Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling

Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling

<b>Objective:</b> Radiotherapy is a primary method for cancer treatment, wherein radiation doses are divided into multiple sessions or fractions to effectively target tumors and minimize damage to surrounding tissues. <b>Methods:</b> In this study, we leverage reinforcement l...

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Bibliographic Details
Main Authors: Mélanie Ghislain, Florian Martin, Manon Dausort, Damien Dasnoy-Sumell, Ana Maria Barragan Montero, Benoît Macq
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Biomedicines
Subjects:
reinforcement learning
radiotherapy treatment planning
tumor growth simulation
cancer complication
Online Access:https://www.mdpi.com/2227-9059/13/6/1367
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