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Tytuł:
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Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide.
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Autorzy:
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Ebrahimi Zade A; Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran.
Shahabi Haghighi S; Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran. Electronic address: .
Soltani M; Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1969764499, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
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Źródło:
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Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2020 Sep; Vol. 193, pp. 105443. Date of Electronic Publication: 2020 Mar 19.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: Limerick : Elsevier Scientific Publishers
Original Publication: Amsterdam : Elsevier Science Publishers, c1984-
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MeSH Terms:
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Antineoplastic Agents*/therapeutic use
Brain Neoplasms*/drug therapy
Glioblastoma*/drug therapy
Adult ; Antineoplastic Agents, Alkylating/therapeutic use ; Cell Line, Tumor ; Humans ; Temozolomide/therapeutic use
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Contributed Indexing:
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Keywords: Glioblastoma multiforme; Multi scale modeling; Reinforcement learning; Temozolomide; Treatment scheduling
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Substance Nomenclature:
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0 (Antineoplastic Agents)
0 (Antineoplastic Agents, Alkylating)
YF1K15M17Y (Temozolomide)
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Entry Date(s):
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Date Created: 20200421 Date Completed: 20210514 Latest Revision: 20210514
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Update Code:
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20240104
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DOI:
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10.1016/j.cmpb.2020.105443
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PMID:
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32311510
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Background: Glioblastoma multiforme (GBM) is the most frequent primary brain tumor in adults and Temozolomide (TMZ) is an effective chemotherapeutic agent for its treatment. In Silico models of GBM growth provide an appropriate foundation for analysis and comparison of different regimens. We propose a mathematical frame for patient specific design of optimal chemotherapy regimens for GBM patients.
Methods: The proposed frame includes online interaction of a virtual GBM with an optimizing agent. Spatiotemporal dynamics of GBM growth and its response to TMZ are simulated with a three dimensional hybrid cellular automaton. Q learning is tailored to the virtual GBM for treatment optimization aimed at minimizing tumor size at the end of treatment course. Q learning consists of a learning agent that interacts with the virtual GBM. System state is affected by the agent decisions and the obtained rewards guide Q learning to the optimal schedule.
Results: Computational results confirm that the optimal chemotherapy schedule depends on some patient specific parameters including body weight, tumor size and its position in the brain. Furthermore, the algorithm is used for scheduling 2100 mg of TMZ on a virtual GBM and the obtained schedule is to administer150 mg of TMZ every other day. The obtained schedule is compared to the standard 7/14 regimen and the results show that it is superior to the 7/14 regimen in minimizing tumor size.
Conclusion: The proposed frame is an appropriate decision support system for patient specific design of TMZ administration regimens on GBM patients. Also, since the obtained optimal schedule outperforms the standard 7/14 regimen, it is worthy of further clinical testing.
(Copyright © 2020. Published by Elsevier B.V.)
