A deep learning model to predict dose distributions for breast cancer radiotherapy.

ABSTRACT:

Purpose

In this work, we propose to develop a 3D U-Net-based deep learning model that accurately predicts the dose distribution for breast cancer radiotherapy.

Methods

This study included 176 breast cancer patients, divided into training, validating and testing sets. A deep learning model based on the 3D U-Net architecture was developed to predict dose distribution, which employed a double encoder combination attention (DECA) module, a cross stage partial + Resnet + Attention (CRA) module, a difficulty perception and a critical regions loss. The performance and generalization ability of this model were evaluated by the voxel mean absolute error (MAE), several clinically relevant dosimetric indexes and 3D gamma passing rates.

Results

Our model accurately predicted the

SUBMITTER: Hou X 

PROVIDER: S-EPMC11822156 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature