Periodic Reporting for period 2 - MoDeLLiver (NUMERICAL MODELLING OF HEMODYNAMICS AND PHARMACOKINETICS FOR CLINICAL TRANSLATION)
Período documentado: 2022-04-01 hasta 2023-09-30
MoDeLLiver aims to develop a mathematical model to understand and predict blood flow changes caused by surgical interventions, particularly in the lung and liver. Improving treatment outcomes is essential. The model should consider the local point of intervention, whole organ perfusion, function, and their interaction with the entire circulation. Organ interactions and remodeling due to surgery or diseases should be included. Personalized planning requires simulations based on patient data, ideally non-invasive ones. Despite advances in dynamic imaging, the link between signal and underlying tissue perfusion and function needs further exploration. This understanding will provide new data for personalized simulations. The ultimate goal is to offer practitioners a simulation planning tool.
Work accomplished during the 1rst half of the project.
Section 1:
Our main goal is to create a new model that simulates the flow of blood and substances injected into the body across all major organs. This will help us understand how the circulatory system responds to changes and how injected substances travel throughout the body.
1.1 We developed a computer model of blood flow in the entire body (a closed-loop 0D nonlinear model), focusing on specific surgical procedures. It takes into account different configurations of the circulatory system, both normal and abnormal, to simulate various surgical scenarios selected with our clinical partners: liver resection to treat liver cancer or chronical liver disease [Sala 2023], and adding a shunt, called the ‘Potts shunt’ to palliate pulmonary hypertension [Pant 2022].
We also conducted a sensitivity analysis to understand uncertainties, at the population level [Sala 2023]. It was a novel approach to develop a multiscale modeling strategy specifically for Potts shunt treatment, which had never been done before [Pant 2022]. This allowed us to fine-tune our model using a simplified version and successfully predict outcomes (pressure equilibration) of this novel procedure for one teen-ager case. Different shunt designs were studied. The study provides insights into a complex condition and offers a predictive potential tool for Potts shunt suitability in pulmonary hypertensive patients.
1.2 We also started to study how substances injected into the bloodstream spread throughout the body. We took an initial step towards modeling the transport within the entire vascular system and gaining insight into how dispersion occurs due to its structure [Kowalski, CMBBE 2023, Paris]. We focused on the meso-scale, constructing vascular trees and simulating flow within tissues with and without tumors to understand their impact on flow and transport [Vignon-Clementel 2023].
Section 2:
Our second objective is to create organ models at different levels (whole body, whole organ, organ subpart, and functional unit) to predict blood flow and substance transport changes caused by diseases and interventions with minimal data requirements.
In [Sala 2023], we studied liver cancer treatment through partial resection, examining representative cases with different portal hypertension risks. We also analyzed heart hemodynamics associated with liver cirrhosis, considering hypo- and hyperhemodynamics in the left and right sides of the liver due to surgical interventions.
Our modeling work on pulmonary hypertension palliation led to a collaboration with Stanford University and clinicians from Hop. Marie Lannelongue to analyze 4D-MRI flow structures [Dong 2022]. Additionally, we were invited to co-author a book chapter with Stanford University colleagues [Yang 2022].
Section 3:
Our 3rd objective is to integrate dynamic imaging with modeling to assess organ perfusion and function. We seek to establish a quantitative relationship between imaging results and tissue characteristics.
Our models generate simulated images resembling real dynamic contrast-enhanced images. This helps us study how tissue structure variations, like blood vessel density and leakiness (commonly found in tumors), influence the resulting images. Additionally, we're working on an inverse problem to quantitatively recover structural differences from the images [Vignon-Clementel 2023].
Section 4:
Our final objective is to create simulation software based on our models for clinical use. We have enhanced the software's speed and efficiency, enabling us to run thousands of simulations for sensitivity analysis and population studies [Sala 2023]. The virtual population in [Sala 2023] and the code for [Vignon-Clementel 2023] are openly accessible. Additionally, we are conducting a clinical study (LSM, registered in clinical trials) to further develop and validate our high-level hemodynamics model for liver surgery.
References:
Dong M, J Cardio Magn Reson, 2022
Pant S, Biomechanics and Modeling in Mechanobiology 2022
Sala L, Annals of Biomed. Eng. 2023
Vignon-Clementel IE, Frontiers in Bioinformatics 2023
Yang W, et al. In Modelling Congenital Heart Disease 2022
Section 1:
Our main goal is to create a new model that simulates the flow of blood and substances injected into the body across all major organs. This will help us understand how the circulatory system responds to changes and how injected substances travel throughout the body.
1.1 We developed a computer model of blood flow in the entire body (a closed-loop 0D nonlinear model), focusing on specific surgical procedures. It takes into account different configurations of the circulatory system, both normal and abnormal, to simulate various surgical scenarios selected with our clinical partners: liver resection to treat liver cancer or chronical liver disease [Sala 2023], and adding a shunt, called the ‘Potts shunt’ to palliate pulmonary hypertension [Pant 2022].
We also conducted a sensitivity analysis to understand uncertainties, at the population level [Sala 2023]. It was a novel approach to develop a multiscale modeling strategy specifically for Potts shunt treatment, which had never been done before [Pant 2022]. This allowed us to fine-tune our model using a simplified version and successfully predict outcomes (pressure equilibration) of this novel procedure for one teen-ager case. Different shunt designs were studied. The study provides insights into a complex condition and offers a predictive potential tool for Potts shunt suitability in pulmonary hypertensive patients.
1.2 We also started to study how substances injected into the bloodstream spread throughout the body. We took an initial step towards modeling the transport within the entire vascular system and gaining insight into how dispersion occurs due to its structure [Kowalski, CMBBE 2023, Paris]. We focused on the meso-scale, constructing vascular trees and simulating flow within tissues with and without tumors to understand their impact on flow and transport [Vignon-Clementel 2023].
Section 2:
Our second objective is to create organ models at different levels (whole body, whole organ, organ subpart, and functional unit) to predict blood flow and substance transport changes caused by diseases and interventions with minimal data requirements.
In [Sala 2023], we studied liver cancer treatment through partial resection, examining representative cases with different portal hypertension risks. We also analyzed heart hemodynamics associated with liver cirrhosis, considering hypo- and hyperhemodynamics in the left and right sides of the liver due to surgical interventions.
Our modeling work on pulmonary hypertension palliation led to a collaboration with Stanford University and clinicians from Hop. Marie Lannelongue to analyze 4D-MRI flow structures [Dong 2022]. Additionally, we were invited to co-author a book chapter with Stanford University colleagues [Yang 2022].
Section 3:
Our 3rd objective is to integrate dynamic imaging with modeling to assess organ perfusion and function. We seek to establish a quantitative relationship between imaging results and tissue characteristics.
Our models generate simulated images resembling real dynamic contrast-enhanced images. This helps us study how tissue structure variations, like blood vessel density and leakiness (commonly found in tumors), influence the resulting images. Additionally, we're working on an inverse problem to quantitatively recover structural differences from the images [Vignon-Clementel 2023].
Section 4:
Our final objective is to create simulation software based on our models for clinical use. We have enhanced the software's speed and efficiency, enabling us to run thousands of simulations for sensitivity analysis and population studies [Sala 2023]. The virtual population in [Sala 2023] and the code for [Vignon-Clementel 2023] are openly accessible. Additionally, we are conducting a clinical study (LSM, registered in clinical trials) to further develop and validate our high-level hemodynamics model for liver surgery.
References:
Dong M, J Cardio Magn Reson, 2022
Pant S, Biomechanics and Modeling in Mechanobiology 2022
Sala L, Annals of Biomed. Eng. 2023
Vignon-Clementel IE, Frontiers in Bioinformatics 2023
Yang W, et al. In Modelling Congenital Heart Disease 2022
(1) The response to Potts shunt (PS) creation in clinical practice varies, and the underlying mechanisms are not well understood. Cardiovascular magnetic resonance imaging provides some insights, but it doesn't capture the full range of hemodynamic changes. Computational models, like the patient-specific multiscale model (MM) developed in [Pant 2022], offer valuable tools for understanding this complex condition and predicting patient-specific hemodynamic changes based on pre-operative characteristics. This computational model fills the gap in comprehensive investigations and provides valuable insights into flow patterns around and through the PS. The model's versatility allows adaptation for other patient cases, making it an essential tool for studying PS-related hemodynamics.
(2) General Sensitivity Analysis has proven valuable in cardiovascular modeling, especially when combined with the polynomial chaos expansion method. However, it has been rarely applied to closed-loop lumped models of the entire cardiovascular system. [Sala 2023] addresses this gap by focusing on varying parameters of different systemic blocks to capture population variability. Model outputs were kept within physiological ranges during sensitivity analysis. An innovative approach using the polynomial chaos expansion method helped reduce computational costs. While this work concentrates on partial hepatectomy, the insights gained can parameterize patient-specific models and define a physiologically relevant virtual population for other cardiovascular hemodynamics models.
(3) The [Vignon-Clementel 2023] paper is a significant achievement, demonstrating a proof of concept for analyzing the connection between dynamic imaging data and underlying tissue microstructure and function using in-silico data. This represents a crucial first step towards better understanding dynamic contrast-enhanced imaging, potentially leading to wider adoption in clinics. New collaborations are being discussed as a result.
The MoDeLLiver project aims to achieve the following results:
Understand the circulatory system's reaction to surgical acts and how different components impact substance circulation.
Determine the minimal organ model required to predict hemodynamics and changes due to disease and intervention.
Improve our understanding of the link between dynamic imaging and the observed system.
Facilitate clinical translation of the project's findings.
(2) General Sensitivity Analysis has proven valuable in cardiovascular modeling, especially when combined with the polynomial chaos expansion method. However, it has been rarely applied to closed-loop lumped models of the entire cardiovascular system. [Sala 2023] addresses this gap by focusing on varying parameters of different systemic blocks to capture population variability. Model outputs were kept within physiological ranges during sensitivity analysis. An innovative approach using the polynomial chaos expansion method helped reduce computational costs. While this work concentrates on partial hepatectomy, the insights gained can parameterize patient-specific models and define a physiologically relevant virtual population for other cardiovascular hemodynamics models.
(3) The [Vignon-Clementel 2023] paper is a significant achievement, demonstrating a proof of concept for analyzing the connection between dynamic imaging data and underlying tissue microstructure and function using in-silico data. This represents a crucial first step towards better understanding dynamic contrast-enhanced imaging, potentially leading to wider adoption in clinics. New collaborations are being discussed as a result.
The MoDeLLiver project aims to achieve the following results:
Understand the circulatory system's reaction to surgical acts and how different components impact substance circulation.
Determine the minimal organ model required to predict hemodynamics and changes due to disease and intervention.
Improve our understanding of the link between dynamic imaging and the observed system.
Facilitate clinical translation of the project's findings.