Patient-specific Basis Research Articles

Multi-physics simulations reveal hemodynamic impacts of patient-derived fibrosis-related changes in left atrial tissue mechanics.

Left atrial (LA) fibrosis is associated with arrhythmogenesis and increased risk of ischemic stroke; its extent and pattern can be quantified on a patient-specific basis using late gadolinium enhancement magnetic resonance imaging.Current stroke risk prediction tools have limited personalization, and their accuracy could be improved by incorporating patient-specific information like fibrotic maps and hemodynamic patterns.We present the first electro-mechano-fluidic multi-physics computational simulations of LA flow, including fibrosis and anatomies from medical imaging. Mechanical changes in fibrotic tissue impair global LA motion, decreasing LA and left atrial appendage (LAA) emptying fractions, especially in subjects with higher fibrosis burdens. Fibrotic-mediated LA motion impairment alters LA and LAA flow near the endocardium and the whole cavity, ultimately leading to more stagnant blood regions in the LAA.

A Novel Stem Cell Model to Study Preeclampsia Endothelial Dysfunction

Preeclampsia is a common pregnancy complication affecting 5% to 7% of all pregnancies worldwide annually. While the pathogenesis is not fully understood, maternal endothelium dysfunction is thought to be a central component to preeclampsia development. Studies to dissect maternal endothelial dysfunction, particularly on a patient-specific basis, are hampered by limited access to systemic primary endothelial cells (ECs). The objective of this study was to establish a replenishable, patient-specific in vitro EC model to allow robust mechanistic studies to dissect endothelial dysfunction in preeclampsia. Induced pluripotent stem cells (iPSCs) from three women with a history of normotensive pregnancies were differentiated into ECs. The established ECs were exposed to pooled sera from normotensive pregnancies, preeclamptic pregnancies, normotensive postpartum for non-pregnant comparison and controls. Endothelial functions including nitric oxide (NO) release, cell migration, tube formation and viability were evaluated. Levels of NO release were significantly lower after incubation with preeclamptic sera compared to the fetal bovine serum (FBS) control, and normotensive and non-pregnant (postpartum) sera treatments were also lower than FBS but higher than preeclamptic sera treatments. Tube formation and cell migration were also impaired with preeclamptic sera compared to FBS controls. Cell viabilities remained unaffected by any sera treatment. Consistent outcomes were obtained across all three patient-specific lines treated with the same pooled sera. Establishment of patient-derived iPSC-ECs treated with pregnancy sera serves as a novel model to explore the interplay between individual maternal endothelial health and circulating factors that lead to endothelial dysfunction in preeclampsia.

Fixed dose daptomycin: An opportunity for pharmacokinetic/pharmacodynamic optimization in Staphylococcus aureus infections.

Daptomycin is a high-use intravenous antimicrobial agent affording the convenience of once-daily dosing. Prior studies suggest an opportunity to use a more operationally convenient fixed rather than weight-based dosing but this approach has not been studied prospectively. This study quantified the probability of toxicity and efficacy end points by prospectively testing a fixed dose regimen of daptomycin (750 mg) in obese and non-obese adults. At least, three daptomycin concentrations were measured at steady-state for each patient. A population pharmacokinetic model was constructed to evaluate concentration-time profiles and investigate covariates of daptomycin clearance. Simulations were performed to evaluate the probability of achieving efficacy (24-h area under the curve (AUC0-24) ≥ 666 mg∙h/L) and toxicity (minimum concentration (C min) ≥24.3 mg/L) targets for fixed (500-1000 mg) and weight-based (6-12 mg/kg) daptomycin doses. Thirty-one patients (16 females, 15 males) with median (interquartile range (IQR)) age of 50 (30, 62) years and weight of 74 (54, 156) kg were included in the final analysis. Fixed dose daptomycin (750 mg) resulted in similar exposure across weights with a median (IQR) AUC0-24 of 819 (499, 1501) mg∙h/L and 749 (606, 1265) mg∙h/L in patients weighing ≤74 kg and >74 kg, respectively. Overall, male sex and increased kidney function necessitate higher fixed and weight-based doses to achieve efficacy. Creatine phosphokinase elevation was observed in two patients (6.5%) and predicted to be lower with fixed versus weight-based regimens. Fixed daptomycin dosing adjusted for sex and kidney function is expected to improve the efficacy-to-toxicity ratio, transitions of care, and costs compared to weight-based doses. However, no empiric dosing approach is predicted to achieve ≥90% efficacy while minimizing the risk of toxicity, so therapeutic drug monitoring should be considered on a patient-specific basis.

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data.

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

Influence of the flow split ratio on the position of the main atrial vortex: Implications for stasis on the left atrial appendage

Background:Despite the recent advances in computational fluid dynamics (CFD) techniques applied to blood flow within the left atrium (LA), the relationship between atrial geometry, flow patterns, and blood stasis within the left atrial appendage (LAA) remains unclear. A better understanding of this relationship would have important clinical implications, as thrombi originating in the LAA are a common cause of stroke in patients with atrial fibrillation (AF). Aim:To identify the most representative atrial flow patterns on a patient-specific basis and study their influence on LAA blood stasis by varying the flow split ratio and some common atrial modeling assumptions. Methods:Three recent techniques were applied to nine patient-specific computational fluid dynamics (CFD) models of patients with AF: a kinematic atrial model to isolate the influence of wall motion because of AF, projection on a universal LAA coordinate system, and quantification of stagnant blood volume (SBV). Results:We identified three different atrial flow patterns based on the position of the center of the main circulatory flow. The results also illustrate how atrial flow patterns are highly affected by the flow split ratio, increasing the SBV within the LAA. As the flow split ratio is determined by the patient’s lying position, the results suggest that the most frequent position adopted while sleeping may have implications for the medium- and long-term risks of stroke.

A gray zone: Role of transesophageal echocardiography before atrial tachycardia catheter ablation.

There is a lack of studies in the literature directly investigating the relationship between atrial tachycardia (AT) and left atrial (LA)/left atrial appendage (LAA) thrombus, and current guidelines do not provide strong recommendations regarding the use of transesophageal echocardiography (TEE) before AT catheter ablation. This study aims to elucidate the relationship between AT and the presence of LA/LAA thrombus and contribute to the literature on the use of TEE before AT catheter ablation. This single-center retrospective observational study screened patients who underwent TEE between February 10, 2019, and February 10, 2023. Patients were assigned to the AT patient and control groups. TEE was conducted to exclude thrombus in the AT ablation group. The control group included patients who underwent TEE for interatrial septum evaluation and had LA imaging during TEE but did not have atrial arrhythmia. To mitigate bias between the AT patient group and the control group, they were randomized 1:1 using propensity-score matching (PSM). Following randomization, each group consisted of 49 patients. All analyses were conducted after PSM. There were no statistically significant differences between the AT patient and control groups in terms of baseline clinical characteristics and echocardiographic features. Additionally, no significant differences were found between the blood viscosities calculated at low and high shear rates in both groups. The study revealed a significant difference between the two groups in the presence of LA spontaneous echo contrast (SEC) (24.5% in AT group vs 0% in Control group, p = .001), but not in the presence of thrombi (8.2% in AT group vs 0% in Control group, p = .117). Compared to the control group, the presence of SEC was significantly higher in the AT patient group. The increased frequency of SEC in AT patients suggests the hypothesis that AT may contribute to LA stasis. The routine use of TEE before AT catheter ablation remains controversial, despite the presence of LA thrombus and SEC in the AT patient group. The clinical assessment of thrombus presence before the procedure must be conducted on a patient-specific basis.

Multimodal radiotherapy dose prediction using a multi-task deep learning model.

In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt-60-based systems and linear accelerators with C-arm, O-ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality-specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient-specific basis. However, manually generating treatment plans for each modality across every patient is time-consuming and clinically impractical. We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning-based convolutional neural networks. The baseline network performs a single-task (ST), predicting dose for a single modality. Our proposed multi-task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient-specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient-specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning. The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two-tailed Wilcoxon signed-rank test. Training times for five ST models ranged from 255 to 430min per modality, totaling 1925min, while the MT model required 2384min. MT model prediction required an average of 1.82s per patient, compared to ST model predictions at 0.93s per modality. The MT model yielded MAPE of 1.1033±0.3627% as opposed to the collective MAPE of 1.2386±0.3872% from ST models, and the differences were statistically significant (p=0.0003, 95% confidence interval=[-0.0865, -0.0712]). Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre-treatment decision-making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site-agnostic dose prediction model, enhancing its generalizability and applicability.

Abstract PO5-07-03: Integrating mechanism-based and data-driven modeling to predict the response of triple negative breast cancer to therapy

Abstract Introduction Neoadjuvant chemotherapy (NAC) is the standard-of-care (SOC) for patients with locally advanced triple-negative breast cancer (TNBC) [1]. Unfortunately, only about half of TNBC patients achieve a pathological complete response (pCR) at the completion of NAC [2]. With the recent FDA approval of immunotherapy, the response rate improved by about 8% [3]. To try to further improve NAC response rates, we build upon methods that predict treatment response to optimize interventions and outcomes. Our previous studies have demonstrated that excellent predictive accuracy of TNBC response to NAC was achieved through calibrating mechanism-based models to both pre- and on- treatment imaging data [4]. However, by requiring on-treatment imaging data, therapy optimization can only occur after a patient begins NAC. Here, our goal is to relax the requirement of on-treatment imaging data such that we can predict, and potentially optimize, a patient’s response to NAC before initiating it. We will accomplish this through combining data-driven approaches with mechanism-based modeling to obtain spatiotemporal predictions of tumor response prior to NAC. More specifically, we integrate a reaction-diffusion equation based mathematical model with a U-Net based convolutional neural network (CNN). Methods The reaction-diffusion equation models the development of tumor cellularity over time as the sum of tumor cell diffusion, controlled by a fixed diffusivity, and tumor cell proliferation, controlled by a calibrated net proliferation rate. The net proliferation rate implicitly accounts for both proliferative and drug induced death effects. We calibrated the net proliferation rates in this model on a patient-specific basis for 128 patients from the ARTEMIS trial (NCT02276443) using three imaging timepoints – pre- (V1), post two cycles (V2), and post four cycles (V3) of A/C. We then employed a CNN to characterize the relationship between the calibrated net proliferation rates and the pre-treatment imaging data. The CNN takes pretreatment imaging inputs of the apparent diffusion coefficient map, percent enhancement maps from the dynamic contrast enhanced MRI timecourse, and precontrast T1 map. The CNN outputs an estimate of the calibrated net proliferation rate which can be used to run the mathematical model forward in time to predict cellularity maps at V2/3. In training the CNN, our loss considers both the concordance correlation coefficient (CCC) and the normalized root mean square error (NRMSE). We calculated loss on the net proliferation rate predictions as NRMSE with a CCC penalty to aid in characterizing the non-Gaussian distribution of the net proliferation rate. We calculated loss on the cellularity predictions as the CCC between predicted and measured voxel-wise cellularity. The CNN was trained using the Adam optimizer and a fivefold cross validation with a withheld test set of 26 patients. Results Using the CNN predictions to forward run the mechanism-based model in time to V3 we calculated change in total tumor cellularity and volume from V1 to V3. In the test cohort, we obtained CCC values between the predicted and measured changes of 0.95 and 0.91 for cellularity and volume, respectively. Discussion and summary Through integrating mechanistic modeling and deep learning, we can accurately predict the spatiotemporal development of TNBC response to NAC using only pretreatment imaging data. We will build upon these results by expanding our model to include separate proliferation and drug induced death terms to allow for therapy intervention and optimization. Additionally, we will incorporate other pretreatment data types including genetic data. [1] Liedtke et al., J of Clin Oncol, 2008. [2] Spring et al., Clin Cancer Res, 2020. [3] Schmid et al., NEJM, 2020. [4] Wu et al., Cancer Res, 2022. Citation Format: Casey Stowers, Chengyue Wu, Sidharth Kumar, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan Tamir, Gaiane Rauch, Thomas Yankeelov. Integrating mechanism-based and data-driven modeling to predict the response of triple negative breast cancer to 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 PO5-07-03.

Practical Aspects of Acne Scar Management: ASAP 2024.

Acne scars are one of the most common complications of acne. They can significantly affect the patient's quality of life. Often, several types of atrophic acne scars are observed simultaneously; therefore, consideration must be given to the type of scar while choosing the treatment modality. Effective treatment is not only important to prevent and improve acne scars but also crucial in preventing psychosocial effects. Treatment of acne scars requires an algorithmic approach that targets each component of the scars, and combination therapy on a patient-specific basis may offer the best chance for significant improvement. The goal of the current article is to discuss the practical aspects of management of atrophic acne scars using the vast modalities of treatment available. The panel of dermatologists and plastic surgeons, each one with at least 20 years of experience in acne scar treatment, participated in a series of 'Practical Aspects of Acne Scar Management' (ASAP) meetings: ASAP 2024. ASAP meetings were organized by "Scar Forum India" from March 2023 to July 2023 in four Indian cities (Mumbai, Delhi, Bengaluru, and Kolkata), each one for a duration of at least three hours. During these meetings and discussions, panelists reviewed and discussed the acne scar-related literature, their clinical experience in its management, available treatment options, along with recent advances. Consequently, a summary of the discussion and practical approach for the management of acne scars is developed. It was concluded that, though there is no specific guideline available to optimize acne scar management despite the multitude of treatment options, the best results can be achieved through the synergy of multiple treatment modalities and using the algorithmic approach.

Biomimicking Atherosclerotic Vessels: A Relevant and (Yet) Sub-Explored Topic.

Atherosclerosis represents the etiologic source of several cardiovascular events, including myocardial infarction, cerebrovascular accidents, and peripheral artery disease, which remain the leading cause of mortality in the world. Numerous strategies are being delineated to revert the non-optimal projections of the World Health Organization, by both designing new diagnostic and therapeutic approaches or improving the interventional procedures performed by physicians. Deeply understanding the pathological process of atherosclerosis is, therefore, mandatory to accomplish improved results in these trials. Due to their availability, reproducibility, low expensiveness, and rapid production, biomimicking physical models are preferred over animal experimentation because they can overcome some limitations, mainly related to replicability and ethical issues. Their capability to represent any atherosclerotic stage and/or plaque type makes them valuable tools to investigate hemodynamical, pharmacodynamical, and biomechanical behaviors, as well as to optimize imaging systems and, thus, obtain meaningful prospects to improve the efficacy and effectiveness of treatment on a patient-specific basis. However, the broadness of possible applications in which these biomodels can be used is associated with a wide range of tissue-mimicking materials that are selected depending on the final purpose of the model and, consequently, prioritizing some materials' properties over others. This review aims to summarize the progress in fabricating biomimicking atherosclerotic models, mainly focusing on using materials according to the intended application.

Characterizing cardiac contractile motion for noninvasive radioablation of ventricular tachycardia

BackgroundRespiratory motion management strategies are used to minimize the effects of breathing on the precision of stereotactic ablative radiotherapy for ventricular tachycardia, but the extent of cardiac contractile motion of the human heart has not been systematically explored. ObjectiveWe aim to assess the magnitude of cardiac contractile motion between different directions and locations in the heart. MethodsPatients with intracardiac leads or valves who underwent 4D cardiac computed tomography (4DCT) prior to a catheter ablation procedure for atrial or ventricular arrhythmias at 2 medical centers were studied retrospectively. The displacement of transvenous right atrial appendage (RA), right ventricle (RV) ICD, coronary sinus (CS) lead tips and prosthetic cardiac devices across the cardiac cycle were measured in orthogonal 3D views on a maximal-intensity projection CT reconstruction. ResultsA total of 31 pre-ablation cardiac 4DCTs were analyzed. The LV lead tip had significantly greater motion compared to the RV lead in the anterior-posterior direction (6.0±2.2mm vs 3.8±1.7mm; p=0.01) and superior-inferior direction (4.4±2.9mm vs 3.5±2.0mm; p=0.049). The prosthetic aortic valves had the least movement of all fiducials, specifically compared to the RV lead tip in the left-right direction (3.2±1.2mm vs 6.1±3.8mm, p=0.04) and the LV lead tip in the antero-posterior direction (3.8±1.7mm vs 6.0±2.2mm, p=0.03). ConclusionThe degree of cardiac contractile motion varies significantly (1mm to 15.2 mm) across different locations in the heart. The effect of contractile motion on the precision of radiotherapy should be assessed on a patient-specific basis.

The Role of Facial Fillers in Non-Surgical Rhinoplasty: A Systematic Review of Safety and Efficacy

Purpose: Facial fillers, also alluded to as dermal fillers, consist of injectable chemicals as portion of a minimally invasive strategy to incidentally enhance the aesthetic and appearance of facial highlights by correcting volumes. Non-surgical rhinoplasty, also called liquid rhinoplasty, includes the utilize of dermal fillers to improve the shape of the nose. Non-surgical rhinoplasty is picking up popularity over the surgical choice on account of its ease of application, cheaper cost, better safety profile, less adverse effects, and faster recuperation time. Nevertheless, with the complex nasal arterial vasculature and structure, complications have been detailed within the literature. In this review, we examine the distributed applications of facial fillers in non-surgical rhinoplasty and its viability and security profile as compared to the surgical alternative.
 Materials and Methods: A comprehensive literature review of articles on facial fillers in non-surgical rhinoplasty was performed. Search results were filtered to incorporate articles and studies that were distributed within the last five years, to increase the relevance of the results to current practices. Search results were then reviewed for significance to the utilize of facial or dermal fillers in non-surgical rhinoplasty only. Studies that detailed the role of face fillers in areas unrelated to rhinoplasty and nose defects were excluded.
 Findings: Seven articles met the inclusion criteria and discussed the role, security and viability of facial or dermal fillers in non-surgical rhinoplasty. Of these, three were designed to illustrate particular techniques embraced in non-surgical rhinoplasty as well as the common uses of diverse and common fillers. The remaining three reviewed the safety and viability of particular filler sorts: one article inspected the use of Hyaluronic corrosive based dermal fillers in non-surgical rhinoplasty, another reviewed the strategy of PMMA-collagen gel in non-surgical rhinoplasty absconds, and finally, a study analyzed the utilize of a hybrid nasal filler which combined agarose gel and hyaluronic corrosive. The remaining article from the literature reviewed a case from a patient who detailed persistent headache and migraine-like indications following non-surgical rhinoplasty.
 Conclusions: Facial fillers are compelling and safe options for patients who wish to rectify or enhance their nasal shape or form, without going for surgery. Although the results are not as permanent as with the surgical alternative, non-surgical rhinoplasty offers a better recovery time, and cheaper cost. Given the distinctive properties ascribed to each filler type, and the diverse cases of nasal deformities, the filler choice and area should be chosen on a patient-specific basis. Compared to surgical rhinoplasty, the complications and adverse occasions from the non-surgical injections are as a rule mild and self-limiting. While severe complications are uncommon, deep knowledge of the nasal anatomy and vasculature, as well as extensive training, stay the main ways to minimize their event and increase patient security and satisfaction.
 Implications to Theory, Practice and Policy: Severe complications are uncommon, deep knowledge of the nasal anatomy and vasculature, as well as extensive training, remain the main ways to minimize their event and increase patient safety and fulfillment. In any case, more studies are required to clearly establish the safety of dermal fillers, particularly in the long-term.

STEM-02. SYNGN: A NOVEL EPIGENETIC PLATFORM FOR PATIENT-SPECIFIC DRUG-MATCHING IN GLIOBLASTOMA

Abstract The alteration of the glioblastoma genome by epigenetic mechanisms that share functions with normal developmental processes, such as self-renewal and fate specification of NSC, is a key piece of evidence that links brain cancer pathogenesis with dysregulated stem cell functions. A patient-specific comparison of glioblastoma cells with NSC, a putative cell of origin of at least a proportion of these tumours, is not feasible as patient-matched endogenous NSC are not surgically accessible and all epigenetic studies in glioblastoma have so far compared epigenetic changes of different tumours with each other or to comparators obtained from foetal brains or an unrelated donor. We reasoned that availability of syngeneic GSC and NSC pairs would allow to identify crucial epigenetic differences on a patient-specific basis and would provide essential therapeutic contrast to define disease-and patient-intrinsic biomarkers of drug response that are less confounded by germline variation. We have derived GSC from IDH-wildtype glioblastoma and harnessed state-of-the-art stem cell technologies to generate patient-matched fibroblast-derived EPSC, which were induced to NSC (iNSC). We demonstrate that integrated analysis of the transcriptome and DNA methylome of GSC/iNSC pairs identifies druggable target genes (PTGER4, ALDH3B1, NTRK2) in a proportion of patients and we validate this patient-specific prediction of drug response at pre-clinical level in 3D synGLICO and in in vivo models. Integration of small RNA regulation to the pipeline allows refined in silico prediction of drug responses on a patient-specific basis. Most recently we have added assessment of chromatin remodelling (H3K4me3, H3K27ac, H3K3me3, H3K27me3 and ATACSeq) to the pipeline and shown that redistribution of selected histone marks as well as shifts in chromatin states across the genome identifies known and novel druggable regulatory mechanisms in GSC, which are specific of the neoplastic context. In conclusion, SYNGN is an epigenetic platform which can identify patient-specific druggable targets in glioblastoma.

Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics

Patient-specific finite element analysis (FEA) holds great promise in advancing the prognosis of cardiovascular diseases by providing detailed biomechanical insights such as high-fidelity stress and deformation on a patient-specific basis. Albeit feasible, FEA that incorporates three-dimensional, complex patient-specific geometry can be time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) models, e.g., deep neural networks (DNNs), have been increasingly utilized as potential alternatives to finite element method (FEM) for biomechanical analysis. So far, efforts have been made in two main directions: (1) learning the input-to-output mapping of traditional FEM solvers and replacing FEM with data-driven ML surrogate models; (2) solving equilibrium equations using physics-informed loss functions of neural networks. While these two existing strategies have shown improved performance in terms of speed or scalability, ML models have not yet provided practical advantages over traditional FEM due to generalization issues. This has led us to the question: instead of abandoning or replacing the traditional FEM framework that can reliably solve biomechanical problems, can we integrate FEM and DNNs to enhance performance?In this study, we propose a synergistic integration of DNNs and FEM to overcome their individual limitations. Using biomechanical analysis of the human aorta as the test bed, we demonstrated two novel integrative strategies in forward and inverse problems. For the forward problem, we developed DNNs with state-of-the-art architectures to predict a nodal displacement field, and this initial DNN solution was then updated by a FEM-based refinement process, yielding a fast and accurate computing framework. For the inverse problem of heterogeneous material parameter identification, our method employs DNN as a regularizer of the spatial distribution of material parameters, aiding the optimizer in locating the optimal solution. In our demonstrative examples, despite that the DNN-only forward models yielded small displacement errors in most test cases; stress errors were considerably large, and for some test cases, the peak stress errors were greater than 50%. Our DNN-FEM integration eliminated these non-negligible errors in DNN-only models and was magnitudes faster than the FEM-only approach. Additionally, compared to FEM-only inverse method with errors greater than 50%, our DNN-FEM inverse approach significantly improved the parameter identification accuracy and reduced the errors to less than 1%.

Trajectory Planning Software for Deep Brain Stimulation Driven by Patient-Specific Data

Abstract Deep brain stimulation (DBS) is a treatment for several neurological disorders including Parkinson's disease, essential tremor, and epilepsy. The neurosurgical procedure involves implanting a lead of electrodes to a deep brain target and thereafter electrically stimulating that target to suppress symptoms. To reduce the probability of intracranial bleeding during implantation, neurosurgeons carefully plan out a patient-specific lead trajectory that avoids passing the lead through regions with major blood vessels. This process can be tedious, and there is a need to provide neurosurgeons with a more efficient and quantitative means to identify major blood vessels on a patient-specific basis. Here, we developed a modular graphical user interface (GUI) containing anatomically segmented digital reconstructions of patient vasculature, cortex, and deep brain target anatomy from preoperative high-field (3T and 7T) MRI. The system prompts users to identify the deep brain target, and then algorithmically calculates a log-scale blood vessel density along the length of potential lead trajectories that pivot around the deep brain target. Heatmaps highlighting regions with low blood vessel density were calculated for cortical and subcortical vasculature models. The modeling framework enabled users to further interact with the models by panning, rotating, zooming, showing, or hiding the various anatomical reconstructions and heatmaps. Providing surgeons with quantitative, patient specific vasculature data has the potential to further reduce the likelihood of hemorrhage events during microelectrode mapping and DBS lead implantation.

IMPLEMENTATION AND EVOLUTION OF THE REGIONAL REVISION KNEE ARTHROPLASTY MULTIDISCIPLINARY TEAM MEETING IN SOUTH WEST LONDON

AbstractMultidisciplinary team (MDT) meetings in orthopaedic surgery are evolving. The goals include patient optimisation, surgical planning and discharge arrangements, individually on a patient specific basis. In line with British Association Surgery of the Knee and GIRFT guidelines, we report our regional experience on the implementation and evolution of our regional Revision Knee Arthroplasty MDT.We undertook a retrospective review of the process, cases discussed and quality assurance conducted in the weekly MDT.Since implementation of the MDT meeting in January 2019, 550 patients with painful joint replacements have been discussed. In May 2021, we formalised our ‘terms of reference’ and ‘standard operating procedures’ which are adhered to by the consultants operating within the network, all of whom attend the meeting. A proforma has been developed and is completed for all cases capturing all pertinent information. All cases are graded according to R1, R2, or R3 complexity scale. Since its inception, the MDT has looked at all post-operative imaging, we now describe a new classification system for grading the appearances. Confirmation of the pre-operative plan and actual surgical intervention is reviewed and audited. In addition, confirmation of indication of revision at the time of operation is reviewed and validated for accuracy with benefits to the unit, individual surgeon and the NJR.In conclusion, the implementation of the revision MDT facilitating pre-operative revision arthroplasty discussion and post-operative quality assurance processes enable surgeons to educate, reflect on their practice and ensure that the highest standards of care are being provided.

Parameter based 4D dose calculations for proton therapy.

Background and purpose Retrospective log file-based analysis provides the actual dose delivered based on the patient's breathing and the daily beam-delivery dynamics. To predict the motion sensitivity of the treatment plan on a patient-specific basis before treatment start a prospective tool is required. Such a parameter-based tool has been investigated with the aim to be used in clinical routine. Materials and Methods 4D dose calculations (4DDC) were performed for seven cancer patients with small breathing motion treated with scanned pulsed proton beams. Validation of the parameter-based 4DDC (p-4DDC) method was performed with an anthropomorphic phantom and patient data employing measurements and a log file-based 4DDC tool. The dose volume histogram parameters () were investigated for the target and the organs at risk, compared to static and the file-based approach. Results The difference between the measured and the p-4DDC dose was within the deviation of the measurements. The maximum deviation was 0.4Gy. For the planning target volume varied up to 15% compared to the static scenario, while the results from the log file and p-4DDC agreed within 2%. For the liver patients, deviated up to 35% compared to static and 10% comparing the two 4DDC tools, while for the pancreas patients the varied up to 45% and 11%, respectively. Conclusion The results showed that p-4DDC could be used prospectively. The next step will be the clinical implementation of the p-4DDC tool, which can support a decision to either adapt the treatment plan or apply motion mitigation strategies.

Republication of "Assessment of Recovery From Geriatric Ankle Fracture Using the Life Space Mobility Assessment (LSA): A Pilot Study".

The Life Space Assessment (LSA) is a validated outcomes measure that aims to assess the level of mobility and physical functioning within one's own environment following a medical event. We sought to study the recovery of geriatric ankle fracture patients utilizing the LSA. We hypothesized that the LSA would provide improved assessment of these patients and help identify key differences in operative and nonoperatively treated patients. Prospective observational study of geriatric patients age 65 years and older with an ankle fracture with 1-year follow-up. Operative versus nonoperative intervention was determined by the attending physician on a patient-specific basis. The LSA, Short Form-36 (SF-36), and visual analog pain scale (VAPS) were administered at predetermined intervals postinjury and scores were analyzed for significance. 20 patients were enrolled in this study. 11 underwent surgery whereas 9 were treated nonoperatively. Regardless of treatment, the preinjury LSA score was 86.7. This significantly dropped to 20.6 at 6 weeks and recovered to 73.6 at 12 months. In the operative cohort, the LSA scores preinjury were 91.4 and improved to 87.6 after 1 year. The nonoperative group recorded 80.9 preinjury and only improved to 59.5 at 1 year (P = 0.007). There was no statistically significant difference when comparing the results of the SF-36 and VAPS to the LSA. The LSA was effective in assessing recovery in geriatric ankle fracture patients. A severe deficit in mobility was seen for the first 6 months of recovery regardless of treatment. Operative patients ultimately returned to their baseline LSA at 1 year while nonoperative patients did not. Outcomes from the VAS and SF-36 mirrored the LSA but were not found to be statistically significant. Level II.

Effectively utilizing publicly available databases for cancer target evaluation.

The majority of compounds designed against cancer drug targets do not progress to become approved drugs, mainly due to lack of efficacy and/or unmanageable toxicity. Robust target evaluation is therefore required before progressing through the drug discovery process to reduce the high attrition rate. There are a wealth of publicly available databases that can be mined to generate data as part of a target evaluation. It can, however, be challenging to learn what databases are available,how and when they should be used, and to understand the associated limitations. Here, we have compiled and present key, freely accessible and easy-to-use databases that house informative datasets from in vitro, in vivo and clinical studies. We also highlight comprehensive target review databases that aim to bring together information from multiple sources into one-stop portals. In the post-genomics era, a key objective is to exploit the extensive cell, animal and patient characterization datasets in order to deliver precision medicine on a patient-specific basis. Effective utilization of the highlighted databases will go some way towards supporting the cancer research community achieve these aims.

Abstract 4060: Selective Neoantigens Peptide TILs (snapTILs): A next generation tumor infiltrating lymphocytes (TIL) therapy platform

Abstract Background: Immunotherapies using autologous tumor infiltrating lymphocytes (TILs) have become effective treatment modalities for several patients with solid tumors, yielding a response rate of around 49% in patients with metastatic melanoma. Most clinical centers utilize bulk, randomly isolated TILs from the tumor tissue for ex vivo expansion and infusion. Only a minor fraction of these administered TILs recognize tumor antigens and most are non-mutated public antigens. Moreover, expression of these antigens on normal cells can trigger central and peripheral tolerance mechanisms. Against this background, cancer neoantigens derived from private mutations represent an ideal class of cancer antigens to target because they are highly tumor specific by nature, therefore reducing the potential induction of central and peripheral tolerance. We recently developed a new technology to enrich TILs with predefined neoantigen specificities, which we termed Selective Neo Antigens Peptides TILs (snapTILs). Here we describe the efficacy studies of a melanoma and pancreatic cancer patient’s snapTILs done in autologous settings both ex vivo and in vivo. Methods: Our workflow consisted of a multistep process performed on a patient-specific basis. Briefly, mutations were called by whole-exome sequencing of tumor vs. normal DNA to generate a peptide library. The peptides were further filtered using an in-silico prediction algorithm and our proprietary peptide-MHC binding assay. Selected peptides were then incubated with TILs isolated from the patient’s tumor biopsy, resulting in TIL population enriched of T cells that recognize the neopeptides (snapTILs). After rapid expansion, snapTIL efficacy was tested by checking their ability to infiltrate in patient-derived tumor organoids, their activation status (IFNγ), cytotoxicity (Granzyme B), and T-cell exhaustion markers. Lastly, snapTILs in vivo efficacy was tested in human melanoma and pancreatic cancer xenograft models using hIL-2 transgenic mice. Results: Our initial data showed that the levels of infiltration of snapTILs into tumor organoids was significantly higher compared to IL2-stimulated TILs in both cancer models. Also, snapTILs recognized tumors with great specificity as they showed significantly less infiltration in organoids generated from generally uninvolved tissues from the same patients. Moreover, snapTILs were found to be more cytotoxic in nature than the IL2-stimulated TILs in terms of IFNγ and Granzyme B secretion after coculture with autologous tumor cells. Our in vivo data confirmed these observations by showing 70% tumor growth inhibition in highly immunogenic melanoma and 50% tumor growth restriction in a poorly immunogenic pancreatic tumor model. In conclusion, our approach proves the potential for personally tailored immunotherapies to yield more effective and safer cancer treatments using snapTILs. Citation Format: Tithi Ghosh Halder, Shelby Rheinschmidt, Sydney Adamson, Anna Larson, Taylor Bargenquast, Ryan Rodriguez del Villar, Serina Ng, Kate Gutowsky, Alexis Weston, Trason Thode, Mohan Kaadige, Erin Kelley, Jorge Soria-Bustos, Jorge Giron, John Altin, Raffaella Soldi, Sunil Sharma. Selective Neoantigens Peptide TILs (snapTILs): A next generation tumor infiltrating lymphocytes (TIL) therapy platform. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4060.