An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom

Wu, Chengyue; Hormuth, David A.; Easley, Ty; Eijkhout, Victor; Pineda, Federico; Karczmar, Gregory S.; Yankeelov, Thomas E.

doi:10.1016/j.media.2021.102186

Cited by 6 publications

(13 citation statements)

References 78 publications

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“…The main limitation to extending to 3D is the characterization of the angiogenic sprouts by endothelial cells that make up both sides (in 2D) of the sprout. This can be overcome by describing the vessels as nodes (which describe the radius of the vessel) connected by edges (which describe the directionality of the vessels), an approach taken by [ 70 , 71 ]. Another key modeling limitation is the use the maximum likelihood values from Scenarios 1 and 2, as opposed to Bayesian inference in Scenarios 3–5.…”

Section: Discussionmentioning

confidence: 99%

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

et al. 2023

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The goal of this study is to calibrate a multiscale model of tumor angiogenesis with time-resolved data to allow for systematic testing of mathematical predictions of vascular sprouting. The multi-scale model consists of an agent-based description of tumor and endothelial cell dynamics coupled to a continuum model of vascular endothelial growth factor concentration. First, we calibrate ordinary differential equation models to time-resolved protein concentration data to estimate the rates of secretion and consumption of vascular endothelial growth factor by endothelial and tumor cells, respectively. These parameters are then input into the multiscale tumor angiogenesis model, and the remaining model parameters are then calibrated to time resolved confocal microscopy images obtained within a 3D vascularized microfluidic platform. The microfluidic platform mimics a functional blood vessel with a surrounding collagen matrix seeded with inflammatory breast cancer cells, which induce tumor angiogenesis. Once the multi-scale model is fully parameterized, we forecast the spatiotemporal distribution of vascular sprouts at future time points and directly compare the predictions to experimentally measured data. We assess the ability of our model to globally recapitulate angiogenic vasculature density, resulting in an average relative calibration error of 17.7% ± 6.3% and an average prediction error of 20.2% ± 4% and 21.7% ± 3.6% using one and four calibrated parameters, respectively. We then assess the model’s ability to predict local vessel morphology (individualized vessel structure as opposed to global vascular density), initialized with the first time point and calibrated with two intermediate time points. In this study, we have rigorously calibrated a mechanism-based, multiscale, mathematical model of angiogenic sprouting to multimodal experimental data to make specific, testable predictions.

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Section: Discussionmentioning

confidence: 99%

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

et al. 2023

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“…As out of plane effects could play a significant role in vessel sprouting, future efforts will be focused on extending the formalism to The main limitation to extending to 3D is the characterization of the angiogenic sprouts by cells that make up both sides (in 2D) of the sprout. This can be overcome by describing the vessels as nodes (which describe the radius of the vessel) connected by edges (which describe the directionality of the vessels), an approach taken by [65,66]. In our future work, we aim to utilize more experimental replicates to further calibrate and validate our model and to devise experimental protocols with anti-angiogenic and radiation therapies to model the effects of these treatments on angiogenic sprouts in vitro [67].…”

Section: Discussionmentioning

confidence: 99%

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

Phillips

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Gadde

et al. 2021

Preprint

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The goal of this study is to calibrate a multiscale model of tumor angiogenesis with time-resolved data to allow for systematic testing of mathematical predictions of vascular sprouting. The multi-scale model consists of an agent-based description of tumor and endothelial cell dynamics coupled to a continuum model of vascular endothelial growth factor concentration. First, we calibrate ordinary differential equation models to time-resolved protein expression data to estimate the rates of secretion and consumption of vascular endothelial growth factor by endothelial and tumor cells, respectively. These parameters are then input into the multiscale tumor angiogenesis model, and the remaining model parameters are then calibrated to time resolved confocal microscopy images obtained within a 3D vascularized microfluidic platform. The microfluidic platform mimics a functional blood vessel with a surrounding collagen matrix seeded with inflammatory breast cancer cells, which induce tumor angiogenesis. Once the multi-scale model is fully parameterized, we forecast the spatiotemporal distribution of vascular sprouts at future time points and directly compare the predictions to experimentally measured data. We assess the ability of our model to globally recapitulate angiogenic vasculature density, resulting in an average relative calibration error of 17.7% ± 6.3% and an average prediction error of 20.2% ± 4% and 21.7% ±3.6% using one and four calibrated parameters, respectively. We then assess the model's ability to predict local vessel morphology (individualized vessel structure as opposed to global vascular density), initialized with the first time point and calibrated with two intermediate time points. To the best of our knowledge, this represents the first study to integrate well-controlled, experimental data into a mechanism-based, multiscale, mathematical model of angiogenic sprouting to make specific, testable predictions.

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“…A key advantage of digital phantoms is their capability to represent complex shapes and material properties for populations rather than specific individuals [91][92][93]. Moreover, in contrast with ex vivo medical imaging, digital phantoms are not subject to tissue shrinkage, deformation, or any physical changes caused by invasive or post-mortem procedures [94]. They are analogous to instrument models generated through computer-aided design in that they can be converted directly into sparse patient models or voxelized into volumetric models and in that their suitability for a given simulation depends on the features included and the level of detail.…”

Section: Instrument and Patient Modelsmentioning

confidence: 99%

In silico simulation: a key enabling technology for next-generation intelligent surgical systems

et al. 2023

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To mitigate the challenges of operating through narrow incisions under image guidance, there is a desire to develop intelligent systems that assist decision making and spatial reasoning in minimally invasive surgery (MIS). In this context, machine learning-based systems for interventional image analysis are receiving considerable attention because of their flexibility and the opportunity to provide immediate, informative feedback to clinicians. It is further believed that learning-based image analysis may eventually form the foundation for semior fully automated delivery of surgical treatments. A significant bottleneck in developing such systems is the availability of annotated images with sufficient variability to train generalizable models, particularly the most recently favored deep convolutional neural networks or transformer architectures. A popular alternative to acquiring and manually annotating data from the clinical practice is the simulation of these data from human-based models. Simulation has many advantages, including the avoidance of ethical issues, precisely controlled environments, and the scalability of data collection. Here, we survey recent work that relies on in silico training of learning-based MIS systems, in which data are generated via computational simulation. For each imaging modality, we review available simulation tools in terms of compute requirements, image quality, and usability, as well as their applications for training intelligent systems. We further discuss open challenges for simulation-based development of MIS systems, such as the need for integrated imaging and physical modeling for non-optical modalities, as well as generative patient models not dependent on underlying CT, MRI, or other patient data. In conclusion, as the capabilities of in silico training mature, with respect to sim-to-real transfer, computational efficiency, and degree of control, they are contributing toward the next generation of intelligent surgical systems.

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An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom

Cited by 6 publications

References 78 publications

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

In silico simulation: a key enabling technology for next-generation intelligent surgical systems

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