A fully automated machine-learning-based workflow for radiation treatment planning in prostate cancer.

ABSTRACT:

Introduction

The integration of artificial intelligence into radiotherapy planning for prostate cancer has demonstrated promise in enhancing efficiency and consistency. In this study, we assess the clinical feasibility of a fully automated machine learning (ML)-based "one-click" workflow that combines ML-based segmentation and treatment planning. The proposed workflow was designed to create a clinically acceptable radiotherapy plan within the inter-observer variation of conventional plans.

Methods

We evaluated the fully-automated workflow on five low-risk prostate cancer patients treated with external beam radiotherapy and compared the results with conventional optimized and inverse planned radiotherapy plans based on the contours of six different experienced radiation oncol

SUBMITTER: Bolten JH 

PROVIDER: S-EPMC11871478 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature