Evaluation of deep learning-based automated radiotherapy planning for early-stage lung cancer using SBRT-VMAT: A comparison with manual planning.

ABSTRACT:

Purpose

This study evaluates the feasibility of deep learning (DL)-based automated SBRT-VMAT planning for lung cancer.

Methods

We developed a DL-based dose distribution prediction model (installed in the RatoGuide prototype, AiRato. Inc.), which was trained using manually optimized and clinically approved plans for 124 cases of early-stage lung cancer (62 central and 62 peripheral cases). The test data consisted of eight treatment plans for each case. Automated plans for the test data were created as follows: (i) predicting dose distributions from CT images with target and organs-at-risk contours using RatoGuide, (ii) converting the predicted dose distributions into ring-shaped dose structures and importing them into RayStation software, and (iii) generating deliverable dose

SUBMITTER: Nemoto H 

PROVIDER: S-EPMC12521067 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature