3D Liver Model Research Articles

Mathematical 3D Liver Model for Surgical versus Ablative Therapy Treatment Planning for Colorectal Liver Metastases: Recommendations from the COLLISION and COLDFIRE Trial Expert Panels.

Purpose To further define anatomic criteria for resection and ablation using an expert panel-based three-dimensional liver model to objectively predict local treatment recommendations for colorectal liver metastases (CRLM). Materials and Methods This study analyzed data from participants with small CRLM (≤3 cm) considered suitable for resection, thermal ablation, or irreversible electroporation (IRE), according to a multidisciplinary expert panel, who were included in two prospective multicenter trials (COLLISION [NCT03088150] and COLDFIRE-2 [NCT02082782]) between August 2017 and June 2022. Ten randomly selected participants were used to standardize the model's Couinaud segments. CRLM coordinates were measured and plotted in the model as color-coded lesions according to the treatment recommendations. Statistical validation was achieved through leave-one-out cross-validation. Results A total of 611 CRLM in 202 participants (mean age, 63 [range, 29-87] years; 138 male and 64 female) were included. Superficially located CRLM were considered suitable for resection, whereas more deep-seated CRLM were preferably ablated, with the transition zone at a subsurface depth of 3 cm. Ninety-three percent (25 of 27) of perihilar CRLM treated with IRE were at least partially located within 1 cm from the portal triad. Use of the model correctly predicted the preferred treatment in 313 of 424 CRLM (73.8%). Conclusion The results suggest that CRLM can be defined as superficial (preferably resected) and deep-seated (preferably ablated) if the tumor center is within versus beyond 3 cm from the liver surface, respectively, and as perihilar if the tumor margins extend to within 1 cm from the portal triad. Keywords: Ablation Techniques, CT, MRI, Liver, Abdomen/GI, Metastases, Oncology Supplemental material is available for this article. © RSNA, 2024.

Registration accuracy comparing different rendering techniques on local vs external virtual 3D liver model reconstruction for vascular landmark setting by intraoperative ultrasound in augmented reality navigated liver resection

PurposeAugmented reality navigation in liver surgery still faces technical challenges like insufficient registration accuracy. This study compared registration accuracy between local and external virtual 3D liver models (vir3DLivers) generated with different rendering techniques and the use of the left vs right main portal vein branch (LPV vs RPV) for landmark setting. The study should further examine how registration accuracy behaves with increasing distance from the ROI.MethodsRetrospective registration accuracy analysis of an optical intraoperative 3D navigation system, used in 13 liver tumor patients undergoing liver resection/thermal ablation.Results109 measurements in 13 patients were performed. Registration accuracy with local and external vir3DLivers was comparable (8.76 ± 0.9 mm vs 7.85 ± 0.9 mm; 95% CI = -0.73 to 2.55 mm; p = 0.272). Registrations via the LPV demonstrated significantly higher accuracy than via the RPV (6.2 ± 0.85 mm vs 10.41 ± 0.99 mm, 95% CI = 2.39 to 6.03 mm, p < 0.001). There was a statistically significant positive but weak correlation between the accuracy (dFeature) and the distance from the ROI (dROI) (r = 0.298; p = 0.002).ConclusionDespite basing on different rendering techniques both local and external vir3DLivers have comparable registration accuracy, while LPV-based registrations significantly outperform RPV-based ones in accuracy. Higher accuracy can be assumed within distances of up to a few centimeters around the ROI.

S24-04 3D thyroid and liver models: a holistic in vitro approach for mechanistic investigations of agrochemicals

S24-04 3D thyroid and liver models: a holistic in vitro approach for mechanistic investigations of agrochemicals

Estrogenic Responsiveness of Brown Trout Primary Hepatocyte Spheroids to Environmental Levels of 17α-Ethinylestradiol.

Three-dimensional (3D) fish hepatocyte cultures are promising alternative models for replicating in vivo data. Few studies have attempted to characterise the structure and function of fish 3D liver models and illustrate their applicability. This study aimed to further characterise a previously established spheroid model obtained from juvenile brown trout (Salmo trutta) primary hepatocytes under estrogenic stimulation. The spheroids were exposed for six days to environmentally relevant concentrations of 17α-ethinylestradiol-EE2 (1-100 ng/L). The mRNA levels of peroxisome (catalase-Cat and urate oxidase-Uox), lipid metabolism (acyl-CoA long chain synthetase 1-Acsl1, apolipoprotein AI-ApoAI, and fatty acid binding protein 1-Fabp1), and estrogen-related (estrogen receptor α-ERα, estrogen receptor β-ERβ, vitellogenin A-VtgA, zona pellucida glycoprotein 2.5-ZP2.5, and zona pellucida glycoprotein 3a.2-ZP3a.2) target genes were evaluated by quantitative real-time polymerase chain reaction. Immunohistochemistry was used to assess Vtg and ZP protein expressions. At the highest EE2 concentration, VtgA and ZP2.5 genes were significantly upregulated. The remaining target genes were not significantly altered by EE2. Vtg and ZP immunostaining was consistently increased in spheroids exposed to 50 and 100 ng/L of EE2, whereas lower EE2 levels resulted in a weaker signal. EE2 did not induce significant changes in the spheroids' viability and morphological parameters. This study identified EE2 effects at environmentally relevant doses in trout liver spheroids, indicating its usefulness as a proxy for in vivo impacts of xenoestrogens.

Abstract Mo089: Fontan-Associated Liver Disease On A Chip

Introduction: The Fontan procedure is performed in children with single ventricle physiology, and has led to excellent short-term palliation and survival rates but at the cost of long-term complications. Fontan-associated liver disease (FALD) results from hemodynamic changes following the Fontan procedure, leading to elevated central venous pressures, chronic alterations in hepatic perfusion, and resultant hepatic fibrosis. Improved understanding of FALD pathophysiology, which may provide insights for prevention and treatment is challenged by the lack of experimental models. Current in vitro liver models, mostly 2D or suspension hepatic cell cultures, lack biomimicry. This study presents a 3D bioprinted perfusable human liver model for in vitro study of hepatic fibrosis in response to altered flow conditions in FALD patients. Methods: Vascular 3D model of hepatic sinusoid was biofabricated using hybrid bioinks in two distinct peripheral layers and a central lumen. The outer layer was cast with hepatic (HepG2) cells encapsulated in a mixture of gelatin methacrylate (GelMA) and collagen type I. The inner layer consisted of hepatic stellate cells (HSCs) in GelMA. Human endothelial cells (ECs) were seeded onto the central lumen. 3D cellular constructs were cultured in vitro for three weeks in static or dynamic conditions, by circulating the culture media at varying flow conditions to simulate sinusoid pressure characteristic of healthy vs. disease state. Using Volcano system in cath lab, we invasively measured the pressure in mid-channel. Cell viability, proliferation, maturation into functional liver tissue, and tissue stiffness were evaluated via microindentation, flow hemodynamics assays, metabolomic assays, and immunohistochemistry analyses. Results: Bioengineered liver constructs cultured under static and dynamic conditions demonstrated high cell viability and growth and full endothelialization of the sinusoid channel. HepG2 cells exhibited long-term function and maturation, forming clusters. Under FALD flow conditions, there were notable changes in EC-HepG2 cell morphology compared to dynamic flow in the healthy range, accompanied by reduced levels of functional markers. Conclusion: This proof-of-principle study introduces an innovative and highly biomimetic 3D model of hepatic sinusoid, which can serve as a robust platform for in vitro investigations of complex cell-microenvironment interactions in the context of FALD as well as other hepatic disorders.

Development of a flexible liver phantom for hepatocellular carcinoma treatment planning: a useful tool for training & education

PurposeHepatocellular carcinoma (HCC) is one of the most common types of liver cancer that could potentially be surrounded by healthy arteries or veins that a surgeon would have to avoid during treatment. A realistic 3D liver model is an unmet need for HCC preoperative planning.MethodsThis paper presents a method to create a soft phantom model of the human liver with the help of a 3D-printed mold, silicone, ballistic gel, and a blender.ResultsFor silicone, the elastic modulus of seven different ratios of base silicone and silicone hardener are tested; while for ballistic gel, a model using 20% gelatin and 10% gelatin is created for the tumor and the rest of the liver, respectively. It is found that the silicone modulus of elasticity matches with the real liver modulus of elasticity. It is also found that the 10% gelatin part of the ballistic gel model is an excellent emulation of a healthy human liver.ConclusionThe 3D flexible liver phantom made from a 10% gelatin-to-water mixture demonstrates decent fidelity to real liver tissue in terms of texture and elasticity. It holds significant potential for improving medical training, preoperative planning, and surgical research. We believe that continued development and validation of such models could further enhance their utility and impact in the field of hepatobiliary treatment planning and education.

Impact of virtual reality education on disease-specific knowledge and anxiety for hepatocellular carcinoma patient scheduled for liver resection: a randomized controlled study.

Hepatocellular carcinoma (HCC) is a significant health concern, and the complexity of liver anatomy poses challenges in conveying radiologic findings and surgical plans to patients. This study aimed to evaluate the impact of a virtual reality (VR) education program on anxiety and knowledge in HCC patients undergoing hepatic resection. From 1 January 2022 to 28 February 2023, 88 patients were enrolled in a randomized controlled trial, divided into the VR group ( n =44) and the control group ( n =44). The VR group received patient-specific 3D liver model education through a VR platform, while the control group underwent conventional explanation processes. Both groups completed preintervention and postintervention questionnaires assessing anxiety (using STAI-X-1, STAI-X-2, and VAS) and knowledge about liver resection. Comparison of the questionnaires were performed between the two groups. Multivariable logistic regression was performed to analyze factor related to decrease in anxiety. While there was no significant difference in preintervention anxiety and knowledge scores between the two groups, the VR group exhibited significant reduction in STAI-X-1 scores (-4.14±7.5) compared to the control group (-0.84±5.7, P =0.023), as well as knowledge scores (17.20±2.6) compared to the control group (13.42±3.3, P <0.001). In the multivariable logistic regression model, VR education showed significant impact on decrease in STAI-X-1 score, postintervention. (OR=2.902, CI=1.097-7.674, P =0.032). The VR education program significantly improved knowledge and reduced anxiety among HCC patients compared to conventional methods. This study suggests that VR can be a valuable tool in patient education, enhancing comprehension and alleviating presurgical anxiety.

Biocompatibility evaluation of antioxidant cocktail loaded gelatin methacrylamide as bioink for extrusion-based 3D bioprinting

Three-dimensional (3D) liver bioprinting is a promising technique for creating 3D liver models that can be used for in vitro drug testing, hepatotoxicity studies, and transplantation. The functional performance of 3D bioprinted liver constructs are limited by the lack of cell–cell interactions, which calls for the creation of bioprinted tissue constructs with high cell densities. This study reports the fabrication of 3D bioprinted liver constructs using a novel photocrosslinkable gelatin methacrylamide (GelMA)-based bioink formulation. However, the formation of excess free radicals during photoinitiation poses a challenge, particularly during photocrosslinking of large constructs with high cell densities. Hence, we designed a bioink formulation comprising the base polymer GelMA loaded with an antioxidant cocktail containing vitamin C (L-ascorbic acid (AA)) and vitamin E (α-tocopherol (α-Toc)). We confirmed that the combination of antioxidants loaded in GelMA enhanced the ability to scavenge intracellular reactive oxygen species formed during photocrosslinking. The GelMA formulation was evaluated for biocompatibility in vitro and in vivo. These results demonstrated that the bioink had adequate rheological characteristics and was biocompatible. Furthermore, when compared to bioprinted constructs with lower cell density, high-density primary rat hepatocyte constructs demonstrated improved cell-cell interactions and liver-specific functions like albumin and urea secretion, which increased 5-fold and 2.5-fold, respectively.

Impact of patient-specific three-dimensional printed liver models on hepatic surgery safety: a pilot study

BackgroundSimulation and navigation technologies in hepatobiliary surgery have been developed recently. In this prospective clinical trial, we evaluated the accuracy and utility of our patient-specific three dimensional (3D)-printed liver models as an intraoperative navigation system to ensure surgical safety. MethodPatients requiring advanced hepatobiliary surgeries during the study period were enrolled. Three cases were selected for comparison of the computed tomography (CT) scan data of the models with the patients' original data. Questionnaires were completed after surgeries to evaluate the utility of the models. Psychological stress was used as subjective data and operation time and blood loss as objective data. ResultsThirteen patients underwent surgery using the patient-specific 3D liver models. The difference between patient-specific 3D liver models and the original data was less than 0.6 mm in the 90% area. The 3D model assisted with intra-liver hepatic vein recognition and the definition of the cutting line. According to the post-operative subjective evaluation, surgeons found the models improved safety and reduced psychological stress during operations. However, the models did not reduce operative time or blood loss. ConclusionThe patient-specific 3D-printed liver models accurately reflected patients' original data and were an effective intraoperative navigation tool for meticulously difficult liver surgeries. Clinical trial registrationThis study was registered in the UMIN Clinical Trial Registry (UMIN000025732).

In vitro construction of liver organoids with biomimetic lobule structure by a multicellular 3D bioprinting strategy

Liver disease is one of the serious threats to human life and health. Three-dimensional (3D) liver models, which simulate the structure and function of natural liver tissue in vitro, have become a common demand in medical, scientific and pharmaceutical fields nowadays. However, the complex cellular composition and multi-scale spatial arrangement of liver tissue make it extremely challenging to construct liver models in vitro. According to HepaRG preference and printing strategy, the formulation of bioink system with opposite charge is optimized. The sodium alginate-based bioink 1 and dipeptide-based bioink 2 are used to ensure structural integrity and provide flexible designability, respectively. The HepaRG/HUVECs/LX-2-laden liver organoids with biomimetic lobule structure are fabricated by a multicellular 3D droplet-based bioprinting strategy, to mimic the cell heterogeneity, spatial structure and extracellular matrix (ECM) features. The liver organoids can maintain structural integrity and multicellular distribution within the printed lobule-like structure after 7 days of culture. Compared with the 2D monolayer culture, the constructed 3D organoids show high cell viability, ALB secretion and urea synthesis levels. This study provides a droplet-based and layer-by-layer 3D bioprinting strategy for in vitro construction of liver organoids with biomimetic lobule structure, giving meaningful insights in the fields of new drugs, disease modelling, and tissue regeneration.

Three-Dimensional Cell Co-Culture Liver Models and Their Applications in Pharmaceutical Research.

As the primary site for the biotransformation of drugs, the liver is the most focused on organ type in pharmaceutical research. However, despite being widely used in pharmaceutical research, animal models have inherent species differences, while two-dimensional (2D) liver cell monocultures or co-cultures and three-dimensional (3D) liver cell monoculture in vitro liver models do not sufficiently represent the complexity of the human liver's structure and function, making the evaluation results from these tools less reliable. Therefore, there is a pressing need to develop more representative in vitro liver models for pharmaceutical research. Fortunately, an exciting new development in recent years has been the emergence of 3D liver cell co-culture models. These models hold great promise as in vitro pharmaceutical research tools, because they can reproduce liver structure and function more practically. This review begins by explaining the structure and main cell composition of the liver, before introducing the potential advantages of 3D cell co-culture liver models for pharmaceutical research. We also discuss the main sources of hepatocytes and the 3D cell co-culture methods used in constructing these models. In addition, we explore the applications of 3D cell co-culture liver models with different functional states and suggest prospects for their further development.

Functional Evaluation of 3D Liver Models Labeled with Polysaccharide Functionalized Magnetic Nanoparticles.

Establishing a rapid in vitro evaluation system for drug screening is essential for the development of new drugs. To reproduce tissues/organs with functions closer to living organisms, in vitro three-dimensional (3D) culture evaluation using microfabrication technology has been reported in recent years. Culture on patterned substrates with controlled hydrophilic and hydrophobic regions (Cell-ableTM) can create 3D liver models (miniature livers) with liver-specific Disse luminal structures and functions. MRI contrast agents are widely used as safe and minimally invasive diagnostic methods. We focused on anionic polysaccharide magnetic iron oxide nanoparticles (Resovist®) and synthesized the four types of nanoparticle derivatives with different properties. Cationic nanoparticles (TMADM) can be used to label target cells in a short time and have been successfully visualized in vivo. In this study, we examined the morphology of various nanoparticles. The morphology of various nanoparticles showed relatively smooth-edged spherical shapes. As 3D liver models, we prepared primary hepatocyte-endothelial cell heterospheroids. The toxicity, CYP3A, and albumin secretory capacity were evaluated in the heterospheroids labeled with various nanoparticles. As the culture period progressed, the heterospheroids labeled with anionic and cationic nanoparticles showed lower liver function than non-labeled heterospheroids. In the future, there is a need to improve the method of creation of artificial 3D liver or to design a low-invasive MRI contrast agent to label the artificial 3D liver.

Image-based deep learning of finite element simulations for fast surrogate biomechanical organ deformations

Abstract Introduction: We apply deep learning to emulate Finite Element simulations by exploiting neural networks as universal function approximators. A novel processing pipeline automates the incorporation of three-dimensional biomechanical simulation data into an image-based deep learning task where spatially resolved deformations are predicted using pixelwise regression. We show that a model trained in this way can quickly reproduce simulation results with reasonable preliminary performance. Methods: We trained a customized U-Net (32 input features) to predict displacement models based on the input force vectors. The pipeline was applied to a basic indentation process for an idealized reference geometry (puck) and a 3D liver model from the LiTS dataset. The generated 3D simulation data is normalized and projected onto 2D images using the midpoint circle algorithm such that x, y, and z coordinates are mapped to pixel values of RGB image channels. This way, 1500 simulations of the reference geometry and 500 simulations of the liver deformation with force-controlled, randomly initialized load distributions were performed. Simulations took about 2-5 minutes of computation time with FE models of 6000 and 10000 elements. The neural network was trained on the simulation database using pixelwise regression. Results: We successfully trained a model on the pixelwise regression of simulation results. The model optimization reached an f1- score of 0.69 and 0.61 for the puck and the liver. The displacement images were reprojected and compared to the 3D ground truth. The trained models reached an average surface distance of 3.1 ± 1.2 and 4.2 ± 2.2, as well as 4.3 ± 0.4 and 6.4 ± 0.2 for the Hausdorff distances. Conclusion: The model performance is still impractical for clinical decisions. Still, with mean computation times of under 250 ms, the approach's potential with a more extensive database and a more sophisticated deep learning architecture, e.g., GNN, is recognizable.

Point-of-use, automated fabrication of a 3D human liver model supplemented with human adipose microvessels.

Advanced in vitro tissue models better reflect healthy and disease tissue conditions in the body. However, complex tissue models are often manufactured using custom solutions and can be challenging to manufacture to scale. Here, we describe the automated fabrication of a cell-dense, thick (≤ 1 cm), human vascularized liver tissue model using a robotic biomanufacturing platform and off-the-shelf components to build, culture, and sample liver tissues hands-free without compromising tissue health or function. Fabrication of the tissue involved 3D bioprinting and incorporation of primary human hepatocytes, primary human non-parenchymal cells, and isolated fragments of intact human microvessels as vascular precursors. No differences were observed in select assessments of the liver tissues fabricated by hand or via automation. Furthermore, constant media exchange, via perfusion, improved urea output and elevated tissue metabolism. Interestingly, inclusion of adipose-derived human microvessels enhanced functional gene expression, including an enhanced response to a drug challenge. Our results describe the fabrication of a thick liver tissue environment useful for a variety of applications including liver disease modeling, infectious agent studies, and cancer investigations. We expect the automated fabrication of the vascularized liver tissue, at the point of use and using off-the-shelf platforms, eases fabrication of the complex model and increases its utility.

2D and 3D liver models

2D and 3D liver models

Long term maintenance of cytochrome P450 activity in a cell sheet-based three-dimensional human hepatic model

Primary human hepatocytes (PHH) are the gold standard of in vitro human liver model for drug screening. However, a problem of culturing PHH in vitro is the rapid decline of cytochrome P450 (CYP450) activity, which plays an important role in drug metabolism. In this study, thermo-responsive culture dishes were used to explore the conditions for murine embryonic 3T3-J2 fibroblasts to form cell sheet. Based on the cell sheet engineering technology, a three-dimensional (3D) "sandwich" co-culture system of 3T3-J2 cell sheet/PHH/collagen gel was constructed. The tissue structure and protein expression of the model section were observed by hematoxylin eosin staining and immunofluorescence staining respectively. Phenacetin and bupropion were used as substrates to determine the activity of CYP450. The contents of albumin and urea in the system were determined by enzyme linked immunosorbent assay (ELISA). The results showed that the complete 3T3-J2 cell sheet could be obtained when the cell seeding density was 1.5×106 /dish (35 mm dish) and the incubation time at low temperature was 60 min. Through cell sheet stacking, a 3D in vitro liver model was developed. Compared with the two-dimensional (2D) model, in the 3D model, the cell-cell and cell-matrix connections were tighter, the activities of cytochrome P450 CYP1A2 and cytochrome P450 CYP2B6 were significantly increased, and the secretion levels of albumin and urea were increased. These indexes could be maintained stably for 21 d. Therefore, cell sheet stacking is helpful to improve the level of liver function of 3D liver model. This model is expected to be used to predict the metabolism of low-clearance drugs in preclinical, which is of great significance for drug evaluation and other studies.

Automated Three-Dimensional Liver Reconstruction with Artificial Intelligence for Virtual Hepatectomy

ObjectiveTo validate the newly developed artificial intelligence (AI)-assisted simulation by evaluating the speed of three-dimensional (3D) reconstruction and accuracy of segmental volumetry among patients with liver tumors. BackgroundAI with a deep learning algorithm based on healthy liver computer tomography images has been developed to assist three-dimensional liver reconstruction in virtual hepatectomy. Methods3D reconstruction using hepatic computed tomography scans of 144 patients with liver tumors was performed using two different versions of Synapse 3D (Fujifilm, Tokyo, Japan): the manual method based on the tracking algorithm and the AI-assisted method. Processing time to 3D reconstruction and volumetry of whole liver, tumor-containing and tumor-free segments were compared. ResultsThe median total liver volume and the volume ratio of a tumor-containing and a tumor-free segment were calculated as 1035 mL, 9.4%, and 9.8% by the AI-assisted reconstruction, whereas 1120 mL, 9.9%, and 9.3% by the manual reconstruction method. The mean absolute deviations were 16.7 mL and 1.0% in the tumor-containing segment and 15.5 mL and 1.0% in the tumor-free segment. The processing time was shorter in the AI-assisted (2.1 vs. 35.0 min; p < 0.001). ConclusionsThe virtual hepatectomy, including functional liver volumetric analysis, using the 3D liver models reconstructed by the AI-assisted methods, was reliable for the practical planning of liver tumor resections.

From 2D Ultrasound to Patient-Specific 3D Surface Models for Interventional Guidance.

Ultrasound exam output of large organs like liver has traditionally been limited to still images or 2D cine loops of key structures, without 3D context. Having 3D context for follow up studies makes ultrasound scanning much easier and for interventional applications such as biopsy. 3D context will reduce wrong sample selection thereby increasing patient comfort. As of today, there is no existing solution which provides 3D anatomical context to users during scanning for large organs like liver. Even for routine measurements like liver volume, patients have to undergo CT or MR scan. In this paper, we propose a novel approach to build-patient specific 3D anatomical surface models from B-mode ultrasound images and tracking information from position sensors. The complexity of the problem stems from the fact that liver boundaries are often not very clear in ultrasound images, in addition to large variability in liver size and shape across patients. Our work uses state-of-the-art deep learning algorithms to detect surface landmarks of liver followed by registering a geometric model to surface point cloud to build patient specific 3D liver model. Further, the developed models will be used to guide users to right lesion locations during the interventional procedure. Our proposed semi -automated workflow ensures the accuracy of the developed models are within acceptable limits for the targeted problem.

Wound-Healing on Minimally Invasive Systems with Micro-Arc Oxidized (MAO) and Acid-Etched (SLA) Surface Modifications in Finite Element Liver Model

The purpose of this research is to study micro-arc oxidation (MAO) and acid etching (SLA) in the liver after minimally invasive treatment. The three-dimensional (3D) liver model is rebuilt by magnetic resonance imaging (MRI) to simulate the clinical process. The processing layer is an important factor in clinical applications. Several studies have investigated the finite element model of the liver; however, few people have studied the equipment model with treated layers. The results revealed that temperature was significantly decreased when devices using MAO and SLA thin films. In addition, the SLA treatment group showed a relatively low temperature, which was 12.88% lower. It is an effective means to reduce abnormally thermal injury and lateral thermal area. The present study reveals that the novel nanostructured thin film on electrosurgery devices is an effective means of improving the performance of reducing over thermal injury and uniformly distributing temperature in livers.

SAT078 - Recapitulating insulin resistance in a 3D human liver model for efficacy testing of drug candidates

SAT078 - Recapitulating insulin resistance in a 3D human liver model for efficacy testing of drug candidates