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AuthorYazdjerdi, Parisa
AuthorMeskin, Nader
AuthorAl-Naemi, Mohammad
AuthorAl Moustafa, Ala-Eddin
AuthorKovács, Levente
Available date2019-10-16T06:03:47Z
Publication Date2019-05-01
Publication NameComputer Methods and Programs in Biomedicineen_US
Identifierhttp://dx.doi.org/10.1016/j.cmpb.2019.03.004
Citationisa Yazdjerdi, Nader Meskin, Mohammad Al-Naemi,Ala-Eddin Al Moustafa, Levente Kov ́acs, Reinforcement Learning-based Control of Tumor Growthunder Anti-angiogenic Therapy,Computer Methods and Programs in Biomedicine(2019), doi:https://doi.org/10.1016/j.cmpb.2019.03.004
ISSN0169-2607
URIhttp://hdl.handle.net/10576/12124
AbstractIn recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment with minimum side effects. Anti-angiogenic is a molecular targeted therapy which can be coupled with chemotherapy and radiotherapy. Although this method does not eliminate the whole tumor, but it can keep the tumor size in a given state by preventing the formation of new blood vessels. In this paper, a novel model-free method based on reinforcement learning (RL) framework is used to design a closed-loop control of anti-angiogenic drug dosing administration. A Q-learning algorithm is developed for the drug dosing closed-loop control. This controller is designed using two different values of the maximum drug dosage to reduce the tumor volume up to a desired value. The mathematical model of tumor growth under anti-angiogenic inhibitor is used to simulate a real patient. The effectiveness of the proposed method is shown through in silico simulation and its robustness to patient parameters variation is demonstrated. It is demonstrated that the tumor reaches its minimal volume in 84 days with maximum drug inlet of 30 mg/kg/day. Also, it is shown that the designed controller is robust with respect to  ± 20% of tumor growth parameters changes. The proposed closed-loop reinforcement learning-based controller for cancer treatment using anti-angiogenic inhibitor provides an effective and novel result such that with a clinically valid and safe dosage of drug, the volume reduces up to 1mm in a reasonable short period compared to the literature.
Languageen
PublisherElsevier
SubjectAngiogenesis
SubjectAnti-angiogenic therapy
SubjectDrug administration control
SubjectReinforcement learning
TitleReinforcement learning-based control of tumor growth under anti-angiogenic therapy.
TypeArticle
Pagination15-26
Volume Number173
dc.identifier.essn 1872-7565


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