Automatic radiotherapy planning for deliverable plans using deep learning dose prediction and dose rings optimization in cervical cancer.

ABSTRACT:

Background

Automatic radiotherapy (RT) planning based on deep learning (DL) has been extensively researched. However, it is challenging to import the predicted dose distribution into mainstream treatment planning systems (TPSs) and generate clinically deliverable plans.

Purpose

To investigate the feasibility and accuracy of an automatic volumetric modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT) planning method for generation of universally deliverable plans based on DL dose prediction and dose rings optimization.

Methods

First, dose distributions were predicted using a three-dimensional (3D) Fusion Residual Unet (F-ResUnet) DL network with data from two hospitals, which included 230 and 210 gynecological cancer (GC) patients underwent VMA

SUBMITTER: Huang W 

PROVIDER: S-EPMC12611595 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature