In Silico Medicine Literature Roundup

Posted on 25 July 2023 by Rebecca Bryan


We are happy to present another round-up of publications by Simpleware users, following on from our recent posts on patient-specific planning and point of care 3D printing. In this new collection of papers, the benefits of in silico medicine using medical imaging and computational modelling can be seen across different applications. The use of in silico methods can supplement bench and animal testing, accelerate R&D, as well as supporting clinical trials to evaluate efficacy and safety, and analyzing performance in post-market surveillance studies. These methods align with Simpleware software’s established capabilities in image processing, automation, and converting image data into computational models. Read on for some highlights of recent papers covering in silico applications:

Patient-Specific Stomach Biomechanics Before and After Laparoscopic Sleeve Gastrectomy

Toniolo, I., Berardo, A., Foletto, M., Fiorillo, C., Quero, G., Perretta, S., Carniel, E. L., 2022. Patient-specific stomach biomechanics before and after laparoscopic sleeve gastrectomy. Surgical Endoscopy, 36.

Patient-specific models of 23 patients before and after LSG (CC BY 4.0)

Fig. 1. A Patient-specific models obtained from the MRI of the 23 considered patients, before and after LSG. The scale bar is 100 mm. b Pressure–volume relationships obtained after an inflation simulation; solid lines represent the pre-surgical stomachs, dashed lines stated for the post-surgical ones (Image by Toniolo et al. / CC BY 4.0 / Resized from original).


"Obesity has become a global epidemic. Bariatric surgery is considered the most effective therapeutic weapon in terms of weight loss and improvement of quality of life and comorbidities. Laparoscopic sleeve gastrectomy (LSG) is one of the most performed procedures worldwide, although patients carry a nonnegligible risk of developing post-operative GERD and BE. The aim of this work is the development of computational patient-specific models to analyze the changes induced by bariatric surgery, i.e., the volumetric gastric reduction, the mechanical response of the stomach during an inflation process, and the related elongation strain (ES) distribution at different intragastric pressures. Patient-specific pre- and post-surgical models were extracted from Magnetic Resonance Imaging (MRI) scans of patients with morbid obesity submitted to LSG. Twenty-three patients were analyzed, resulting in forty-six 3D-geometries and related computational analyses. A significant difference between the mechanical behavior of pre- and post-surgical stomach subjected to the same internal gastric pressure was observed, that can be correlated to a change in the global stomach stiffness and a minor gastric wall tension, resulting in unusual activations of mechanoreceptors following food intake and satiety variation after LSG. Computational patient-specific models may contribute to improve the current knowledge about anatomical and physiological changes induced by LSG, aiming at reducing post-operative complications and improving quality of life in the long run."

Use of Simpleware Software

"For each patient two models were reconstructed, i.e., the physiological (pre-surgical) stomach and the corresponding sleeved stomach at six months after surgery. The segmentation of the MRI scans led to the generation of 46 virtual solid stomach models (#23 pre-surgical stomachs + the corresponding #23 sleeved stomachs) by means of Synopsys Simpleware ScanIP. The specifics of the scanner machine and image resolution are reported in [22]. The volumetric identification was done from the MRI sequences of an empty stomach in the transverse plane, considering the optimal view for the recognition of gastroesophageal junction. The segmentation considered the whole stomach to the pyloric ring. The stomach volumes obtained were checked in the coronal plane and then exported."

Outcomes and Impact

"Bariatric surgery is considered the best option to treat people with morbid obesity but needs to be refined, since it is mainly based on empiric approach, with sometimes complications and side effects. Computational modeling can be a powerful tool to address the main limits of bariatric surgery, without performing additional clinical trials and animal testing. This work pointed out the importance to use a patient-specific approach for a better comprehension of the effects of LSG procedure aimed at improving bariatric surgery outcomes. Even if some assumptions were adopted, stomach mechanics showed a different behavior after the operation, mainly regarding ES field. The removal of the fundus due to the LSG resulted in a stiffer structure with important reflexes on food intake and satiety. Moreover, lower values of ES are related to the reduction in stomach diameter, which implies a minor gastric wall tension, at equal internal pressure (Laplace law). Future developments concerning this topic will include other key variables, as the role of the gastric wall and its interaction with metallic clips, which could generate high stress concentrations and possible tissues damages."

The Biomechanics of Metaphyseal Cone Augmentation in Revision Knee Replacement

Hu, J., Gundry, Michael., Zheng, K., Zhong, J., Hourigan, P., Meakin, J. R., Winlove, C. P., Toms, A. D., Knapp, K. M., Chen, J., 2022. The biomechanics of metaphyseal cone augmentation in revision knee replacement. Journal of the Mechanical Behavior of Biomedical Materials, 131.

Patient-specific knee replacement model with DXA image and CAD data (CC BY 4.0)

Fig. 2. a) Masked DXA image isolating tibia and fibula bone; b) processed DXA image only contains tibia bone and the ROIs assigned; c) 3D rendered tibia and implant based on CT images; d) Individual masks after segmentation (pink – cortical bone, green – trabecular bone, blue – bone cement, cyan – cone, red – baseplate and stem); e) registration of CAD models by using virtual surgery to create the patient-specific model; f) patient-specific FE models with mesh refinement at the interfaces (Image by Hu et al. / CC BY 4.0 / Resized from original).


"The demand for revision knee replacement (RKR) has increased dramatically with rising patient life expectancy and younger recipients for primary TKR. However, significant challenges to RKR arise from osseous defects, reduced bone quality, potential bone volume loss from implant removal and the need to achieve implant stability. This study utilizes the outcomes of an ongoing RKR clinical trial using porous metaphyseal cones 3D-printed of titanium, to investigate 1) bone mineral density (BMD) changes in three fixation zones (epiphysis, metaphysis, and diaphysis) over a year and 2) the biomechanical effects of the cones at 6 months post-surgery. It combines dual-energy x-ray absorptiometry (DXA), computed tomography (CT) with patient-specific based finite element (FE) modelling. Bone loss (−0.086 ± 0.05 g/cm2) was found in most patients over the first year. The biomechanical assessment considered four different loading scenarios from standing, walking on a flat surface, and walking downstairs, to a simulated impact of the knee. The patient-specific FE models showed that the cones marginally improved the strain distribution in the bone and shared the induced load but played a limited role in reducing the risks of bone fracture or cement debonding. This technique of obtaining real live data from a randomized clinical trial and inserting it into an in-silico FE model is unique and innovative in RKR research. The tibia RKR biomechanics examined open up further possibilities, allowing the in-silico testing of prototypes and implant combinations without putting patients at risk as per the recommended IDEAL framework standards. This process with further improvements could allow rapid innovation, optimization of implant design, and improve surgical planning."

Use of Simpleware Software

"The CT images of the individual patient at month six were processed in ScanIP (Ver. 2019; Simpleware Ltd., Exeter, UK), to distinguish and label individual regions, including implants (baseplate and stem), metaphyseal cone, bone cement, cortical and trabecular bones. These regions were identified based on their Hounsfield Unit (HU) values and used to create masks (Fig. 2c and d, both 3D rendering and 2D masks). The created masks were then exported as surface models with the Standard Tessellation Language (.stl) format for modelling."

Outcomes and Impact

"This study combines clinical imaging data and FEA to investigate the time-dependent BMD change and the biomechanical effects of metaphyseal cone augmentation in RKR. The most significant BMD changes were found to occur within the first three months after surgery with considerable diversity across the patient cohort. Two-third of the patients showed different degrees of resorption after the 12 months. These changes were much milder than in primary TKR patients. However, the increased bone loss and the reduced bone quality in RKR patients altered the biomechanical environment dramatically compared with the primary TKR, including more strain-shielded regions and with more load transmitted through the implants. The insertion of the metaphyseal cones marginally improved the strain distribution and load bearing ratios but played a limited role in reducing the risks of bone fracture and/or cement debonding."

Integrating Particle Tracking with Computational Fluid Dynamics to Assess Haemodynamic Perturbation by Coronary Artery Stents

Boldock, L., Inzoli, A., Bonardelli, S., Hsiao, S., Marzo, A., Narracott, A., Gunn, J., Dubini, G., Chiastra, C., Halliday, I., Morris, P. D., Evans, P. C., Perrault, C. M., 2022. Integrating particle tracking with computational fluid dynamics to assess haemodynamic perturbation by coronary artery stents. Plos One, 17(7).

Reconstructed μCT data of a stent cast and registration (CC BY 4.0)

A: The use of PDMS casts of model vessels provided μCT scan data of accurate geometry and captured fine stent strut detail, including prolapsed struts (red arrow). Top: Optimising μCT scan and reconstruction parameters for homogeneous PDMS casts produced an image of the lumen with a clearly defined wall boundary. Bottom: Cutaway of a section of wall boundary reconstructed from a 9.92 μm resolution scan of a Coroflex Blue coronary stent cast. B: Rigid body registration was performed on models of a Coroflex Blue coronary stent and its associated cast, reconstructed from data from separate μCT scans. The two were aligned in an effort to measure the ability of casts to recreate the geometry of stents (Image by Boldock et al. / CC BY 4.0 / Resized from original).


"Coronary artery stents have profound effects on arterial function by altering fluid flow mass transport and wall shear stress. We developed a new integrated methodology to analyse the effects of stents on mass transport and shear stress to inform the design of haemodynamically-favourable stents. Stents were deployed in model vessels followed by tracking of fluorescent particles under flow. Parallel analyses involved high-resolution micro-computed tomography scanning followed by computational fluid dynamics simulations to assess wall shear stress distribution. Several stent designs were analysed to assess whether the workflow was robust for diverse strut geometries. Stents had striking effects on fluid flow streamlines, flow separation or funnelling, and the accumulation of particles at areas of complex geometry that were tightly coupled to stent shape. CFD analysis revealed that stents had a major influence on wall shear stress magnitude, direction and distribution and this was highly sensitive to geometry. Integration of particle tracking with CFD allows assessment of fluid flow and shear stress in stented arteries in unprecedented detail. Deleterious flow perturbations, such as accumulation of particles at struts and non-physiological shear stress, were highly sensitive to individual stent geometry. Novel designs for stents should be tested for mass transport and shear stress which are important effectors of vascular health and repair."

Use of Simpleware Software

"Reconstructed image datasets were imported into ScanIP software (Synopsys Inc.) and the fluid domain selected via thresholding to form a mask. Following a mesh sensitivity analysis, masks were converted to volume meshes consisting of ≥2 million tetrahedral elements via the ScanIP +FE Module, in FLUENT CFD Output format, using the +FE Free mesh algorithm. Element size and internal volume change rate were altered on a 100-point scale, where element edge length was dependent on voxel size, a product of the original μCT scan resolution. Once complete, the Mesh Quality Inspection Tool was checked for errors or warnings, before mesh files were exported."

Outcomes and Impact

"We have developed a workflow that integrates particle tracking with CFD that can detect deleterious flow perturbations that are sensitive to the geometry of stents. We suggest that novel designs for stents should be routinely tested for fluid flow and shear stress effects because these are important effectors of vascular health and repair."

A Mechano-Chemical Computational Model of Deep Vein Thrombosis

Jimoh-Taiwo, Q., Haffejee, R., Ngoepe, M., 2022. A Mechano-Chemical Computational Model of Deep Vein Thrombosis. Frontiers in Physics, 10.

Analysis of clot formed in a vein (CC BY 4.0)

(A,B) Maximum thrombin and fibrin concentration in the vein over 100s. (C) Volume of clot formed in the vein over 100s. (D) Comparison between in vivo and in silico clot. (E) Contour showing clot growth over t = 100 s. Scalar = 1 for clot core and >0.5 for clot shell (patient-specific model) (Image by Jimoh-Taiwo et al. / CC BY 4.0 / Resized from original).


"Computational models of deep vein thrombosis (DVT) typically account for either the mechanical or biochemical factors involved in thrombus formation. Developing a model that accounts for both factors will improve our understanding of the coagulation process in this particular disease. The work presented in this study details the development of a CFD model that considers the biochemical reactions between thrombin and fibrinogen, pulsatile blood flow, and clot growth within a three-dimensional patient-specific common femoral vein. Thrombin is released into the bloodstream from an injury zone on the wall of the vein. The Michaelis–Menten equation is used to represent the conversion of thrombin and fibrinogen to fibrin, the final product of the coagulation process. The model development starts with a two-dimensional idealized geometry. At this stage, the model is used to conduct a parametric study to determine the effects of varying parameters such as inlet velocity, vein diameter, and peak thrombin concentration on the size and shape of the clot formed. Peak thrombin concentration is the key factor driving the initiation and propagation of clots in the vein. To demonstrate the potential use of the model, the two-dimensional model is then extended to an image-derived three-dimensional patient-specific geometry. Realistic clot growth was achieved using this model, and the clot was compared to a clot formed in vivo. The volume of the clot that formed in the patient was about 4% smaller than that formed in the simulation. This demonstrates that with further development and refinement, this model could be used for patient-specific interventional planning. The model provides a means for predicting clot formation under different physiological conditions in a non-invasive manner."

Use of Simpleware Software

"Geometry segmentation and reconstruction were important stages of developing a patient-specific DVT model. For this study, the vein geometry was extracted from a CT scan of a 40-year-old male patient. The CT scan images were downloaded from, an open-edit educational radiology repository [50]. To develop the 3D geometry from the CT image, the images underwent segmentation using SYNOPSYS Simpleware Software (SYNOPSYS, California, United States). To make segmentation easier and less complex for Simpleware, the images were converted from the 3D image with pixel value (R, B, G) into a grayscale image. As a result of image noise in some slices, the applied threshold value of 200 did not accurately account for the CFV. On vein and clot surfaces, a Fourier smoothing function of order 10 was applied to eliminate the ragged finish generated as a result of the high segmentation spacing. Detailed images of the segmentation process are presented in the Supplementary material. From the data given, the clot on the left CFV was identified and the right CFV was developed and served as the control. The result generated by the model was compared to the clot formed under physiological conditions. The three-dimensional model at its most complex stage was applied on the right CFV. First, a steady-state simulation was performed to determine areas with recirculation, stagnation (<0.001 m/s), and/or low strain rate (<100 s−1). A UDF was included in the model to ensure that the clot initiates only in cells that have met this threshold requirement."

Outcomes and Impact

"In this study, the development of a framework that predicts clot formation in a patient-specific three-dimensional femoral vein geometry using CFD techniques and biochemical reactions is outlined. This study aims to understand the effect of changing these factors on the clot size and determining which factor had the most impact on clot initiation and propagation. Increasing the velocity and vein diameter caused a reduction in clot size, and increasing the thrombin peak concentration increased the clot size. Thrombin concentration was found to be the sole factor determining when clot initiation occurs and the driving factor when determining the size of the clot. The work shows that higher thrombin concentration produces denser, larger clots. The model developed in this work was verified using an experimental clot growth study. The clot formed experimentally is compared to the computationally grown clot. After carrying out a visual comparison between both clots, there is a 24% difference between the heights of these clots with the experimental clot being thicker. Previously, most DVT models were mainly flow-based, studying areas of stagnation and recirculation. Most of them included solid valve walls and sinuses. This model avoids this complexity by simulating blood flow coming out of the valve and includes biochemical reactions on the desired injury zone, which allows for further investigation of the clot formation process. This process also helps us study the initiation and propagation of clots, in addition to identifying regions of clot formation. It is evident that the model can predict clot formation under different flow conditions."

Convection-Enhanced Delivery In Silico Study for Brain Cancer Treatment

Lambride, C., Vavourakis, V., Stylianopoulos, T., 2022. Convection-Enhanced Delivery In Silico Study for Brain Cancer Treatment. Frontiers in Bioengineering and Biotechnology, 10.

Simulated drug concentration simulation for brain cancer treatment (CC BY 4.0)

Simulated drug concentration using baseline tumor microenvironment conditions. A sagittal view in the center of tumor tissue showing the spatial distribution of drug concentration and for different diameters of the therapeutic agent, Ds: (A) 1 nm, (B) 20 nm, and (C) 60 nm at three time points: 6, 12, and 24 h. Drug concentration is normalized by division with the reference value entering the catheter (Image by Lambride et al. / CC BY 4.0 / Resized from original).


"Brain cancer therapy remains a formidable challenge in oncology. Convection-enhanced delivery (CED) is an innovative and promising local drug delivery method for the treatment of brain cancer, overcoming the challenges of the systemic delivery of drugs to the brain. To improve our understanding about CED efficacy and drug transport, we present an in silico methodology for brain cancer CED treatment simulation. To achieve this, a three-dimensional finite element formulation is utilized which employs a brain model representation from clinical imaging data and is used to predict the drug deposition in CED regimes. The model encompasses biofluid dynamics and the transport of drugs in the brain parenchyma. Drug distribution is studied under various patho-physiological conditions of the tumor, in terms of tumor vessel wall pore size and tumor tissue hydraulic conductivity as well as for drugs of various sizes, spanning from small molecules to nanoparticles. Through a parametric study, our contribution reports the impact of the size of the vascular wall pores and that of the therapeutic agent on drug distribution during and after CED. The in silico findings provide useful insights of the spatio-temporal distribution and average drug concentration in the tumor towards an effective treatment of brain cancer."

Use of Simpleware Software

"Magnetic resonance (MR) images of a healthy adult subject were used to create realistic FE model of the brain in 3D. The MR images were acquired from our previous study (Angeli and Stylianopoulos, 2016) and were reused in this work. Specifically, for the morphological imaging of the brain a T1-weighted, three-dimensional, fast field echo pulse sequence was acquired with an echo and repetition time of 3.2 and 7.1 ms respectively, while an isotropic voxel size of 1 mm was used to cover the entire brain. The commercial software ScanIP from Simpleware (version 6.0; Synopsys, Mountain View, United States) was employed for the three-dimensional reconstruction of the brain geometry. Specifically, two masks were first generated from the MR images using Simpleware’s “threshold” operation, which selects each pixel according to its brightness. The darkest areas comprise the mask of the gray matter, whereas the brightest regions comprise the mask of the white matter in the brain. These masks were the two different domains of the resulting 3D brain geometry. Then, the “island removal” and “cavity fill” operations were used to eliminate small unconnected parts of the masks and fill any gaps of the model, respectively. Additionally, smoothing was performed using Simpleware’s “Gaussian smoothing” operation. The 3D brain geometry was eventually created and exported in a COMSOL-compatible geometry file."

Outcomes and Impact

"To conclude, modifying the tumor microenvironment properties (e.g., by pharmaceutical interventions) prior to the drug administration through CED, may be suitable for effective drug delivery within the tumor, while simultaneously minimizing drug toxicity to the healthy brain tissue. Our in silico predictions provide further and useful insights of the spatial distribution and the drug concentration in the tumor towards improving brain cancer therapy. Based on our results, it is predicted that the chemotherapeutics (drug size: 1 nm) are diffused rapidly away from the tumor either through the blood vessels or from the tumor periphery, and thus, their average concentration in the tumor tissue is significantly lower compared to liposomes or other nanoparticles (drug size: >10 nm)."

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