In silico model of the early effects of radiation therapy on the microcirculation and the surrounding tissues

Cicchetti, A.; Laurino, Federica; Possenti, Luca; Rancati, T.; Zunino, Paolo

doi:10.1016/j.ejmp.2020.04.006

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…The study of the effects of radiation treatment on microcirculation was recently addressed by Cicchetti et al [56]. The authors developed a numerical model to understand the effects of radiation therapy on normal tissues surrounding a tumor.…”

Section: Fluid Flow and Mass Transfermentioning

confidence: 99%

“…A brief analysis of the results reveals several blank spaces which represent the absence of information regarding the assumptions made when performing the numerical works. Additionally, the preferred software for these types of studies was found to be COMSOL Multiphysics ® software, followed by Ansys software ® , although researchers have begun to develop their own simulation software to overcome some of the limitations of the existing ones [56,60]. Moreover, a fundamental step in numerical simulations is mesh development.…”

Section: Numerical Optimizationmentioning

confidence: 99%

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Computational Simulations in Advanced Microfluidic Devices: A Review

et al. 2021

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Numerical simulations have revolutionized research in several engineering areas by contributing to the understanding and improvement of several processes, being biomedical engineering one of them. Due to their potential, computational tools have gained visibility and have been increasingly used by several research groups as a supporting tool for the development of preclinical platforms as they allow studying, in a more detailed and faster way, phenomena that are difficult to study experimentally due to the complexity of biological processes present in these models—namely, heat transfer, shear stresses, diffusion processes, velocity fields, etc. There are several contributions already in the literature, and significant advances have been made in this field of research. This review provides the most recent progress in numerical studies on advanced microfluidic devices, such as organ-on-a-chip (OoC) devices, and how these studies can be helpful in enhancing our insight into the physical processes involved and in developing more effective OoC platforms. In general, it has been noticed that in some cases, the numerical studies performed have limitations that need to be improved, and in the majority of the studies, it is extremely difficult to replicate the data due to the lack of detail around the simulations carried out.

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Section: Fluid Flow and Mass Transfermentioning

confidence: 99%

Section: Numerical Optimizationmentioning

confidence: 99%

Computational Simulations in Advanced Microfluidic Devices: A Review

et al. 2021

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“…They 58 are likely to reduce toxicity (Drean et al, 2016b;Lafond et al, 2020), and may one day inform or 59 help validate in silico models of treatment toxicity (e.g. (Cicchetti et al, 2020)). 60…”

Section: Spatial Descriptions Of Radiotherapy Dose: Normal Tissue Complication Models and Statistical Associationsmentioning

confidence: 99%

Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations

Ebert¹,

Gulliford²,

Acosta³

et al. 2021

Phys. Med. Biol.

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For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for individualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.

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Hybrid-dimensional models for blood flow and mass transport: Sequential and embedded 3D-1D models

Formaggia

Zunino

2022

Modeling of Mass Transport Processes in Biological Media

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In silico model of the early effects of radiation therapy on the microcirculation and the surrounding tissues

Cited by 4 publications

References 29 publications

Computational Simulations in Advanced Microfluidic Devices: A Review

Computational Simulations in Advanced Microfluidic Devices: A Review

Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations

Hybrid-dimensional models for blood flow and mass transport: Sequential and embedded 3D-1D models

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