Domain Adaptation for Cervical Cancer Dose Map Prediction](https://mlsanity.com/paper/2308.10142/) (2023)
- [DoseBridge: Denoising Diffusion Bridge Model for Dose Prediction in Lung Intensity-Modulated Proton Therapy](https://mlsanity.com/paper/2608.10173/) (2026)
- [Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning](https://mlsanity.com/paper/2608.26901/) (2026)

## Abstract (arXiv)

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.