A finite-horizon Markov decision process model for cancer chemotherapy treatment planning: an application to sequential
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A finite‑horizon Markov decision process model for cancer chemotherapy treatment planning: an application to sequential treatment decision making in clinical trials Nazila Bazrafshan1 · M. M. Lotfi2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Cancer is one of the major diseases that seriously threaten the human life. Increasing interest in cancer treatment strategies for chemotherapy treatment planning and optimal drug administration has created new applications for mathematical modeling. In this paper, we develop a finite-horizon Markov decision process (MDP) model for cancer chemotherapy treatment planning that could advise selection of the optimal policy for the chemotherapy regimen according to the patient’s condition. The proposed model uses a finite action space of optimal cancer chemotherapy regimens for gastric and gastroesophageal cancers resulted from the proposed optimization model and a finite state space of patients’ toxicity levels. Results show that the proposed approach yields the optimal sequence of gastric and gastroesophageal cancer chemotherapy treatment regimens for a period of chemotherapy treatment which makes possible designing clinical trials for sequential treatments. Keywords Cancer · Clinical trial · Combination chemotherapy · Sequential treatment decision making · Markov decision process · Survival
1 Introduction and literature review Cancer is the general term for a group of more than 100 diseases. Although there are many types of cancer, all of them start when abnormal cells are grown and reproduced uncontrollably. The cancerous cells may invade surrounding tissue and metastasize to whole body. Untreated cancers can cause serious illness and death. A significant portion of cancers can be cured by surgery, radiotherapy or chemotherapy, especially if they are detected in early stages (American Cancer Society 2016; World Health Organization
* M. M. Lotfi [email protected] Nazila Bazrafshan [email protected] 1
Department of Systems Science and Industrial Engineering, Binghamton University, State University of New York, Binghamton, NY, USA
2
Department of Industrial Engineering, Yazd University, Yazd, Iran
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Vol.:(0123456789)
Annals of Operations Research
2016). Chemotherapy is a common and powerful treatment method in which anticancer drugs are used to destroy cancerous cells. Depending upon the type of cancer, the patient may be treated with a single medicine (monotherapy) or a combination of medicines (combination therapy). Today, most successful chemotherapy treatments for advanced cancers use multiple drugs simultaneously (Macmillan Cancer Support 2016; Kahruman et al. 2012). Even though chemotherapy is often effective for treating cancer or relieving its symptoms, it causes some side effects. That is because the medicines used in chemotherapy may not distinguish between fast-growing and normal cancerous cells (Choices NHS 2016; Dua et al. 2008). Therefore, advising the best chemotherapy plans which result in both maximum reduct
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