Sequential Therapeutic Response Modeling for Tumor Treatment Using Computational Hybrid Control Systems Approach

Oduola, Wasiu Opeyemi; Li, Xiangfang L.; Chen, Duan; Qian, Lijun; Dougherty, Edward R.

doi:10.1109/tbme.2017.2723957

Cited by 6 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational models are increasingly being used for cancer forecasting 25 . Although several models have been detailed in the scientific literature, actual biological conditions are far more complex to the point that current computational models are not able to sufficiently describe all details 26 .…”

Section: Discussionmentioning

confidence: 99%

Multiscale computational modeling of cancer growth using features derived from microCT images

Zangooei

Margolis

Hoyt

2021

Sci Rep

View full text Add to dashboard Cite

Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.

show abstract

Section: Discussionmentioning

confidence: 99%

Multiscale computational modeling of cancer growth using features derived from microCT images

Zangooei

Margolis

Hoyt

2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Mathematical models are increasingly being used in the area of quantitative multiscale cancer modeling. 38 Although several modeling approaches have been explored in the literature, the actual biosystems are far more complex to the point that current available computational tools are not able to sufficiently describe all the details. In addition, such mathematical models are usually computationally demanding and should therefore be designed in a way that maintains a balance between model complexity and mathematical tractability or simplicity with the aid of clinical or experimental data.…”

Section: Discussionmentioning

confidence: 99%

Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System

Oduola

2018

Cancer Inform

Self Cite

View full text Add to dashboard Cite

Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.

show abstract

Optimal administration strategy in chemotherapy regimens using multi-drug cell-cycle specific tumor growth models

Qods

Arkat

Batmani

2023

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Sequential Therapeutic Response Modeling for Tumor Treatment Using Computational Hybrid Control Systems Approach

Abstract: This paper is the first attempt to provide a switched hybrid control systems framework on sequential drug administration to biomedical researchers and clinicians.

Cited by 6 publications

References 27 publications

Multiscale computational modeling of cancer growth using features derived from microCT images

Multiscale computational modeling of cancer growth using features derived from microCT images

Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System

Optimal administration strategy in chemotherapy regimens using multi-drug cell-cycle specific tumor growth models

Contact Info

Product

Resources

About