Simulation-based optimization of radiotherapy: agent-based modeling and reinforcement learning

DOI10.1016/j.matcom.2016.05.008OpenAlexW2424760598MaRDI QIDQ2229008

Madjid Soltani, Hamidreza Shahabi Haghighi, Ammar Jalalimanesh, Abbas Ahmadi

Publication date: 19 February 2021

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.matcom.2016.05.008

zbMATH Keywords

reinforcement learningradiotherapyagent-based modelingQ-learningcancer treatment

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Classical thermodynamics, heat transfer (80-XX)

Related Items (3)

Can we replicate real human behaviour using artificial neural networks? ⋮ Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning ⋮ Optimization of antitumor radiotherapy fractionation via mathematical modeling with account of 4 R's of radiobiology

Uses Software

• NetLogo
• RNetLogo

Cites Work

• Tumour-host dynamics under radiotherapy
• Modelling the effects of cell-cycle heterogeneity on the response of a solid tumour to chemotherapy: biological insights from a hybrid multiscale cellular automaton model
• Q-learning for estimating optimal dynamic treatment rules from observational data
• The Importance of Spatial Distribution of Stemness and Proliferation State in Determining Tumor Radioresponse
• Optimal Dynamic Treatment Regimes

This page was built for publication: Simulation-based optimization of radiotherapy: agent-based modeling and reinforcement learning