A finite-horizon Markov decision process model for cancer chemotherapy treatment planning: an application to sequential treatment decision making in clinical trials

Bazrafshan, Nazila; Lotfi, Mohammad Mehdi

doi:10.1007/s10479-020-03706-5

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…Treatment modalities with a lower risk 3. No treatment Function of side effect and tumor progression Optimize the treatment decision No Bazrafshan and Lotfi (2020) [ 35 ] Finite (after every 8 treatment cycles) Gastroesophageal cancer 3 states (low toxicity, moderate toxicity, high toxicity) Sequential treatment (5 different types of chemotherapy/5 chemotherapy treatment strategies) Expected costs Optimize treatment decision Yes Alagoz et al (2004) [ 27 ] Infinite (unclear) Severe liver failure (in need of transplantation) Unclear number of states (defined by the patient’s end-stage liver score) 1. Transplant 2.…”

Section: Resultsmentioning

confidence: 99%

“…The MDP approach has been used to determine the optimal sequence of chemotherapy and radiation therapy [ 35 , 39 , 42 , 43 ] and to select the appropriate drugs for anemia [ 47 ], tuberculosis [ 46 ], atherosclerotic cardiovascular disease [ 45 ], and hepatitis C [ 44 ]. In studies by Meng et al [ 32 ], Mason et al [ 33 ], and Shifrin and Siegelmann [ 41 ], MDP is applied to optimize the management of diabetes medication for glycemic control.…”

Section: Resultsmentioning

confidence: 99%

“…Researchers have applied MDP models to optimize initial treatment selection [25,26] and the timing of transplantation [27,28], to compare the effectiveness of different combinations of treatment [29], to optimize screening policy [30], and to prevent disease-related complications [31]. However, 16 studies concern the optimization of treatment decisions [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Five studies use the MDP to optimize treatment decisions for cancer [30,35,39,42,43], five focus on optimizing the treatment of diabetes mellitus [31][32][33][34]41], and the remaining (N = 13) studies are concerned with liver diseases [27,28], high blood pressure/hypertension [37,40], hepatitis C [44], atherosclerotic cardiovascular disease [45], ischemic heart disease [29,36], atrial fibrillation [38], anemia [47], tuberculosis [46...…”

Section: Overview Of Existing Applications Of Mdp In Treatment Of Dis...mentioning

confidence: 99%

“…However, 16 studies concern the optimization of treatment decisions [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Five studies use the MDP to optimize treatment decisions for cancer [30,35,39,42,43], five focus on optimizing the treatment of diabetes mellitus [31][32][33][34]41], and the remaining (N = 13) studies are concerned with liver diseases [27,28], high blood pressure/hypertension [37,40], hepatitis C [44], atherosclerotic cardiovascular disease [45], ischemic heart disease [29,36], atrial fibrillation [38], anemia [47], tuberculosis [46], aneurysms [25], and stroke [26].…”

Section: Overview Of Existing Applications Of Mdp In Treatment Of Dis...mentioning

confidence: 99%

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A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

Jörg

et al. 2022

PharmacoEconomics

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The most appropriate next step in depression treatment after the initial treatment fails is unclear. This study explores the suitability of the Markov decision process for optimizing sequential treatment decisions for depression. We conducted a formal comparison of a Markov decision process approach and mainstream state-transition models as used in health economic decision analysis to clarify differences in the model structure. We performed two reviews: the first to identify existing applications of the Markov decision process in the field of healthcare and the second to identify existing health economic models for depression. We then illustrated the application of a Markov decision process by reformulating an existing health economic model. This provided input for discussing the suitability of a Markov decision process for solving sequential treatment decisions in depression. The Markov decision process and state-transition models differed in terms of flexibility in modeling actions and rewards. In all, 23 applications of a Markov decision process within the context of somatic disease were included, 16 of which concerned sequential treatment decisions. Most existing health economic models relating to depression have a state-transition structure. The example application replicated the health economic model and enabled additional capacity to make dynamic comparisons of more interventions over time than was possible with traditional state-transition models. Markov decision processes have been successfully applied to address sequential treatment-decision problems, although the results have been published mostly in economics journals that are not related to healthcare. One advantage of a Markov decision process compared with state-transition models is that it allows extended action space: the possibility of making dynamic comparisons of different treatments over time. Within the context of depression, although existing state-transition models are too basic to evaluate sequential treatment decisions, the assumptions of a Markov decision process could be satisfied. The Markov decision process could therefore serve as a powerful model for optimizing sequential treatment in depression. This would require a sufficiently elaborate state-transition model at the cohort or patient level.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Overview Of Existing Applications Of Mdp In Treatment Of Dis...mentioning

confidence: 99%

Section: Overview Of Existing Applications Of Mdp In Treatment Of Dis...mentioning

confidence: 99%

See 2 more Smart Citations

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

Jörg

et al. 2022

PharmacoEconomics

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“…They categorized the treatment options into three types in terms of tumor reduction, risk to normal tissue, and repeatability. More recently, Bazrafshan et al [12] used MDP framework to propose a model for optimizing cancer chemotherapy in clinical trials which captured the treatment dynamics. However, the authors only considered the toxicity level of the patient.…”

Section: Introductionmentioning

confidence: 99%

A Markov Decision Process Framework for Optimal Cancer Chemotherapy Dose Selection

Adeyiola

Rabia

Elsaid

et al. 2022

J. Phys.: Conf. Ser.

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Chemotherapy is arguably the most effective treatment for treating cancer. However, it triggers toxic effects on patients due to its interaction with normal cells. In this paper, we propose a finite-horizon Markov decision process framework for optimal chemotherapy dose selection during cancer treatment. In contrast to many other research works, our model accommodates all possible patient’s clinical health states that could be considered during treatment. We study the use of our model by applying it on Wilms tumor. We explore three different cases of varying transition probabilities and rewards. Our numerical results agree with clinical intuition and demonstrate the potential applicability of our model.

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Optimized drug regimen and chemotherapy scheduling for cancer treatment using swarm intelligence

et al. 2021

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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

Cited by 9 publications

References 23 publications

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

A Markov Decision Process Framework for Optimal Cancer Chemotherapy Dose Selection

Optimized drug regimen and chemotherapy scheduling for cancer treatment using swarm intelligence

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