Beam field guided diffusion model for liver cancer radiotherapy dose distribution prediction

Abstract

Background

Deep learning has been widely applied to the design of cancer radiotherapy treatment planning for dose distribution prediction. However, the significant variability in tumor size, quantity, and location poses substantial challenges for accurate dose distribution prediction in liver cancer radiotherapy.

Purpose

Given that the clinical effectiveness and accuracy of the predicted dose distribution directly impact the quality of treatment plans generated by automatic radiotherapy planning methods, this study aims to develop a novel and precise dose prediction method based on diffusion models.

Methods

We propose a beam field (BF) guided diffusion model (BeamDiff) consisting of a forward and a reverse process for liver cancer radiotherapy dose distribution prediction. In the forward process, noise is progressively added to the actual dose distribution map until it transforms into a standard Gaussian noise map. In the reverse process, a noise predictor is used to estimate the noise and iteratively generate the desired dose distribution map. To effectively leverage patient-specific clinical features, we design a multi-branch hybrid encoder to extract features from BF and clinical structural information, with their relationships captured by a designed multi-condition aggregation module (MAM). Given that our inputs consist solely of 2D slices, which inherently lack inter-slice dependencies and similarity features, we integrate the multi-head attention (MHA) module into the encoder to re-establish connections between slices. In the decoder, we design an asymmetric fusion module (AFM) to integrate high-level feature maps from the encoder with low-level ones from the decoder, mitigating information loss caused by downsampling while preserving fine details and contextual information.

Results

We evaluate the proposed method on a clinical liver cancer radiotherapy dataset. In terms of prediction accuracy, our model achieves an average Dose score of 1.27 Gy and a DVH score of 0.28 Gy. The mean absolute error (MAE) is 1.97 Gy for the planning target volume (PTV), 2.21 Gy for the liver, 1.14 Gy for the spinal cord, and 1.16 Gy for the stomach. Regarding clinical effectiveness, the predicted results of our method are the closest to meeting clinical requirements across the evaluated metrics.

Conclusions

We develop a method specifically tailored for liver cancer radiotherapy dose prediction. The proposed model demonstrates competitive performance in terms of both prediction accuracy and clinical effectiveness. These results suggest that the method has considerable potential to enhance the efficiency of the radiotherapy workflow.