Mechano-Biological Features in a Patient-Specific Computational Model of Glioblastoma

Paravalvular leak (PVL) is a relatively frequent complication after transcatheter aortic valve replacement (TAVR) with increased mortality. Currently, there is no effective method to pre-operatively predict and prevent PVL. In this study, we developed a computational model to predict the severity of PVL after TAVR. Nonlinear finite element (FE) method was used to simulate a self-expandable CoreValve deployment into a patient-specific aortic root, specified with human material properties of aortic tissues. Subsequently, computational fluid dynamics (CFD) simulations were performed using the post-TAVR geometries from the FE simulation, and a parametric investigation of the impact of the transcatheter aortic valve (TAV) skirt shape, TAV orientation, and deployment height on PVL was conducted. The predicted PVL was in good agreement with the echocardiography data. Due to the scallop shape of CoreValve skirt, the difference of PVL due to TAV orientation can be as large as 40%. Although the stent thickness is small compared to the aortic annulus size, we found that inappropriate modeling of it can lead to an underestimation of PVL up to 10 ml/beat. Moreover, the deployment height could significantly alter the extent and the distribution of regurgitant jets, which results in a change of leaking volume up to 70%. Further investigation in a large cohort of patients is warranted to verify the accuracy of our model. This study demonstrated that a rigorously developed patient-specific computational model can provide useful insights into underlying mechanisms causing PVL and potentially assist in pre-operative planning for TAVR to minimize PVL.

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87Non-invasive virtual prediction of site of origin in outflow tract ventricular arrhythmias with a patient-specific computational model

European Heart Journal ◽

10.1093/eurheartj/ehx501.87 ◽

2017 ◽

Vol 38 (suppl_1) ◽

Author(s):

R. Doste ◽

D. Soto-Iglesias ◽

G. Bernardino ◽

R. Sebastian ◽

M. Sermesant ◽

...

Keyword(s):

Computational Model ◽

Ventricular Arrhythmias ◽

Outflow Tract ◽

Patient Specific

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A Computational Model for the Evaluation of Complement System Regulation under Homeostasis, Disease, and Drug Intervention

10.1101/225029 ◽

2017 ◽

Cited By ~ 1

Author(s):

Nehemiah Zewde ◽

Dimitrios Morikis

Keyword(s):

Computational Model ◽

Complement System ◽

Complement Activation ◽

Late Stage ◽

Inflammatory Diseases ◽

Early Stage ◽

Host Cells ◽

Patient Specific ◽

Complement Dysregulation ◽

The Complement System

HighlightsComputational model describing dynamics of complement system activation pathwaysComplement dysregulation leads to deviation from homeostasis and to inflammatory diseasesModel identifies biomarkers to quantify the effects of complement dysregulationKnown drugs restore impaired dynamics of complement biomarkers under dysregulationDisease-specific models are suitable for diagnosis and patient-specific drug treatmentAbstractThe complement system is a part of innate immunity that rapidly removes invading pathogens and impaired host-cells. Activation of the complement system is balanced under homeostasis by regulators that protect healthy host-cells. Impairment of complement regulators tilts the balance, favoring activation and propagation that leads to inflammatory diseases. The most potent regulator of the complement system is Factor H (FH), and its impairment induces improper complement activation that leads to inflammatory diseases, such as atypical hemolytic uremic syndrome and age related macular degeneration. To understand the dynamics involved in the pivotal balance between activation and regulation, we have developed a comprehensive computational model of the alternative and classical pathways of the complement system. The model is composed of 290 ordinary differential equations with 142 kinetic parameters that describe the state of complement system under homeostasis and disorder through FH impairment. We have evaluated the state of the system by generating concentration-time profiles for the biomarkers C3, C3a-desArg, C5, C5a-desArg, Factor B (FB), Ba, Bb, and fC5b-9 that are influenced by complement dysregulation. We show that FH-mediated disorder induces substantial levels of complement activation compared to homeostasis, by generating reduced levels of C3 and FB, and to a lesser extent C5, and elevated levels of C3a-desArg, Ba, Bb, C5a-desArg, and fC5b-9. These trends are consistent with clinically observed biomarkers associated with complement-mediated diseases. Furthermore, we introduced therapy states by modeling known drugs of the complement system, a compstatin variant (C3 inhibitor) and eculizumab (a C5 inhibitor). Compstatin demonstrates strong restorative effects for early-stage biomarkers, such as C3a-desArg, FB, Ba, and Bb, and milder restorative effects for late-stage biomarkers, such as C5a-desArg and fC5b-9, whereas eculizumab has strong restorative effects on late-stage biomarkers, and negligible effects on early-stage biomarkers. These results highlight the need for patient-specific therapies that target early complement activation at the C3 level, or late-stage propagation of the terminal cascade at the C5 level, depending on the specific FH-mediated disease and the manifestations of a patient’s genetic profile in complement regulatory function.

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Patient-Specific Computational Model and Dosimetry Calculations for PET/CT of a Patient Pregnant with Twins

Journal of Nuclear Medicine ◽

10.2967/jnumed.117.205286 ◽

2018 ◽

Vol 59 (9) ◽

pp. 1451-1458 ◽

Cited By ~ 3

Author(s):

Tianwu Xie ◽

Paolo Zanotti-Fregonara ◽

Agathe Edet-Sanson ◽

Habib Zaidi

Keyword(s):

Computational Model ◽

Patient Specific ◽

Pet Ct

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A personalized computational model of edema formation in myocarditis based on long-axis biventricular MRI images

BMC Bioinformatics ◽

10.1186/s12859-019-3139-0 ◽

2019 ◽

Vol 20 (S6) ◽

Cited By ~ 3

Author(s):

Ruy Freitas Reis ◽

Juliano Lara Fernandes ◽

Thaiz Ruberti Schmal ◽

Bernardo Martins Rocha ◽

Rodrigo Weber dos Santos ◽

...

Keyword(s):

Mathematical Model ◽

Immune Response ◽

Computational Model ◽

Cardiac Tissue ◽

Geometrical Model ◽

Specific Model ◽

Patient Specific ◽

Edema Formation ◽

Promising Tool ◽

Inflammatory Edema

Abstract Background Myocarditis is defined as the inflammation of the myocardium, i.e. the cardiac muscle. Among the reasons that lead to this disease, we may include infections caused by a virus, bacteria, protozoa, fungus, and others. One of the signs of the inflammation is the formation of edema, which may be a consequence of the interaction between interstitial fluid dynamics and immune response. This complex physiological process was mathematically modeled using a nonlinear system of partial differential equations (PDE) based on porous media approach. By combing a model based on Biot’s poroelasticity theory with a model for the immune response we developed a new hydro-mechanical model for inflammatory edema. To verify this new computational model, T2 parametric mapping obtained by Magnetic Resonance (MR) imaging was used to identify the region of edema in a patient diagnosed with unspecific myocarditis. Results A patient-specific geometrical model was created using MRI images from the patient with myocarditis. With this model, edema formation was simulated using the proposed hydro-mechanical mathematical model in a two-dimensional domain. The computer simulations allowed us to correlate spatiotemporal dynamics of representative cells of the immune systems, such as leucocytes and the pathogen, with fluid accumulation and cardiac tissue deformation. Conclusions This study demonstrates that the proposed mathematical model is a very promising tool to better understand edema formation in myocarditis. Simulations obtained from a patient-specific model reproduced important aspects related to the formation of cardiac edema, its area, position, and shape, and how these features are related to immune response.

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Addendum to ‘A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18 F-FMISO-PET’

Journal of The Royal Society Interface ◽

10.1098/rsif.2015.0927 ◽

2015 ◽

Vol 12 (112) ◽

pp. 20150927 ◽

Cited By ~ 14

Author(s):

Russell C. Rockne ◽

Andrew D. Trister ◽

Joshua Jacobs ◽

Andrea J. Hawkins-Daarud ◽

Maxwell L. Neal ◽

...

Keyword(s):

Computational Model ◽

Radiation Resistance ◽

Patient Specific

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A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18 F-FMISO-PET

Journal of The Royal Society Interface ◽

10.1098/rsif.2014.1174 ◽

2015 ◽

Vol 12 (103) ◽

pp. 20141174 ◽

Cited By ~ 33

Author(s):

Russell C. Rockne ◽

Andrew D. Trister ◽

Joshua Jacobs ◽

Andrea J. Hawkins-Daarud ◽

Maxwell L. Neal ◽

...

Keyword(s):

Computational Model ◽

Radiation Resistance ◽

Radiation Treatment ◽

Treatment Plan ◽

Three Dimensional ◽

Radiation Sensitivity ◽

Patient Specific ◽

Primary Brain Tumour ◽

The Brain

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient's disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [ 18 F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.

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Effective Computational Framework for Pre-Interventional Planning of Peripheral Arteriovenous Malformations with In Vivo and In Vitro Validation

10.1101/2021.05.15.444310 ◽

2021 ◽

Author(s):

Gaia Franzetti ◽

Mirko Bonfanti ◽

Cyrus Tanade ◽

Chung Sim Lim ◽

Janice Tsui ◽

...

Keyword(s):

Blood Flow ◽

Computational Model ◽

Arteriovenous Malformations ◽

Clinical Decision Making ◽

Patient Specific ◽

Post Intervention ◽

Computational Framework ◽

Mock Circulatory System ◽

Flow Through

Purpose: Peripheral arteriovenous malformations (pAVMs) are congenital lesions characterised by abnormal high-flow, low-resistance vascular connections - constituting the so-called nidus - between arteries and veins. The mainstay treatment typically involves the embolisation of the nidus with embolic and sclerosant agents, however the complexity of AVMs often leads to uncertain outcomes. This study aims at developing a simple, yet effective computational framework to aid the clinical decision making around the treatment of pAVMs. Methods: A computational model was developed to simulate the pre-, intra-, and post-intervention haemodynamics of an AVM. A porous medium of varying permeability was used to simulate the effect that the sclerosant has on the blood flow through the nidus. The computational model was informed by computed tomography (CT) scans and digital subtraction angiography (DSA) images, and the results were compared against clinical data and experimental results. Results: The computational model was able to simulate the blood flow through the AVM throughout the intervention and predict (direct and indirect) haemodynamic changes due to the embolisation. The simulated transport of the dye in the AVM was compared against DSA time-series obtained at different intervention stages, providing confidence in the results. Moreover, experimental data obtained via a mock circulatory system involving a patient specific 3D printed phantom of the same AVM provided further validation of the simulation results. Conclusion: We developed a simple computational framework to simulate AVM haemodynamics and predict the effects of the embolisation procedure. The developed model lays the foundation of a new, computationally driven treatment planning tool for AVM embolisation procedures.

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