Reinforcement learning-based control of tumor growth under anti-angiogenic therapy

Yazdjerdi, Parisa; Meskin, Nader; Al-Naemi, M.; Moustafa, Ala‐Eddin Al; Kovács, Levente

doi:10.1016/j.cmpb.2019.03.004

Cited by 30 publications

(20 citation statements)

References 15 publications

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“…Such models are promising for the development of mathematical models of cancer mechanisms and treatment (Figure 1). Another advantage of representing cancer mechanisms in terms of mathematical models is that it enables easy design and implementation of algorithms for optimizing drug dose and effective treatment schedules [35,186]. Even though many such optimization results are reported for various cancers [31,158], only a few studies are reported specifically for BC [36].…”

Section: Mathematical Models Used For Breast Cancer Managementmentioning

confidence: 99%

“…Even though many such optimization results are reported for various cancers [31,158], only a few studies are reported specifically for BC [36]. Even though many such optimization results are reported for various cancers [31,35,158,186], only a few studies are reported specifically for BC [36].…”

Section: Mathematical Models Used For Breast Cancer Managementmentioning

confidence: 99%

“…The data flow arrows in Figure 1 indicate the use of experimental data from preclinical and clinical trials to devise mathematical models. On the other hand, the results obtained from theoretical analysis using mathematical models (e.g., tumor doubling time, optimal drug dose, predicted tumor volume, estimated time for relapse of disease) are used to optimize the clinical experiment and proposed therapeutic strategy [31,[35][36][37][38]. Specifically, mathematical models can be used to analyze drug distribution (pharmacokinetics), drug response (pharmacodynamics) to monotherapy and combination therapy, development of drug resistance, and effect of drug toxicity related to cancer treatment [39,40].…”

Section: Introductionmentioning

confidence: 99%

“…The dynamics of other drugs have yet to be explored on a quantitative basis. Such drug-specific models can be used for treatment planning and dose optimization [30,34,35,64,160,187].…”

mentioning

confidence: 99%

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Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

Padmanabhan

Kheraldine

Meskin

et al. 2020

Cancers

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Breast cancer is one of the major causes of mortality in women worldwide. The most aggressive breast cancer subtypes are human epidermal growth factor receptor-positive (HER2+) and triple-negative breast cancers. Therapies targeting HER2 receptors have significantly improved HER2+ breast cancer patient outcomes. However, several recent studies have pointed out the deficiency of existing treatment protocols in combatting disease relapse and improving response rates to treatment. Overriding the inherent actions of the immune system to detect and annihilate cancer via the immune checkpoint pathways is one of the important hallmarks of cancer. Thus, restoration of these pathways by various means of immunomodulation has shown beneficial effects in the management of various types of cancers, including breast. We herein review the recent progress in the management of HER2+ breast cancer via HER2-targeted therapies, and its association with the programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) axis. In order to link research in the areas of medicine and mathematics and point out specific opportunities for providing efficient theoretical analysis related to HER2+ breast cancer management, we also review mathematical models pertaining to the dynamics of HER2+ breast cancer and immune checkpoint inhibitors.

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Section: Mathematical Models Used For Breast Cancer Managementmentioning

confidence: 99%

Section: Mathematical Models Used For Breast Cancer Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The dynamics of other drugs have yet to be explored on a quantitative basis. Such drug-specific models can be used for treatment planning and dose optimization [30,34,35,64,160,187].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

Padmanabhan

Kheraldine

Meskin

et al. 2020

Cancers

Self Cite

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“…Additionally, utilizing Q-learning, Zade et al [ 31 ] proposed a simulation framework where an RL agent optimizes the dosage of temozolomide in order to minimize glioblastoma tumor size. Yazdjerdi et al [ 32 ] applied Q-learning to optimize anti-angiogenic therapy in a simulated tumor environment. RL-based drug sensitivity screening regarding different tumor cell lines with Q-rank was proposed by Daoud et al [ 33 ].…”

Section: Recent Studies Of Reinforcement Learning In Malignant Diseasementioning

confidence: 99%

Reinforcement Learning for Precision Oncology

Wendt

Bornhäuser

Middeke

2021

Cancers

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Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are vastly applied for risk assessment and outcome prediction using either supervised or unsupervised learning. Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes. Recent breakthroughs in RL demonstrated remarkable results in gameplay and autonomous driving, often achieving human-like or even superhuman performance. While this type of machine learning holds the potential to become a helpful decision support tool, it comes with a set of distinctive challenges that need to be addressed to ensure applicability, validity and safety. In this review, we highlight recent advances of RL focusing on studies in oncology and point out current challenges and pitfalls that need to be accounted for in future studies in order to successfully develop RL-based decision support systems for precision oncology.

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Deep learning in precision medicine and focus on glioma

Liu

2023

Bioengineering & Transla Med

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Deep learning (DL) has been successfully applied to different fields for a range of tasks. In medicine, DL methods have been also used to improve the efficiency of disease diagnosis. In this review, we first summarize the history of the development of artificial intelligence models, demonstrate the features of the subtypes of machine learning and different DL networks, and then explore their application in the different fields of precision medicine, such as cardiology, gastroenterology, ophthalmology, dermatology, and oncology. By digging more information and extracting multilevel features from medical data, we found that DL helps doctors assess diseases automatically and monitor patients' physical health. In gliomas, research regarding application prospect of DL was mainly shown through magnetic resonance imaging and then by pathological slides. However, multi‐omics data, such as whole exome sequence, RNA sequence, proteomics, and epigenomics, have not been covered thus far. In general, the quality and quantity of DL datasets still need further improvements, and more fruitful multi‐omics characteristics will bring more comprehensive and accurate diagnosis in precision medicine and glioma.

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Reinforcement learning-based control of tumor growth under anti-angiogenic therapy

Cited by 30 publications

References 15 publications

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

Reinforcement Learning for Precision Oncology

Deep learning in precision medicine and focus on glioma

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