Patient-specific Models Research Articles

Impact of Tissue Damage and Hemodynamics on Restenosis Following Percutaneous Transluminal Angioplasty: A Patient-Specific Multiscale Model.

Multiscale agent-based modeling frameworks have recently emerged as promising mechanobiological models to capture the interplay between biomechanical forces, cellular behavior, and molecular pathways underlying restenosis following percutaneous transluminal angioplasty (PTA). However, their applications are mainly limited to idealized scenarios. Herein, a multiscale agent-based modeling framework for investigating restenosis following PTA in a patient-specific superficial femoral artery (SFA) is proposed. The framework replicates the 2-month arterial wall remodeling in response to the PTA-induced injury and altered hemodynamics, by combining three modules: (i) the PTA module, consisting in a finite element structural mechanics simulation of PTA, featuring anisotropic hyperelastic material models coupled with a damage formulation for fibrous soft tissue and the element deletion strategy, providing the arterial wall damage and post-intervention configuration, (ii) the hemodynamics module, quantifying the post-intervention hemodynamics through computational fluid dynamics simulations, and (iii) the tissue remodeling module, based on an agent-based model of cellular dynamics. Two scenarios were explored, considering balloon expansion diameters of 5.2 and 6.2mm. The framework captured PTA-induced arterial tissue lacerations and the post-PTA arterial wall remodeling. This remodeling process involved rapid cellular migration to the PTA-damaged regions, exacerbated cell proliferation and extracellular matrix production, resulting in lumen area reduction up to 1-month follow-up. After this initial reduction, the growth stabilized, due to the resolution of the inflammatory state and changes in hemodynamics. The similarity of the obtained results to clinical observations in treated SFAs suggests the potential of the framework for capturing patient-specific mechanobiological events occurring after PTA intervention.

Abstract PO5-03-06: Optimizing therapeutic regimens via digital twins to improve triple negative breast cancer response to neoadjuvant therapy

Abstract Introduction: Neoadjuvant systemic therapy (NAT) has been the standard-of-care of stage II-III, locally advanced triple-negative breast cancer (TNBC). However, about 50% of TNBC patients achieve a pathological complete response (pCR) to conventional neoadjuvant chemotherapy (NAC)1. Recently approved combination of NAC with the immunotherapy pembrolizumab, has improved the pCR rate by 7.5%2, although with a 44% risk of immune-related adverse reaction3. Aside from the need to develop new therapies with higher efficacy and lower toxicity, a critical barrier to improving TNBC response is the lack of rigorous ways to personally tailor therapeutic regimens. We seek to address this challenge by employing digital twins (i.e., mathematical models that provide virtual representation of individual patients and predict the changes at future time points) to systematically evaluate individual TNBC patient’s response to different NAT regimens, thereby patient-specifically optimizing treatments. Methods: A TNBC cohort (n = 139) from the ARTEMIS trial (NCT02276433)4 was used for this study. All patients received 4 cycles of Adriamycin/Cytoxan (A/C) every 2 weeks, followed by 12 cycles of weekly Taxol or experimental therapy in Phase II trials. All patients had surgery after NAT and post-surgical pathology to assess response status. For each patient, longitudinal MRIs were collected before, during, and after A/C. We have developed digital twins to integrate the longitudinal MRIs with a mechanism-based model to accurately predict TNBC response5. The model was based on a reaction-diffusion equation that describes the change in tumor cellularity due to migration, proliferation, and drug-induced death. With parameters personalized using MRIs, the patient-specific model (i.e., digital twin) significantly improved the accuracy to predict pCR5. We investigated patient-specific treatment optimization on 37 (19 pCR, 18 non-pCR) chemo-sensitive patients (≥ 70% volume decrease after A/C) who received only NAC. We evaluated the effect of altering the A/C/Taxol schedules on patient response. Specifically, each patient’s digital twin was used to predict the patient’s response to 128 clinically reasonable schedules of A/C/Taxol; i.e., 8 candidate A/C schedules combining with 16 candidate Taxol schedules (Table 1). The predicted response (pCR or non-pCR) from each alternative schedule was compared to the patient response from the actual treatment. Results: Without changing the total dose, shortening the duration of A/C/Taxol administration increased the treatment efficacy. The effectiveness of altering the schedules varied substantially in different patients. In particular, 8 patients who had non-pCR responses to their actual treatment were predicted to achieve pCR with the dense-dose Taxol (i.e., 4 cycles Taxol, 2 weeks per cycle), indicating a 21.62% improvement of pCR rate in the cohort. Discussion and conclusion: The preliminary results with our digital twin approach provided a unique opportunity of improving TNBC response to NAT through patient-specific optimization of therapeutic schedules. The ongoing effort focuses on accounting for toxicity and investigating the effects of altering therapy types and doses in combination with the schedules on the patient response. [1] Spring et al., Clin Cancer Res, 2020. [2] Schmid et al., NEJM, 2022. [3] Shah et al., Clin Cancer Res, 2022. [4] Yam et al., Clin Cancer Res, 2021. [5] Wu et al., Cancer Res, 2022. Table 1. Candidate therapeutic schedules Citation Format: Chengyue Wu, Ernesto Lima, Casey Stowers, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane Rauch, Thomas Yankeelov. Optimizing therapeutic regimens via digital twins to improve triple negative breast cancer response to neoadjuvant therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-03-06.

Abstract PO1-07-12: Inverse modeling with surface temperature accurately detects the presence of breast cancer

Abstract Breast cancer affects over 250,000 women in the United States every year. Patient outcomes including overall survival significantly decline with large sized tumors and nodal involvement on initial presentation. The introduction of mammogram screening has been instrumental in the early detection of cancer. However, it has lower sensitivity in dense breast tissue and limited specificity which leads to unnecessary additional invasive testing. Several methods such as magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound imaging are used as adjunctive methods. However, there is still a need for cost-effective techniques without additional radiation exposure for breast cancer screening for use with mammography. We propose an adjunctive screening method using surface temperature measurements and inverse breast modeling for early detection of breath cancer. Methods: We enrolled patients who presented with mammogram detected breast tumors that were confirmed to be cancerous lesions by biopsy. We used prone position steady state infrared imaging of bilateral breasts to measure surface temperatures and multi-view MRI images to create patient specific 3D breast models. The inverse technique applies Levenberg-Marquard algorithms (LMA) and utilizes commercial software for thermal transport modeling. The inverse technique predicts a heat generation map normalized for baseline surface temperatures to localize the presence of an underlying tumor. Results: A total of 25 breast tumors (diameter range from 5 to 27 mm) from 24 patients were included in the analysis with a median patient age of 67 years. One patient had bilateral breast tumors. Mammogram data showed inclusion of mixed breast tissue composition including dense and extremely dense breast tissue. LMA accurately detected the tumor in all 25 patients with maximum absolute errors of 7 mm in location of the tumor in the breast and 1.78 mm in diameter. The accuracy of detection was not affected by tumor histology which included invasive ductal and lobular carcinomas, ductal and lobular carcinoma in-situ and atypical ductal hyperplasia. No heat sensitive tumors were detected in the 23 contralateral breasts which acted as internal negative controls. Conclusion: Infrared temperature profiles and inverse modeling successfully detected malignant breast tumors with no missed tumors or false positive results and could be used as adjunctive screening along with mammography, especially in patients with dense breast tissue. Citation Format: Nithya Sritharan, Carlos Gutierrez, Isaac Perez-Raya, Jose-Luis Gonzalez-Hernandez, Alyssa Owens, Donnette Dabydeen, Lori Medeiros, Satish Kandlikar, Pradyumna Phatak. Inverse modeling with surface temperature accurately detects the presence of breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-12.

Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees

Computational simulations of coronary artery blood flow, using anatomical models based on clinical imaging, are an emerging non-invasive tool for personalized treatment planning. However, current simulations contend with two related challenges – incomplete anatomies in image-based models due to the exclusion of arteries smaller than the imaging resolution, and the lack of personalized flow distributions informed by patient-specific imaging. We introduce a data-enabled, personalized and multi-scale flow simulation framework spanning large coronary arteries to myocardial microvasculature. It includes image-based coronary anatomies combined with synthetic vasculature for arteries below the imaging resolution, myocardial blood flow simulated using Darcy models, and systemic circulation represented as lumped-parameter networks. We propose an optimization-based method to personalize multiscale coronary flow simulations by assimilating clinical CT myocardial perfusion imaging and cardiac function measurements to yield patient-specific flow distributions and model parameters. Using this proof-of-concept study on a cohort of six patients, we reveal substantial differences in flow distributions and clinical diagnosis metrics between the proposed personalized framework and empirical methods based purely on anatomy; these errors cannot be predicted a priori. This suggests virtual treatment planning tools would benefit from increased personalization informed by emerging imaging methods.

Patient-specific closed-loop model of the Fontan circulation: calibration and validation

The Fontan circulation, designed for managing patients with a single functional ventricle, presents challenges in long-term outcomes. Computational methods offer potential solutions, yet their application in cardiology practice remains largely unexplored. Our aim was to assess a patient-specific, closed-loop, reduced-order blood flow model’s ability to simulate pulsatile blood flow in the Fontan circulation. Using one-dimensional models, we simulated the aorta, superior and inferior venae cavae, and right and left pulmonary arteries, while lumping heart chambers and remaining vessels into zero-dimensional models. The model was calibrated with patient-specific haemodynamic data from combined cardiac catheterisation and magnetic resonance exams, using a novel physics-based stepwise methodology involving simpler open-loop models. Testing on a 10-year-old, anesthetised patient, demonstrated the model’s capability to replicate pulsatile pressure and flow in the larger vessels and ventricular pressure. Average relative errors in mean pressure and flow were 2.9% and 3.6%, with average relative point-to-point errors (RPPE) in pressure and flow at 5.2% and 16.0%. Comparing simulation results to measurements, mean aortic pressure and flow values were 50.7 vs. 50.4 mmHg and 41.6 vs 41.9 ml/s, respectively, while ventricular pressure values were 28.7 vs. 27.4 mmHg. The model accurately described time-varying ventricular volume with a RPPE of 2.9%, with mean, minimum, and maximum ventricular volume values for simulation results vs. measurements at 59.2 vs. 58.2 ml, 38.0 vs 37.6 ml, and 76.0 vs. 74.4 ml, respectively. It provided physiologically realistic predictions of haemodynamic changes from pulmonary vasodilation and atrial fenestration opening. The new model and calibration methodology are freely available, offering a platform to virtually investigate the Fontan circulation’s response to clinical interventions and explore potential mechanisms of Fontan failure. Future efforts will concentrate on broadening the model’s applicability to a wider range of patient populations and clinical scenarios, as well as testing its effectiveness.

Data-driven modeling of interrelated dynamical systems

Non-linear dynamical systems describe numerous real-world phenomena, ranging from the weather, to financial markets and disease progression. Individual systems may share substantial common information, for example patients’ anatomy. Lately, deep-learning has emerged as a leading method for data-driven modeling of non-linear dynamical systems. Yet, despite recent breakthroughs, prior works largely ignored the existence of shared information between different systems. However, such cases are quite common, for example, in medicine: we may wish to have a patient-specific model for some disease, but the data collected from a single patient is usually too small to train a deep-learning model. Hence, we must properly utilize data gathered from other patients. Here, we explicitly consider such cases by jointly modeling multiple systems. We show that the current single-system models consistently fail when trying to learn simultaneously from multiple systems. We suggest a framework for jointly approximating the Koopman operators of multiple systems, while intrinsically exploiting common information. We demonstrate how we can adapt to a new system using order-of-magnitude less new data and show the superiority of our model over competing methods, in terms of both forecasting ability and statistical fidelity, across chaotic, cardiac, and climate systems.

A Biomimetic Leaflet Scaffold for Aortic Valve Remodeling.

Heart valve disease poses a significant clinical challenge, especially in pediatric populations, due to the inability of existing valve replacements to grow or respond biologically to their microenvironment. Tissue-engineered heart valves (TEHVs) provide a solution by facilitating patient-specific models for self-repair and remodeling. In this study, a 3D bioprinted TEHV is designed to emulate the tri-layer leaflet structure of an aortic valve. A cell-laden hydrogel scaffold made from gelatin-methacrylate and polyethylene glycol diacrylate (GelMA/PEGDA) incorporates valvular interstitial-like (VIC-like) cells, being reinforced with a layer of polycaprolactone (PCL). The composition of the hydrogel scaffold remains stable over 7 days, having increased mechanical strength compared to pure GelMA. The scaffold maintains VIC-like cell function and promotes extracellular matrix (ECM) protein expression up to 14 days under two dynamic culture conditions: shear stress and stretching; replicating heart valve behavior within a more physiological-like setting and suggesting remodeling potential via ECM synthesis. This TEHV offers a promising avenue for valve replacements, closely replicating the structural and functional attributes of a native aortic valve, leading to mechanical and biological integration through biomaterial-cellular interactions. This article is protected by copyright. All rights reserved.

Implications of Artificial intelligence in the form of 3D-printing in Endodontics: A Review

Three-dimensional (3D) printing has become an emerging technology in dentistry, and specifically in endodontics. This technology enables the creation of highly accurate and patient-specific 3D models, surgical guides, and custom-made instruments that have revolutionized endodontic treatment. This abstract provides an overview of the applications of 3D printing in endodontics, including preoperative planning, fabrication of surgical guides, production of custom-made instruments, and postoperative evaluation. The accuracy and precision of 3D-printed models have shown to improve the quality of endodontic treatment and reduce the risk of complications. The creation of new dental models is made possible by three-dimensional printing. 3D-printed teeth have been used in a lot of studies, however standards for standardised research are still being created. Although they have significant disadvantages, using real teeth is still the norm in ex vivo investigations and pre-clinical training. All the restrictions of natural teeth may be overcome by printed teeth. Printing technology uses 3D data and post-processing tools to create a 3D model, which is then used by 3D printers to create a prototype. The hardness of the resin and the correctness of the canal anatomy printing are the main issues with 3D-printed teeth. Future research is given direction in order to address the issues with 3D-printed teeth and create defined protocols in order to attain method standardisation. In the future, ex vivo investigations and endodontic training could use 3D-printed teeth as the gold standard. Furthermore, 3D printing has also facilitated communication between the dental team and the patient, leading to a better understanding of the treatment plan and enhancing the patient experience. This review intends to gather information regarding 3D-printed teeth on the following themes: (1) their benefits; (2) their manufacturing processes; (3) their issues; and (4) potential future research topics. In conclusion, 3D printing has the potential to enhance the accuracy and efficiency of endodontic treatment and improve clinical outcomes.

Computer patient-specific 3D modeling and custom-made guides for revision ACL surgery.

Revision Anterior cruciate ligament reconstruction (ACLR) is a challenging surgery occurring in 3-24% of primary reconstructions. A meticulous planning to study the precise size and location of both femoral and tibial bone tunnels is mandatory. The aim of the study was to evaluate the intra- and inter-operator differences in the decision-making process between experienced surgeons after they were asked to make pre-operative planning for ACL revision reconstruction with the use of both the CT scan and a 3D printed model of the knee. Data collected from 23 consecutive patients undergoing revision of ACLR for graft failure at a single Institute between September 2018 and February 2020 were prospectively reviewed. The double-blinded collected data were presented to three board-certificate attending surgeons. Surgeons were asked to decide whether to perform one-stage or two-stage revision ACLR based on the evaluation of the CT scan images and the 3D printed custom-made models at two different rounds, T0 and T1 respectively, seven days apart one from the other. Inter-operator consensus following technical mistake was 52% at T0 and 56% at T1 using the CT scans, meanwhile concordance was 95% at T0 and 94% at T1 using the 3D models. Concordance between surgeons following new knee injury was 66% at T0 and 70% at T1 using CT scans, while concordance was 96% both at T0 and T1 using 3D models. Intra-operative variability using 3D models was extremely low: concordance at T0 and T1 was 98%. McNamar test showed a statistical significance in the use of 3D model for preoperative planning (p < 0.005). 3D printed model reliability resulted to be higher compared to CT as intra-operator surgery technique selection was not modified throughout time from T0 to T1 (p < 0.005). The use of 3D printed models had the most impact when evaluating femoral and tibial tunnels, resulting to be a useful instrument during pre-operative planning of revision ACLR between attending surgeons with medium-high workflow.

Quantitative assessment and objective improvement of the accuracy of neurosurgical planning through digital patient-specific 3D models

ObjectiveNeurosurgical patient-specific 3D models have been shown to facilitate learning, enhance planning skills and improve surgical results. However, there is limited data on the objective validation of these models. Here, we aim to investigate their potential for improving the accuracy of surgical planning process of the neurosurgery residents and their usage as a surgical planning skill assessment tool.MethodsA patient-specific 3D digital model of parasagittal meningioma case was constructed. Participants were invited to plan the incision and craniotomy first after the conventional planning session with MRI, and then with 3D model. A feedback survey was performed at the end of the session. Quantitative metrics were used to assess the performance of the participants in a double-blind fashion.ResultsA total of 38 neurosurgical residents and interns participated in this study. For estimated tumor projection on scalp, percent tumor coverage increased (66.4 ± 26.2%–77.2 ± 17.4%, p = 0.026), excess coverage decreased (2,232 ± 1,322 mm2–1,662 ± 956 mm2, p = 0.019); and craniotomy margin deviation from acceptable the standard was reduced (57.3 ± 24.0 mm–47.2 ± 19.8 mm, p = 0.024) after training with 3D model. For linear skin incision, deviation from tumor epicenter significantly reduced from 16.3 ± 9.6 mm–8.3 ± 7.9 mm after training with 3D model only in residents (p = 0.02). The participants scored realism, performance, usefulness, and practicality of the digital 3D models very highly.ConclusionThis study provides evidence that patient-specific digital 3D models can be used as educational materials to objectively improve the surgical planning accuracy of neurosurgical residents and to quantitatively assess their surgical planning skills through various surgical scenarios.

Simulated Effects of Acute Left Ventricular Myocardial Infarction On Mitral Regurgitation in an Ovine Model.

Development of patient specific models of LV-MV interactions in ischemic mitral regurgitation (IMR) are complicated by the substantial variability and complex etiologies, making it difficult to extract underlying mechanisms. We thus developed a detailed ovine LV-MV finite element model based for the normal and acute post-MI states. The ovine LV FEM model was first extended to incorporate a detailed MV leaflet sub-model with a functionally-equivalent CT structure. The resulting complete LV-MV model was first used to simulate the full cardiac cycle in the normal state to establish baseline responses. We then simulated regional acute infarctions of varying sizes in various known infarct anatomical locations by shutting down the local myocardial contractility, while remote regions were allowed to adjust their active contractile patterns to maintain the prescribed pressure-volume loop. Simulation results indicated that the posterobasal infarct induced the largest MV regurgitation orifice area, consistent with experimental observations. We incorporated previously reported MV leaflet pre-strains (Amini et al., Ann Biomed Eng. 2012 Jul;40(7):1455-67) in the simulations, which then resulted in excellent agreement. Extensional deformations of the posterior leaflets occurred in the posterobasal and laterobasal infarcts, while compressive deformations of anterior leaflets were observed in the anterobasal infarct. The present study is the first detailed LV-MV simulations to reveal the important role of ,V EAF;ET pre-strain For accurate model prediction, underscoring that proper accounting of residual strains is an essential part of accurate simulations of organ level function.

A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments.

Cancer models play critical roles in basic cancer research and precision medicine. However, current in vitro cancer models are limited by their inability to mimic the three-dimensional architecture and heterogeneous tumor microenvironments (TME) of in vivo tumors. Here, we develop an innovative patient-specific lung cancer assembloid (LCA) model by using droplet microfluidic technology based on a microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition by evenly encapsulating patient tumor-derived TME cells and lung cancer organoids inside microgels. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. LCA model could reconstruct the functional heterogeneity of cancer-associated fibroblasts and reflect the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting the potential of the LCA model to predict personalized treatments. Collectively, our studies provide a valuable method for precisely fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.

Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms.

Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.

Impact of palatopharyngeal sizes changing on pharyngeal airflow fluctuation and airway vibration in a pediatric airway

Snoring is common in children and is associated with many adverse consequences. One must study the relationships between pharyngeal morphology and snoring physics to understand snoring progression. Although some model studies have provided fluid–structure interaction dynamic descriptions for the correlation between airway size and snoring physics, the descriptions still need to be further investigated in patient-specific airway models. Fluid-structure interaction studies using patient-specific airway structures complement the above model studies. Based on reported cephalometric measurement methods, this study quantified and preset the size of the palatopharynx airway in a patient-specific airway and investigated how the palatopharynx size affects the pharyngeal airflow fluctuation, soft palate vibration, and glossopharynx vibration with the help of a verified FSI method. The results showed that the stenosis anterior airway of the soft palate increased airway resistance and airway resistance fluctuations, which can lead to increased sleep effort and frequent snoring. Widening of the anterior airway can reduce airflow resistance and avoid obstructing the anterior airway by the soft palate vibration. The pharyngeal airflow resistance, mouth inflow proportion, and soft palate apex displacement have components at the same frequencies in all airway models, and the glossopharynx vibration and instantaneous inflow rate have components at the same frequencies, too. The mechanism of this same frequency fluctuation phenomenon can be explained by the fluid–structure interaction dynamics of an ideal coupled model consisting of a flexible plate model and a collapsible tube model. The results of this study demonstrate the potential of FSI in studying snoring physics and clarify to some degree the mechanism of airway morphology affecting airway vibration physics.

Experimental validation and clinical feasibility of 3D reconstruction of coronary artery bifurcation stents using intravascular ultrasound.

The structural morphology of coronary stents and the local hemodynamic environment following stent deployment in coronary arteries are crucial determinants of procedural success and subsequent clinical outcomes. High-resolution intracoronary imaging has the potential to facilitate geometrically accurate three-dimensional (3D) reconstruction of coronary stents. This work presents an innovative algorithm for the 3D reconstruction of coronary artery stents, leveraging intravascular ultrasound (IVUS) and angiography. The accuracy and reproducibility of our method were tested in stented patient-specific silicone models, with micro-computed tomography serving as a reference standard. We also evaluated the clinical feasibility and ability to perform computational fluid dynamics (CFD) studies in a clinically stented coronary bifurcation. Our experimental and clinical studies demonstrated that our proposed algorithm could reproduce the complex 3D stent configuration with a high degree of precision and reproducibility. Moreover, the algorithm was proved clinically feasible in cases with stents deployed in a diseased coronary artery bifurcation, enabling CFD studies to assess the hemodynamic environment. In combination with patient-specific CFD studies, our method can be applied to stenting optimization, training in stenting techniques, and advancements in stent research and development.

Tailoring epilepsy treatment: personalized micro physiological systems illuminate individual drug responses

Introduction: Approximately 50 million people worldwide have epilepsy, with many not achieving seizure freedom. Organ-on-chip technology, which mimics organ-level physiology, could revolutionize drug development for epilepsy by replacing animal models in preclinical studies. Our goal is to determine if customized micro-physiological systems can lead to tailored drug treatments for epileptic patients. Materials and methods: A comprehensive literature search was conducted utilizing various databases, including PubMed, Ebscohost, Medline, and the National Library of Medicine, using a predetermined search strategy. We focused on articles that addressed the role of personalized micro-physiological systems in individual drug responses and articles that discussed different types of epilepsy, diagnosis, and current treatment options. Additionally, articles that explored the components and design considerations of micro-physiological systems were reviewed to identify challenges and opportunities in drug development for challenging epilepsy cases. Results: The micro-physiological system offers a more accurate and cost-effective alternative to traditional models for assessing drug effects, toxicities, and disease mechanisms. Nevertheless, designing patient-specific models presents critical considerations, including the integration of analytical biosensors and patient-derived cells, while addressing regulatory, material, and biological complexities. Material selection, standardization, integration of vascular systems, cost efficiency, real-time monitoring, and ethical considerations are also crucial to the successful use of this technology in drug development. Conclusion: The future of organ-on-chip technology holds great promise, with the potential to integrate artificial intelligence and machine learning for personalized treatment for epileptic patients.

Influence of circle of Willis modeling on hemodynamic parameters in anterior communicating artery aneurysms and recommendations for model selection

Computational fluid dynamics (CFD) has been utilized to calculate hemodynamic parameters in anterior communicating artery aneurysm (AComA), which is located at a junction between left and right A1 and A2 segments. However, complete or half circle of Willis (CoW) models are used indiscriminately. This study aims to suggest recommendations for determining suitable CoW model. Five patient-specific CoW models with AComA were used, and each model was divided into complete, left-half, and right-half models. After validating the CFD using a flow experiment, the hemodynamic parameters and flow patterns in five AComAs were compared. In four out of five cases, inflow from one A1 side had a dominant influence on the AComA, while both left and right A1 sides affected the AComA in the remaining case. Also, the average difference in time-averaged wall shear stress between the complete and half models for four cases was 4.6%, but it was 62% in the other case. The differences in the vascular resistances of left and right A1 and A2 segments greatly influenced the flow patterns in the AComA. These results may help to enhance clinicians’ understanding of blood flow in the brain, leading to improvements in diagnosis and treatment of cerebral aneurysms.

Image-based biomarkers for engineering neuroblastoma patient-specific computational models

AbstractChildhood cancer is a devastating disease that requires continued research and improved treatment options to increase survival rates and quality of life for those affected. The response to cancer treatment can vary significantly among patients, highlighting the need for a deeper understanding of the underlying mechanisms involved in tumour growth and recovery to improve diagnostic and treatment strategies. Patient-specific models have emerged as a promising alternative to tackle the challenges in tumour mechanics through individualised simulation. In this study, we present a methodology to develop subject-specific tumour models, which incorporate the initial distribution of cell density, tumour vasculature, and tumour geometry obtained from clinical MRI imaging data. Tumour mechanics is simulated through the Finite Element method, coupling the dynamics of tumour growth and remodelling and the mechano-transport of oxygen and chemotherapy. These models enable a new application of tumour mechanics, namely predicting changes in tumour size and shape resulting from chemotherapeutic interventions for individual patients. Although the specific context of application in this work is neuroblastoma, the proposed methodologies can be extended to other solid tumours. Given the difficulty for treating paediatric solid tumours like neuroblastoma, this work includes two patients with different prognosis, who received chemotherapy treatment. The results obtained from the simulation are compared with the actual tumour size and shape from patients. Overall, the simulations provided clinically useful information to evaluate the effectiveness of the chemotherapy treatment in each case. These results suggest that the biomechanical model could be a valuable tool for personalised medicine in solid tumours.

Finite element modeling with patient-specific geometry to assess clinical risks of percutaneous pulmonary valve implantation.

Percutaneous pulmonary valve implantation (PPVI) is a non-surgical treatment for right ventricular outflow tract (RVOT) dysfunction. During PPVI, a stented valve, delivered via catheter, replaces the dysfunctional pulmonary valve. Stent oversizing allows valve anchoring within the RVOT, but overexpansion can intrude on the surrounding structures. Potentially dangerous outcomes include aortic valve insufficiency (AVI) from aortic root (AR) distortion and myocardial ischemia from coronary artery (CA) compression. Currently, risks are evaluated via balloon angioplasty/sizing before stent deployment. Patient-specific finite element (FE) analysis frameworks can improve pre-procedural risk assessment, but current methods require hundreds of hours of high-performance computation. We created a simplified method to simulate the procedure using patient-specific FE models for accurate, efficient pre-procedural PPVI (using balloon expandable valves) risk assessment. The methodology was tested by retrospectively evaluating the clinical outcome of 12 PPVI candidates. Of 12 patients (median age 14.5 years) with dysfunctional RVOT, 7 had native RVOT and 5 had RV-PA conduits. Seven patients had undergone successful RVOT stent/valve placement, three had significant AVI on balloon testing, one had left CA compression, and one had both AVI and left CA compression. A model-calculated change of more than 20% in lumen diameter of the AR or coronary arteries correctly predicted aortic valve sufficiency and/or CA compression in all the patients. Agreement between FE results and clinical outcomes is excellent. Additionally, these models run in 2-6 min on a desktop computer, demonstrating potential use of FE analysis for pre-procedural risk assessment of PPVI in a clinically relevant timeframe.

Patient-specific Advanced Visualization Modeling in Intracranial Aneurysms Featuring Anatomical, Clinical, and Demographic Features (P8-5.024)

Patient-specific Advanced Visualization Modeling in Intracranial Aneurysms Featuring Anatomical, Clinical, and Demographic Features (P8-5.024)