# A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning

arXiv: https://arxiv.org/abs/1709.09233 · HTML: https://mlsanity.com/paper/1709.09233/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [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)
- [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)
- [FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose Prediction in Radiotherapy](https://mlsanity.com/paper/2410.07876/) (2024)

## Abstract (arXiv)

With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients, where the average Dice similarity coefficient is 0.91 when comparing the predicted vs. true isodose volumes between 0% and 100% of the prescription dose. The average value of the absolute differences in [max, mean] dose is found to be under 5% of the prescription dose, specifically for each structure is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%, 1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%, 1.62%](Rectum) of the prescription dose. We thus managed to map a desired radiation dose distribution from a patient's PTV and OAR contours. As an additional advantage, relatively little data was used in the techniques and models described in this paper.