Sarah Grube, Maximilian Neidhardt, Anna-Katarina Herrmann, Johanna Sprenger, Kian Abdolazizi, Sarah Latus, Christian J. Cyron, Alexander Schlaefer
Soft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired. However, force-displacement curves strongly depend on the tool geometry which is often complex in the case of medical tools. Hence, a tool calibration procedure is desired to directly map force-displacement curves to the corresponding tissue elasticity.We present an experimental setup for calibrating medical tools with a robot. First, we propose to estimate the elasticity of gelatin phantoms by spherical indentation with a state-of-the-art contact model. We estimate force-displacement curves for different gelatin elasticities and temperatures. Our experiments demonstrate that gelatin elasticity is highly dependent on temperature, which can lead to an elasticity offset if not considered. Second, we propose to use a more complex material model, e.g., a neural network, that can be trained with the determined elasticities. Considering the temperature of the gelatin sample we can represent different elasticities per phantom and thereby increase our training data.We report elasticity values ranging from 10 to 40 kPa for a 10% gelatin phantom, depending on temperature.
{"title":"A Calibration Approach for Elasticity Estimation with Medical Tools","authors":"Sarah Grube, Maximilian Neidhardt, Anna-Katarina Herrmann, Johanna Sprenger, Kian Abdolazizi, Sarah Latus, Christian J. Cyron, Alexander Schlaefer","doi":"arxiv-2406.09947","DOIUrl":"https://doi.org/arxiv-2406.09947","url":null,"abstract":"Soft tissue elasticity is directly related to different stages of diseases\u0000and can be used for tissue identification during minimally invasive procedures.\u0000By palpating a tissue with a robot in a minimally invasive fashion\u0000force-displacement curves can be acquired. However, force-displacement curves\u0000strongly depend on the tool geometry which is often complex in the case of\u0000medical tools. Hence, a tool calibration procedure is desired to directly map\u0000force-displacement curves to the corresponding tissue elasticity.We present an\u0000experimental setup for calibrating medical tools with a robot. First, we\u0000propose to estimate the elasticity of gelatin phantoms by spherical indentation\u0000with a state-of-the-art contact model. We estimate force-displacement curves\u0000for different gelatin elasticities and temperatures. Our experiments\u0000demonstrate that gelatin elasticity is highly dependent on temperature, which\u0000can lead to an elasticity offset if not considered. Second, we propose to use a\u0000more complex material model, e.g., a neural network, that can be trained with\u0000the determined elasticities. Considering the temperature of the gelatin sample\u0000we can represent different elasticities per phantom and thereby increase our\u0000training data.We report elasticity values ranging from 10 to 40 kPa for a 10%\u0000gelatin phantom, depending on temperature.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research project investigates the critical role of oncostreams in glioma aggressiveness, leveraging advanced ex-vivo 3D explants and in-vivo intravital imaging techniques to establish a direct correlation between oncostream density and cancer severity. The primary objective is to model the cell populations within oncostreams, with a specific focus on GFP+ NPA cells, to simulate cancer dynamics and provide insights into tumor behavior. The study employs a simple Birth-Death process to analyze cell population dynamics and treatment effects, building and solving Kolmogorov equations to predict changes in cell population over time. While the model could be expanded to include additional modulators such as morphological attributes and neurotransmitter exposure, the focus remains on cell population to maintain feasibility. The study also examines various treatment methods, finding that glutamate increases glioma cell movement while histamine reduces it. Collagenase treatment effectively dismantles oncostreams, suggesting a potential therapeutic strategy. For this paper, we specifically are going to be looking at Cytochalasin D, which shows promise in disrupting oncostreams and reducing glioma invasiveness. By integrating these treatment variables into the model, the research aims to understand their impact on glioma cell density within the oncostreams and aggressiveness, thereby contributing to improved cancer management strategies. This comprehensive approach is expected to enhance our understanding of glioma progression and inform the development of effective therapeutic interventions.
{"title":"Analyzing the birth-death model of Oncostreams in Glioma, and the effects of Cytochalasin D treatment","authors":"Kai Poffenbarger, Rohan Pandey","doi":"arxiv-2407.10983","DOIUrl":"https://doi.org/arxiv-2407.10983","url":null,"abstract":"This research project investigates the critical role of oncostreams in glioma\u0000aggressiveness, leveraging advanced ex-vivo 3D explants and in-vivo intravital\u0000imaging techniques to establish a direct correlation between oncostream density\u0000and cancer severity. The primary objective is to model the cell populations\u0000within oncostreams, with a specific focus on GFP+ NPA cells, to simulate cancer\u0000dynamics and provide insights into tumor behavior. The study employs a simple\u0000Birth-Death process to analyze cell population dynamics and treatment effects,\u0000building and solving Kolmogorov equations to predict changes in cell population\u0000over time. While the model could be expanded to include additional modulators such as\u0000morphological attributes and neurotransmitter exposure, the focus remains on\u0000cell population to maintain feasibility. The study also examines various\u0000treatment methods, finding that glutamate increases glioma cell movement while\u0000histamine reduces it. Collagenase treatment effectively dismantles oncostreams,\u0000suggesting a potential therapeutic strategy. For this paper, we specifically\u0000are going to be looking at Cytochalasin D, which shows promise in disrupting\u0000oncostreams and reducing glioma invasiveness. By integrating these treatment\u0000variables into the model, the research aims to understand their impact on\u0000glioma cell density within the oncostreams and aggressiveness, thereby\u0000contributing to improved cancer management strategies. This comprehensive\u0000approach is expected to enhance our understanding of glioma progression and\u0000inform the development of effective therapeutic interventions.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A brief introduction of the technical approach to model FTUs as an aggregate of cells, whose state transition dynamics are mathematically represented as port-hamiltonians or Differential Algebraic equations is presented. A python library and browser based tool to enable modellers to compose the FTU graph, specify the cellular equations and the interconnection between the cells at the level of physical quantities they exchange consistent with the technical approach is discussed.
{"title":"12 Labours tools for developing Functional Tissue Units","authors":"Jagir R. Hussan","doi":"arxiv-2406.10301","DOIUrl":"https://doi.org/arxiv-2406.10301","url":null,"abstract":"A brief introduction of the technical approach to model FTUs as an aggregate\u0000of cells, whose state transition dynamics are mathematically represented as\u0000port-hamiltonians or Differential Algebraic equations is presented. A python\u0000library and browser based tool to enable modellers to compose the FTU graph,\u0000specify the cellular equations and the interconnection between the cells at the\u0000level of physical quantities they exchange consistent with the technical\u0000approach is discussed.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wound geometry and the mechanical properties of human skin govern the failure modes of partially healed or scarred tissue. Though dermatologists and surgeons develop an intuitive understanding of the mechanical characteristics of skin through clinical practice, finite element models of wounds can aid in formalizing intuition. In this work, we explore the effect of wound geometry and primary intention closure on the propagation of mechanical stresses through skin. We use a two-layer, orthotropic, hyperelastic model of the epidermis, dermis, and subcutis to accurately capture the mechanical and geometric effects at work. We highlight the key assumptions which must be made when modeling closure of wounds by primary intention, clearly delineating promising areas for model improvement. Models are implemented in DOLFINx, an open-source finite element framework, and reference code is provided for reproducible and extensible science.
{"title":"Charting a finite element, mechanical atlas of dermatologic wound closure","authors":"Congzhou M Sha","doi":"arxiv-2406.06957","DOIUrl":"https://doi.org/arxiv-2406.06957","url":null,"abstract":"Wound geometry and the mechanical properties of human skin govern the failure\u0000modes of partially healed or scarred tissue. Though dermatologists and surgeons\u0000develop an intuitive understanding of the mechanical characteristics of skin\u0000through clinical practice, finite element models of wounds can aid in\u0000formalizing intuition. In this work, we explore the effect of wound geometry\u0000and primary intention closure on the propagation of mechanical stresses through\u0000skin. We use a two-layer, orthotropic, hyperelastic model of the epidermis,\u0000dermis, and subcutis to accurately capture the mechanical and geometric effects\u0000at work. We highlight the key assumptions which must be made when modeling\u0000closure of wounds by primary intention, clearly delineating promising areas for\u0000model improvement. Models are implemented in DOLFINx, an open-source finite\u0000element framework, and reference code is provided for reproducible and\u0000extensible science.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. J. Hart, Chloe Engler Hart, Spencer Hopson, Paul M. Urie, Dennis Della Corte
Despite considerable progress in developing artificial intelligence (AI) algorithms for prostate cancer detection from whole slide images, the clinical applicability of these models remains limited due to variability in pathological annotations and existing dataset limitations. This article proposes a novel approach to overcome these challenges by leveraging a Bayesian framework to seamlessly integrate new data, and present results as a panel of annotations. The framework is demonstrated by integrating a Bayesian prior with one trained AI model to generate a distribution of Gleason patterns for each pixel of an image. It is shown that using this distribution of Gleason patterns rather than a ground-truth label can improve model applicability, mitigate errors, and highlight areas of interest for pathologists. Additionally, we present a high-quality, hand-curated dataset of prostate histopathological images annotated at the gland level by trained pre-medical students and verified by an expert pathologist. We highlight the potential of this adaptive and uncertainty-aware framework for developing clinically deployable AI tools that can support pathologists in accurate prostate cancer grading, improve diagnostic accuracy, and create positive patient outcomes.
{"title":"Overcoming Limitations in Artificial Intelligence-based Prostate Cancer Detection through Better Datasets and a Bayesian Approach to Aggregate Panel Predictions","authors":"T. J. Hart, Chloe Engler Hart, Spencer Hopson, Paul M. Urie, Dennis Della Corte","doi":"arxiv-2406.06801","DOIUrl":"https://doi.org/arxiv-2406.06801","url":null,"abstract":"Despite considerable progress in developing artificial intelligence (AI)\u0000algorithms for prostate cancer detection from whole slide images, the clinical\u0000applicability of these models remains limited due to variability in\u0000pathological annotations and existing dataset limitations. This article\u0000proposes a novel approach to overcome these challenges by leveraging a Bayesian\u0000framework to seamlessly integrate new data, and present results as a panel of\u0000annotations. The framework is demonstrated by integrating a Bayesian prior with\u0000one trained AI model to generate a distribution of Gleason patterns for each\u0000pixel of an image. It is shown that using this distribution of Gleason patterns\u0000rather than a ground-truth label can improve model applicability, mitigate\u0000errors, and highlight areas of interest for pathologists. Additionally, we\u0000present a high-quality, hand-curated dataset of prostate histopathological\u0000images annotated at the gland level by trained pre-medical students and\u0000verified by an expert pathologist. We highlight the potential of this adaptive\u0000and uncertainty-aware framework for developing clinically deployable AI tools\u0000that can support pathologists in accurate prostate cancer grading, improve\u0000diagnostic accuracy, and create positive patient outcomes.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Irina A. Mizeva, Natalia P. Podolyan, Oleg V. Mamontov, Anastasiia V. Sakovskaia, Alexei A. Kamshilin
Low-frequency oscillations in the human circulatory system is important for basic physiology and practical applications in clinical medicine. Our objective was to study which mechanism (central or local) is responsible for changes in blood flow fluctuations at around 0.1 Hz. We used the method of imaging photoplethysmography synchronized with electrocardiography to measure blood-flow response to local forearm heating of 18 healthy male volunteers. The dynamics of peripheral perfusion was revealed by a correlation processing of photoplethysmography data, and the central hemodynamics was assessed from the electrocardiogram. Wavelet analysis was used to estimate the dynamics of spectral components. Our results show that skin heating leads to multiple increase in local perfusion accompanied by drop in blood flow oscillations at 0.1 Hz, whereas no changes in heart rate variability was observed. After switching off the heating, perfusion remains at the high level, regardless decrease in skin temperature. The 0.1 Hz oscillations are smoothly recovered to the base level. In conclusion, we confirm the local nature of fluctuations in peripheral blood flow in the frequency band of about 0.1 Hz. A significant, but time-delayed, recovery of fluctuation energy in this frequency range after cessation of the skin warming was discovered. This study reveals a novel factor involved in the regulation microcirculatory vascular tone. A comprehensive study of hemodynamics using the new technique of imaging photoplethysmography synchronized with electrocardiography is a prerequisite for development of a valuable diagnostic tool.
{"title":"Local nature of 0.1 Hz oscillations in microcirculation is confirmed by imaging photoplethysmography","authors":"Irina A. Mizeva, Natalia P. Podolyan, Oleg V. Mamontov, Anastasiia V. Sakovskaia, Alexei A. Kamshilin","doi":"arxiv-2405.18760","DOIUrl":"https://doi.org/arxiv-2405.18760","url":null,"abstract":"Low-frequency oscillations in the human circulatory system is important for\u0000basic physiology and practical applications in clinical medicine. Our objective\u0000was to study which mechanism (central or local) is responsible for changes in\u0000blood flow fluctuations at around 0.1 Hz. We used the method of imaging\u0000photoplethysmography synchronized with electrocardiography to measure\u0000blood-flow response to local forearm heating of 18 healthy male volunteers. The\u0000dynamics of peripheral perfusion was revealed by a correlation processing of\u0000photoplethysmography data, and the central hemodynamics was assessed from the\u0000electrocardiogram. Wavelet analysis was used to estimate the dynamics of\u0000spectral components. Our results show that skin heating leads to multiple\u0000increase in local perfusion accompanied by drop in blood flow oscillations at\u00000.1 Hz, whereas no changes in heart rate variability was observed. After\u0000switching off the heating, perfusion remains at the high level, regardless\u0000decrease in skin temperature. The 0.1 Hz oscillations are smoothly recovered to\u0000the base level. In conclusion, we confirm the local nature of fluctuations in\u0000peripheral blood flow in the frequency band of about 0.1 Hz. A significant, but\u0000time-delayed, recovery of fluctuation energy in this frequency range after\u0000cessation of the skin warming was discovered. This study reveals a novel factor\u0000involved in the regulation microcirculatory vascular tone. A comprehensive\u0000study of hemodynamics using the new technique of imaging photoplethysmography\u0000synchronized with electrocardiography is a prerequisite for development of a\u0000valuable diagnostic tool.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141194062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emilio A. Mendiola, Raza Rana Mehdi, Dipan J. Shah, Reza Avazmohammadi
Left ventricular diastolic dysfunction (LVDD) is a group of diseases that adversely affect the passive phase of the cardiac cycle and can lead to heart failure. While left ventricular end-diastolic pressure (LVEDP) is a valuable prognostic measure in LVDD patients, traditional invasive methods of measuring LVEDP present risks and limitations, highlighting the need for alternative approaches. This paper investigates the possibility of measuring LVEDP non-invasively using inverse in-silico modeling. We propose the adoption of patient-specific cardiac modeling and simulation to estimate LVEDP and myocardial stiffness from cardiac strains. We have developed a high-fidelity patient-specific computational model of the left ventricle. Through an inverse modeling approach, myocardial stiffness and LVEDP were accurately estimated from cardiac strains that can be acquired from in vivo imaging, indicating the feasibility of computational modeling to augment current approaches in the measurement of ventricular pressure. Integration of such computational platforms into clinical practice holds promise for early detection and comprehensive assessment of LVDD with reduced risk for patients.
{"title":"On in-silico estimation of left ventricular end-diastolic pressure from cardiac strains","authors":"Emilio A. Mendiola, Raza Rana Mehdi, Dipan J. Shah, Reza Avazmohammadi","doi":"arxiv-2405.18343","DOIUrl":"https://doi.org/arxiv-2405.18343","url":null,"abstract":"Left ventricular diastolic dysfunction (LVDD) is a group of diseases that\u0000adversely affect the passive phase of the cardiac cycle and can lead to heart\u0000failure. While left ventricular end-diastolic pressure (LVEDP) is a valuable\u0000prognostic measure in LVDD patients, traditional invasive methods of measuring\u0000LVEDP present risks and limitations, highlighting the need for alternative\u0000approaches. This paper investigates the possibility of measuring LVEDP\u0000non-invasively using inverse in-silico modeling. We propose the adoption of\u0000patient-specific cardiac modeling and simulation to estimate LVEDP and\u0000myocardial stiffness from cardiac strains. We have developed a high-fidelity\u0000patient-specific computational model of the left ventricle. Through an inverse\u0000modeling approach, myocardial stiffness and LVEDP were accurately estimated\u0000from cardiac strains that can be acquired from in vivo imaging, indicating the\u0000feasibility of computational modeling to augment current approaches in the\u0000measurement of ventricular pressure. Integration of such computational\u0000platforms into clinical practice holds promise for early detection and\u0000comprehensive assessment of LVDD with reduced risk for patients.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141167512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ross Straughan, Karim Kadry, Sahil A. Parikh, Elazer R. Edelman, Farhad R. Nezami
Despite recent advances in diagnosis and treatment, atherosclerotic coronary artery diseases remain a leading cause of death worldwide. Various imaging modalities and metrics can detect lesions and predict patients at risk; however, identifying unstable lesions is still difficult. Current techniques cannot fully capture the complex morphology-modulated mechanical responses that affect plaque stability, leading to catastrophic failure and mute the benefit of device and drug interventions. Finite Element (FE) simulations utilizing intravascular imaging OCT (Optical Coherence Tomography) are effective in defining physiological stress distributions. However, creating 3D FE simulations of coronary arteries from OCT images is challenging to fully automate given OCT frame sparsity, limited material contrast, and restricted penetration depth. To address such limitations, we developed an algorithmic approach to automatically produce 3D FE-ready digital twins from labeled OCT images. The 3D models are anatomically faithful and recapitulate mechanically relevant tissue lesion components, automatically producing morphologies structurally similar to manually constructed models whilst including more minute details. A mesh convergence study highlighted the ability to reach stress and strain convergence with average errors of just 5.9% and 1.6% respectively in comparison to FE models with approximately twice the number of elements in areas of refinement. Such an automated procedure will enable analysis of large clinical cohorts at a previously unattainable scale and opens the possibility for in-silico methods for patient specific diagnoses and treatment planning for coronary artery disease.
尽管最近在诊断和治疗方面取得了进展，但动脉粥样硬化性冠状动脉疾病仍然是全球死亡的主要原因。各种成像模式和指标可以检测病变并预测高危患者；然而，识别不稳定病变仍然困难重重。目前的技术无法完全捕捉到影响斑块稳定性的复杂形态调节机械反应，从而导致灾难性的失败，并削弱了设备和药物干预的益处。利用血管内成像 OCT（光学相干断层扫描）进行有限元（FE）模拟能有效确定生理应力分布。然而，由于 OCT 图框稀疏、材料对比度有限以及穿透深度受限，要从 OCT 图像创建冠状动脉的三维有限元模拟，完全自动化具有挑战性。为了解决这些限制，我们开发了一种算法方法，从标记的 OCT 图像中自动生成三维 FE 就绪数字双胞胎。这些三维模型在解剖学上忠实再现了与机械相关的组织病变成分，自动生成的形态结构与人工构建的模型相似，同时包含更多的细节。网格收敛研究表明，与细化区域元素数量约为两倍的 FE 模型相比，该模型能够实现应力和应变收敛，平均误差分别仅为 5.9% 和 1.6%。这种自动化程序将能够以以前无法实现的规模对大型临床队列进行分析，并为冠状动脉疾病的患者特异性诊断和治疗计划的室内方法提供了可能性。
{"title":"Fully automated construction of three-dimensional finite element simulations from Optical Coherence Tomography","authors":"Ross Straughan, Karim Kadry, Sahil A. Parikh, Elazer R. Edelman, Farhad R. Nezami","doi":"arxiv-2405.13643","DOIUrl":"https://doi.org/arxiv-2405.13643","url":null,"abstract":"Despite recent advances in diagnosis and treatment, atherosclerotic coronary\u0000artery diseases remain a leading cause of death worldwide. Various imaging\u0000modalities and metrics can detect lesions and predict patients at risk;\u0000however, identifying unstable lesions is still difficult. Current techniques\u0000cannot fully capture the complex morphology-modulated mechanical responses that\u0000affect plaque stability, leading to catastrophic failure and mute the benefit\u0000of device and drug interventions. Finite Element (FE) simulations utilizing\u0000intravascular imaging OCT (Optical Coherence Tomography) are effective in\u0000defining physiological stress distributions. However, creating 3D FE\u0000simulations of coronary arteries from OCT images is challenging to fully\u0000automate given OCT frame sparsity, limited material contrast, and restricted\u0000penetration depth. To address such limitations, we developed an algorithmic\u0000approach to automatically produce 3D FE-ready digital twins from labeled OCT\u0000images. The 3D models are anatomically faithful and recapitulate mechanically\u0000relevant tissue lesion components, automatically producing morphologies\u0000structurally similar to manually constructed models whilst including more\u0000minute details. A mesh convergence study highlighted the ability to reach\u0000stress and strain convergence with average errors of just 5.9% and 1.6%\u0000respectively in comparison to FE models with approximately twice the number of\u0000elements in areas of refinement. Such an automated procedure will enable\u0000analysis of large clinical cohorts at a previously unattainable scale and opens\u0000the possibility for in-silico methods for patient specific diagnoses and\u0000treatment planning for coronary artery disease.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141152152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prajwal Ghimire, Ben Kinnersley, Golestan Karami, Prabhu Arumugam, Richard Houlston, Keyoumars Ashkan, Marc Modat, Thomas C Booth
Immunotherapy is an effective precision medicine treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma. A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis. Results Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed. Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.
{"title":"Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies","authors":"Prajwal Ghimire, Ben Kinnersley, Golestan Karami, Prabhu Arumugam, Richard Houlston, Keyoumars Ashkan, Marc Modat, Thomas C Booth","doi":"arxiv-2405.07858","DOIUrl":"https://doi.org/arxiv-2405.07858","url":null,"abstract":"Immunotherapy is an effective precision medicine treatment for several\u0000cancers. Imaging signatures of the underlying genome (radiogenomics) in\u0000glioblastoma patients may serve as preoperative biomarkers of the tumor-host\u0000immune apparatus. Validated biomarkers would have the potential to stratify\u0000patients during immunotherapy clinical trials, and if trials are beneficial,\u0000facilitate personalized neo-adjuvant treatment. The increased use of whole\u0000genome sequencing data, and the advances in bioinformatics and machine learning\u0000make such developments plausible. We performed a systematic review to determine\u0000the extent of development and validation of immune-related radiogenomic\u0000biomarkers for glioblastoma. A systematic review was performed following PRISMA\u0000guidelines using the PubMed, Medline, and Embase databases. Qualitative\u0000analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist.\u0000PROSPERO registered CRD42022340968. Extracted data were insufficiently\u0000homogenous to perform a meta-analysis. Results Nine studies, all retrospective,\u0000were included. Biomarkers extracted from magnetic resonance imaging volumes of\u0000interest included apparent diffusion coefficient values, relative cerebral\u0000blood volume values, and image-derived features. These biomarkers correlated\u0000with genomic markers from tumor cells or immune cells or with patient survival.\u0000The majority of studies had a high risk of bias and applicability concerns\u0000regarding the index test performed. Radiogenomic immune biomarkers have the\u0000potential to provide early treatment options to patients with glioblastoma.\u0000Targeted immunotherapy, stratified by these biomarkers, has the potential to\u0000allow individualized neo-adjuvant precision treatment options in clinical\u0000trials. However, there are no prospective studies validating these biomarkers,\u0000and interpretation is limited due to study bias with little evidence of\u0000generalizability.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work provides an exact mathematical characterization of the meniscus formed by a liquid of density $rho$ (model for tumor tissue) when probed with a cantilever device, operating by gravity (acceleration $g$) and with surface tension coefficient $sigma$ (material-dependent for the specific choice of liquid and cantilever). The shape and extremal parameters (maximum height $mathcal{H}$, break-off volume $mathcal{V}$) of the meniscus formed, as functions of $sigma, rho$, are found by an exact analysis. Having knowledge of the explicit relationship between these parameters allows to perform in one procedure both diagnosis and treatment.
{"title":"A mathematical model for droplet separation by surface tension using contact cantilevers -- applications to {it{in situ}} diagnosis and treatment","authors":"Sonia Elizabeth Teodorescu","doi":"arxiv-2407.00027","DOIUrl":"https://doi.org/arxiv-2407.00027","url":null,"abstract":"This work provides an exact mathematical characterization of the meniscus\u0000formed by a liquid of density $rho$ (model for tumor tissue) when probed with\u0000a cantilever device, operating by gravity (acceleration $g$) and with surface\u0000tension coefficient $sigma$ (material-dependent for the specific choice of\u0000liquid and cantilever). The shape and extremal parameters (maximum height\u0000$mathcal{H}$, break-off volume $mathcal{V}$) of the meniscus formed, as\u0000functions of $sigma, rho$, are found by an exact analysis. Having knowledge\u0000of the explicit relationship between these parameters allows to perform in one\u0000procedure both diagnosis and treatment.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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