Fully automated intensity-modulated radiotherapy plans for rectal cancer based on deep learning predictions of three-dimensional dose distributions.

ABSTRACT:

Background

Designing intensity-modulated radiotherapy (IMRT) plans for rectal cancer is complex and time-consuming. We used a three-dimensional (3D) multitask training U-Net (3D MT-U-Net) deep learning (DL) model to accurately predict radiotherapy dose distributions for rectal cancer. We aimed to achieve fully automated IMRT plans with improved efficiency and quality.

Methods

We developed a 3D MT-U-Net model that precisely captured dose distribution characteristics through pretraining and a multitask learning mechanism. In the multitask learning module, we additionally introduced the learning of gradient maps and isodose line maps to enhance the network's ability to extract semantic information from dose distributions. The patients were divided into a training set (n=99), an

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12682532 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature