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

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

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 combi...

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Main Authors: Jan-Hendrik Bolten, David Neugebauer, Christoph Grott, Fabian Weykamp, Jonas Ristau, Stephan Mende, Elisabetta Sandrini, Eva Meixner, Victoria Navarro Aznar, Eric Tonndorf-Martini, Kai Schubert, Christiane Steidel, Lars Wessel, Jürgen Debus, Jakob Liermann
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:Clinical and Translational Radiation Oncology
Subjects:
Artificial intelligence
Fully automated radiation treatment planning
Prostate cancer
Auto segmentation in radiation oncology
Investigator-dependant variability
One-click radiation treatment planning
Online Access:http://www.sciencedirect.com/science/article/pii/S2405630825000230
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