# Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations

arXiv: https://arxiv.org/abs/1812.06934 · HTML: https://mlsanity.com/paper/1812.06934/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2018.

## Built on by (most cited first, 12 of 12)

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- [LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction](https://mlsanity.com/paper/2106.06733/) (2021)
- [DeepDoseNet: A Deep Learning model for 3D Dose Prediction in Radiation Therapy](https://mlsanity.com/paper/2111.00077/) (2021)
- [Deep Evidential Learning for Radiotherapy Dose Prediction](https://mlsanity.com/paper/2404.17126/) (2024)
- [Learning the Physics of Particle Transport via Transformers](https://mlsanity.com/paper/2109.03951/) (2021)
- [Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability](https://mlsanity.com/paper/2312.00975/) (2023)
- [Latent Spaces Enable Transformer-Based Dose Prediction in Complex Radiotherapy Plans](https://mlsanity.com/paper/2407.08650/) (2024)
- [Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study](https://mlsanity.com/paper/2605.09622/) (2026)
- [Deep Learning 3D Dose Prediction for Conventional Lung IMRT Using Consistent/Unbiased Automated Plans](https://mlsanity.com/paper/2106.03705/) (2021)
- [Site-Agnostic 3D Dose Distribution Prediction with Deep Learning Neural Networks](https://mlsanity.com/paper/2106.07825/) (2021)
- [Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients](https://mlsanity.com/paper/2310.19686/) (2023)

## Abstract (arXiv)

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings.