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Computational techniques to monitoring fractional order type-1 diabetes mellitus model for feedback design of artificial pancreas 监测分数阶 1 型糖尿病模型的计算技术,用于人工胰腺的反馈设计
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-14 DOI: 10.1016/j.cmpb.2024.108420

Background and objectives:

In this paper, we developed a significant class of control issues regulated by nonlinear fractal order systems with input and output signals, our goal is to design a direct transcription method with impulsive instant order. Recent advances in the artificial pancreas system provide an emerging treatment option for type 1 diabetes. The performance of the blood glucose regulation directly relies on the accuracy of the glucose-insulin modeling. This work leads to the monitoring and assessment of comprehensive type-1 diabetes mellitus for controller design of artificial panaceas for the precision of the glucose-insulin glucagon in finite time with Caputo fractional approach for three primary subsystems.

Methods:

For the proposed model, we admire the qualitative analysis with equilibrium points lying in the feasible region. Model satisfied the biological feasibility with the Lipschitz criteria and linear growth is examined, considering positive solutions, boundedness and uniqueness at equilibrium points with Leray–Schauder results under time scale ideas. Within each subsystem, the virtual control input laws are derived by the application of input to state theorems and Ulam Hyers Rassias.

Results:

Chaotic Relation of Glucose insulin glucagon compartmental in the feasible region and stable in finite time interval monitoring is derived through simulations that are stable and bounded in the feasible regions. Additionally, as blood glucose is the only measurable state variable, the unscented power-law kernel estimator appropriately takes into account the significant problem of estimating inaccessible state variables that are bound to significant values for the glucose-insulin system. The comparative results on the simulated patients suggest that the suggested controller strategy performs remarkably better than the compared methods.

Conclusion:

In the model under investigation, parametric uncertainties are identified since the glucose, insulin, and glucagon system’s parameters are accurately measured numerically at different fractional order values. In terms of algorithm resilience and Caputo tracking in the presence of glucagon and insulin intake disturbance to maintain the glucose level. A comprehensive analysis of numerous difficult test issues is conducted in order to offer a thorough justification of the planned strategy to control the type 1 diabetes mellitus with designed the artificial pancreas.

背景和目标:本文提出了一类重要的控制问题,即由输入和输出信号的非线性分形阶系统调节的控制问题,我们的目标是设计一种具有脉冲瞬时阶的直接转录方法。人工胰腺系统的最新进展为 1 型糖尿病提供了一种新兴的治疗方案。血糖调节的性能直接依赖于葡萄糖-胰岛素模型的准确性。这项工作有助于对 1 型糖尿病进行全面监测和评估,从而利用卡普托分数法对三个主要子系统的葡萄糖-胰岛素-胰高血糖素在有限时间内的精确性进行人工胰腺控制器设计。根据 Lipschitz 准则和线性增长研究了模型的生物可行性,考虑了正解、平衡点的有界性和唯一性以及时间尺度思想下的 Leray-Schauder 结果。在每个子系统中,通过应用输入到状态定理和 Ulam Hyers Rassias,得出虚拟控制输入定律。结果:通过模拟得出葡萄糖胰岛素胰高血糖素在可行区域内的分区混沌关系,并在有限时间间隔监测中保持稳定,在可行区域内稳定且有界。此外,由于血糖是唯一可测量的状态变量,无香味幂律核估计器恰当地考虑到了估计葡萄糖-胰岛素系统中绑定为重要值的不可获取状态变量这一重大问题。对模拟患者的比较结果表明,所建议的控制器策略的性能明显优于其他比较方法。结论:在所研究的模型中,由于葡萄糖、胰岛素和胰高血糖素系统的参数在不同分数阶值上都经过了精确的数值测量,因此可以确定参数的不确定性。在存在胰高血糖素和胰岛素摄入干扰的情况下,从算法弹性和卡普托跟踪的角度来维持血糖水平。对许多棘手的测试问题进行了全面分析,以便为利用设计的人工胰腺控制 1 型糖尿病的计划战略提供充分的理由。
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引用次数: 0
Immunohistochemistry annotations enhance AI identification of lymphocytes and neutrophils in digitized H&E slides from inflammatory bowel disease 免疫组化注释增强了炎症性肠病数字化 H&E 切片中淋巴细胞和中性粒细胞的 AI 识别能力
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-13 DOI: 10.1016/j.cmpb.2024.108423

Background and Objective

Histologic assessment of the immune infiltrate in H&E slides is vital in diagnosing and managing inflammatory bowel diseases, but these assessments are subjective and time-consuming even for those with expertise. The development of deep learning models to aid in these assessments has been limited by the paucity of image data with reliably annotated immune cells available for training.

Methods

To address these challenges, we developed a pipeline that automates the neutrophil and lymphocyte labeling in ROIs from digital H&E slides. The data included ROIs extracted from 19 digitized H&E slides and the same slides restained with immunohistochemistry. Our pipeline first delineates each nucleus in H&E ROIs. Using the colorimetric features of the immunohistochemical stains (red: neutrophils, green: lymphocytes) in the immunohistochemistry ROIs, each cell was labeled as a neutrophil, a lymphocyte, or another cell. The labels were then transferred to the corresponding H&E ROIs by image registration, and the ROI registration accuracy was assessed by the median target registration error resulting in a labeled dataset. The newly formed dataset (NeuLy-IHC) comprising 519 ROIs with 235,256 labeled cells (74,339 lymphocytes, 16,326 neutrophils and 144,591 other cells) was used to train the HoVer-Net(NeuLy) model. The performance of HoVer-Net(NeuLy) measured by DICE coefficient (segmentation accuracy) and F1-scores (classification accuracy), was compared to those achieved by HoVer-Net(MoNuSAC) and SMILE(MoNuSAC) publicly available models trained on cancer-containing ROIs from the MoNuSAC dataset with manual cell labeling and pathologists’ annotations.

Results

The 1.0 μm median target registration error of ROIs observed was low demonstrating robust transferring of cellular labels from immunohistochemistry ROIs to H&E ROIs. In the test set comprising 76 NeuLy-IHC and 78 MoNuSAC ROIs, the HoVer-Net(NeuLy) achieved a DICE coefficient of 0.861 and F1-sores of 0.827, 0.838, and 0.828, for neutrophils, lymphocytes, and other cells, respectively, outperforming the HoVer-Net(MoNuSAC)'s and SMILE(MoNuSAC)’s DICE coefficient and F1 scores for each cell category.

Conclusions

We attribute the improved performance of HoVer-Net(NeuLy) to the larger number of immune cells in the NeuLy-IHC dataset (in total 5x more, including 21x more neutrophils) than in the MoNuSAC dataset. Despite being trained on data from inflammatory bowel disease specimens, our model maintained robust performance when tested on previously unseen data derived from cancer specimens. The NeuLy-IHC set provides opportunities for training accurate models to quantify the inflammatory infiltrate in digital histologic slides.

背景和目的对 H&E 切片中的免疫浸润进行组织学评估对于诊断和管理炎症性肠病至关重要,但这些评估既主观又耗时,即使是对具有专业知识的人来说也是如此。为了应对这些挑战,我们开发了一个管道,可以自动对数字 H&E 切片 ROI 中的中性粒细胞和淋巴细胞进行标记。数据包括从 19 张数字化 H&E 切片中提取的 ROI,以及经免疫组化染色的相同切片。我们的工作流程首先在 H&E ROI 中划分出每个细胞核。利用免疫组化 ROI 中免疫组化染色的比色特征(红色:中性粒细胞,绿色:淋巴细胞),将每个细胞标记为中性粒细胞、淋巴细胞或其他细胞。然后通过图像配准将标签转移到相应的 H&E ROI 上,并通过目标配准误差的中位数评估 ROI 配对的准确性,最后得到一个已标记的数据集。新形成的数据集(NeuLy-IHC)包括 519 个 ROI 和 235,256 个标记细胞(74,339 个淋巴细胞、16,326 个中性粒细胞和 144,591 个其他细胞),用于训练 HoVer-Net(NeuLy)模型。用 DICE 系数(分割准确率)和 F1 分数(分类准确率)来衡量 HoVer-Net(NeuLy) 的性能,并将其与 HoVer-Net(MoNuSAC) 和 SMILE(MoNuSAC) 公开发布的模型的性能进行比较。结果 观察到的 ROI 的 1.0 μm 中位目标注册误差很低,这表明细胞标签从免疫组化 ROI 转移到 H&E ROI 的能力很强。在由 76 个 NeuLy-IHC 和 78 个 MoNuSAC ROI 组成的测试集中,HoVer-Net(NeuLy) 的中性粒细胞、淋巴细胞和其他细胞的 DICE 系数为 0.861,F1 值分别为 0.827、0.838 和 0.828,优于 HoVer-Net(MoNuSAC) 和 SMILE(MoNuSAC) 的每个细胞类别的 DICE 系数和 F1 值。结论我们认为,HoVer-Net(NeuLy) 性能的提高是由于 NeuLy-IHC 数据集中的免疫细胞数量比 MoNuSAC 数据集中的免疫细胞数量多(总共多 5 倍,其中中性粒细胞多 21 倍)。尽管我们的模型是在炎症性肠病标本数据的基础上进行训练的,但在对以前未见过的癌症标本数据进行测试时,我们的模型仍然保持了强劲的性能。NeuLy-IHC 数据集提供了训练精确模型的机会,以量化数字组织学切片中的炎症浸润。
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引用次数: 0
Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms 利用进化算法优化用于精神分裂症谱系障碍预测的图神经网络架构
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-11 DOI: 10.1016/j.cmpb.2024.108419

Background and Objective:

The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing functional magnetic resonance imaging data, has been demonstrated to offer invaluable biomarkers conducive to clinical diagnosis. However, previous studies mainly focus on traditional machine learning methods or hand-crafted neural networks, which may not fully capture the spatial topological relationship between brain regions.

Methods:

This paper proposes an evolutionary algorithm (EA) based graph neural architecture search (GNAS) method. EA-GNAS has the ability to search for high-performance graph neural networks for schizophrenia spectrum disorder prediction. Moreover, we adopt GNNExplainer to investigate the explainability of the acquired architectures, ensuring that the model’s predictions are both accurate and comprehensible.

Results:

The results suggest that the graph neural network model, derived using genetic algorithm search, outperforms under five-fold cross-validation, achieving a fitness of 0.1850. Relative to conventional machine learning and other deep learning approaches, the proposed method yields superior accuracy, F1 score, and AUC values of 0.8246, 0.8438, and 0.8258, respectively.

Conclusion:

Based on a multi-site dataset from schizophrenia spectrum disorder patients, the findings reveal an enhancement over prior methods, advancing our comprehension of brain function and potentially offering a biomarker for diagnosing schizophrenia spectrum disorder.

背景和目的:精神分裂症谱系障碍的准确诊断在改善患者预后、及时干预和优化治疗方案方面发挥着重要作用。利用功能性磁共振成像数据进行的功能连接分析已被证明能为临床诊断提供宝贵的生物标志物。方法:本文提出了一种基于进化算法(EA)的图神经架构搜索(GNAS)方法。EA-GNAS能够搜索出用于精神分裂症谱系障碍预测的高性能图神经网络。结果:结果表明,利用遗传算法搜索得到的图神经网络模型在五倍交叉验证下表现优异,达到了0.1850的适配度。结论:基于精神分裂症谱系障碍患者的多站点数据集,研究结果表明该方法优于之前的方法,促进了我们对大脑功能的理解,并有可能成为诊断精神分裂症谱系障碍的生物标志物。
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引用次数: 0
Bayesian networks in modeling leucocyte interplay following brain irradiation: A comprehensive framework 贝叶斯网络在脑部照射后白细胞相互作用建模中的应用:综合框架
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-11 DOI: 10.1016/j.cmpb.2024.108421

Background and objective

Understanding the intricate interactions among leucocyte subpopulations following radiotherapy is crucial for advancing cancer research and immunology. Recently, interest in recent radiotherapy modalities, such as protons, has increased. Herein, we present a framework utilizing Bayesian networks to uncover these complex relationships via an illustrative example of brain irradiation in rodents.

Methods

We utilized data from 96 healthy C57BL/6 adult mice subjected to either X-ray or proton brain irradiation. Leucocyte subpopulations in the blood collected 12 h after the final irradiated fraction were quantified. We employed Bayesian networks to detect causal interplay between physiological parameters, radiation variables and circulating leucocytes. The causal structure was learned via the use of the Bayesian information criterion as a scored criterion. Parameter estimation was performed to quantify the strength of the identified causal relationships. Cross-validation was used to validate our Bayesian network model's performance.

Results

In the X-ray model, we discovered previously undisclosed interactions between NK-cells and neutrophils, and between monocytes and T-CD4+ cells. The proton model revealed an interplay involving T-CD4+ cells and neutrophils. Both X-rays and protons led to heightened interactions between T-CD8+ cells and B cells, indicating their significant role in orchestrating immune responses. Additionally, the proton model displayed strengthened interactions between T-CD4+ and T-CD8+ cells, emphasizing a dynamic and coordinated immune response post-irradiation. Cross-validation results demonstrated the robustness of the Bayesian network model in explaining data uncertainty.

Conclusion

The use of Bayesian networks as tools for causal structure discovery has revealed novel insights into the dynamics of immune responses to radiation exposure.

背景和目的 了解放疗后白细胞亚群之间错综复杂的相互作用对于推动癌症研究和免疫学的发展至关重要。最近,人们对质子等最新放疗方式的兴趣与日俱增。我们利用 96 只健康的 C57BL/6 成年小鼠接受 X 射线或质子脑辐照后的数据,建立了一个贝叶斯网络框架,以揭示这些复杂的关系。我们对最终辐照后 12 小时收集的血液中的白细胞亚群进行了量化。我们采用贝叶斯网络来检测生理参数、辐射变量和循环白细胞之间的因果关系。通过使用贝叶斯信息标准作为评分标准来学习因果结构。通过参数估计来量化已识别因果关系的强度。在 X 射线模型中,我们发现了 NK 细胞与中性粒细胞之间以及单核细胞与 T-CD4+ 细胞之间以前未曾披露的相互作用。质子模型揭示了 T-CD4+ 细胞与中性粒细胞之间的相互作用。X 射线和质子都增强了 T-CD8+ 细胞与 B 细胞之间的相互作用,表明它们在协调免疫反应中发挥着重要作用。此外,质子模型加强了 T-CD4+ 和 T-CD8+ 细胞之间的相互作用,强调了辐照后动态协调的免疫反应。交叉验证结果证明了贝叶斯网络模型在解释数据不确定性方面的稳健性。
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引用次数: 0
A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma 从 CPTAC-胰腺导管腺癌多基因组队列中得出的时间依赖性可解释放射基因组分析。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-07 DOI: 10.1016/j.cmpb.2024.108408

Background and Objective

In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients’ follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project.

Materials and Methods

Analyzed datasets included tumor-annotated radiologic images, clinical, and mutational data. A feature selection was based on univariate (UV) and multivariate (MV) survival analyses according to Overall Survival (OS) and recurrence (REC). In this study, we considered seven multi-omic datasets and compared four SML classifiers: Cox, survival random forest, generalized boosted, and support vector machines (SVM). For each classifier, we assessed the concordance (C) index on the validation set. The best classifiers for the validation set on both OS and REC underwent explainability analyses using SurvSHAP(t), which extends SHapley Additive exPlanations (SHAP).

Results

According to OS, after UV and MV analyses we selected 18/37 and 10/37 multi-omic features, respectively. According to REC, based on UV and MV analyses we selected 10/35 and 5/35 determinants, respectively. Generally, SML classifiers including radiomics outperformed those modelled on clinical or mutational predictors. For OS, the Cox model encompassing radiomic, clinical, and mutational features reached 75 % of C index, outperforming other classifiers. On the other hand, for REC, the SVM model including only radiomics emerged as the best-performing, with 68 % of C index. For OS, SurvSHAP(t) identified the first order Median Gray Level (GL) intensities, the gender, the tumor grade, the Joint Energy GL Co-occurrence Matrix (GLCM), and the GLCM Informational Measures of Correlations of type 1 as the most important features. For REC, the first order Median GL intensities, the GL size zone matrix Small Area Low GL Emphasis, and first order variance of GL intensities emerged as the most discriminative.

Conclusions

In this work, radiomics showed the potential for improving patients’ risk stratification in PDA. Furthermore, a deeper understanding of how radiomics can contribute to prognosis in PDA was achieved with a time-dependent explainability of the top multi-omic predictors.
背景和目的:在胰腺导管腺癌(PDA)中,多组学模型正在兴起,以满足尚未得到满足的临床需求,从而得出新的定量预后因素。我们利用生存机器学习(SML)分类器和基于患者随访(FU)的可解释性实现了一个管道,从 CPTAC-PDA 项目的公开多组学数据集中对预后进行分层:分析数据集包括肿瘤注释放射影像、临床和突变数据。根据总生存期(OS)和复发率(REC)进行单变量(UV)和多变量(MV)生存分析,选择特征。在这项研究中,我们考虑了七个多组数据集,并比较了四种 SML 分类器:Cox、生存随机森林、广义提升和支持向量机(SVM)。我们评估了每个分类器在验证集上的一致性(C)指数。使用SurvSHAP(t)对OS和REC验证集的最佳分类器进行了可解释性分析,SurvSHAP(t)扩展了SHapley Additive exPlanations(SHAP):根据 OS,经过 UV 和 MV 分析,我们分别选出了 18/37 和 10/37 个多原子特征。根据 REC,基于 UV 和 MV 分析,我们分别选择了 10/35 和 5/35 个决定因素。一般来说,包含放射组学的 SML 分类器优于以临床或突变预测因子为模型的分类器。就OS而言,包含放射组学、临床和突变特征的Cox模型达到了75%的C指数,优于其他分类器。另一方面,对于 REC,仅包含放射组学特征的 SVM 模型表现最佳,C 指数为 68%。对于 OS,SurvSHAP(t) 将一阶灰度级(GL)强度中值、性别、肿瘤分级、联合能量 GL 共现矩阵(GLCM)和 1 型 GLCM 相关信息度量确定为最重要的特征。就 REC 而言,一阶 GL 强度中位数、GL 大小区矩阵小区域低 GL 强调度和 GL 强度一阶方差成为最具鉴别力的特征:在这项工作中,放射组学显示出改善 PDA 患者风险分层的潜力。结论:这项研究表明,放射组学具有改善 PDA 患者风险分层的潜力。此外,通过对顶级多组学预测因子的时间依赖性解释,我们对放射组学如何有助于 PDA 的预后有了更深入的了解。
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引用次数: 0
Colorectal cancer risk mapping through Bayesian networks 通过贝叶斯网络绘制大肠癌风险图
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-06 DOI: 10.1016/j.cmpb.2024.108407

Background and Objective:

Only about 14% of eligible EU citizens finally participate in colorectal cancer (CRC) screening programs despite it being the third most common type of cancer worldwide. The development of CRC risk models can enable predictions to be embedded in decision-support tools facilitating CRC screening and treatment recommendations. This paper develops a predictive model that aids in characterizing CRC risk groups and assessing the influence of a variety of risk factors on the population.

Methods:

A CRC Bayesian Network is learnt by aggregating extensive expert knowledge and data from an observational study and making use of structure learning algorithms to model the relations between variables. The network is then parametrised to characterize these relations in terms of local probability distributions at each of the nodes. It is finally used to predict the risks of developing CRC together with the uncertainty around such predictions.

Results:

A graphical CRC risk mapping tool is developed from the model and used to segment the population into risk subgroups according to variables of interest. Furthermore, the network provides insights on the predictive influence of modifiable risk factors such as alcohol consumption and smoking, and medical conditions such as diabetes or hypertension linked to lifestyles that potentially have an impact on an increased risk of developing CRC.

Conclusion:

CRC is most commonly developed in older individuals. However, some modifiable behavioral factors seem to have a strong predictive influence on its potential risk of development. Modeling these effects facilitates identifying risk groups and targeting influential variables which are subsequently helpful in the design of screening and treatment programs.

背景和目的:尽管结肠直肠癌(CRC)是全球第三大常见癌症,但最终只有约 14% 符合条件的欧盟公民参加了该筛查项目。开发 CRC 风险模型可以将预测结果嵌入决策支持工具中,从而为 CRC 筛查和治疗建议提供便利。本文开发了一种预测模型,可帮助确定 CRC 风险群体的特征,并评估各种风险因素对人群的影响。方法:通过汇总广泛的专家知识和一项观察研究的数据,并利用结构学习算法对变量之间的关系进行建模,从而学习 CRC 贝叶斯网络。然后对该网络进行参数化,根据每个节点的局部概率分布来描述这些关系。结果:根据该模型开发了一个图形化的 CRC 风险绘图工具,用于根据相关变量将人群划分为不同的风险亚群。此外,该网络还提供了有关可改变的风险因素(如饮酒和吸烟)以及与生活方式相关的医疗状况(如糖尿病或高血压)的预测影响,这些因素可能会增加患上 CRC 的风险。然而,一些可改变的行为因素似乎对其潜在的发病风险有很大的预测影响。对这些影响进行建模,有助于确定风险群体和有针对性的影响变量,从而有助于筛查和治疗方案的设计。
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引用次数: 0
MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction MMGCN:用于癌症预后预测的多模态多视图卷积网络
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-06 DOI: 10.1016/j.cmpb.2024.108400

Background and objective

Accurate prognosis prediction for cancer patients plays a significant role in the formulation of treatment strategies, considerably impacting personalized medicine. Recent advancements in this field indicate that integrating information from various modalities, such as genetic and clinical data, and developing multi-modal deep learning models can enhance prediction accuracy. However, most existing multi-modal deep learning methods either overlook patient similarities that benefit prognosis prediction or fail to effectively capture diverse information due to measuring patient similarities from a single perspective. To address these issues, a novel framework called multi-modal multi-view graph convolutional networks (MMGCN) is proposed for cancer prognosis prediction.

Methods

Initially, we utilize the similarity network fusion (SNF) algorithm to merge patient similarity networks (PSNs), individually constructed using gene expression, copy number alteration, and clinical data, into a fused PSN for integrating multi-modal information. To capture diverse perspectives of patient similarities, we treat the fused PSN as a multi-view graph by considering each single-edge-type subgraph as a view graph, and propose multi-view graph convolutional networks (GCNs) with a view-level attention mechanism. Moreover, an edge homophily prediction module is designed to alleviate the adverse effects of heterophilic edges on the representation power of GCNs. Finally, comprehensive representations of patient nodes are obtained to predict cancer prognosis.

Results

Experimental results demonstrate that MMGCN outperforms state-of-the-art baselines on four public datasets, including METABRIC, TCGA-BRCA, TCGA-LGG, and TCGA-LUSC, with the area under the receiver operating characteristic curve achieving 0.827 ± 0.005, 0.805 ± 0.014, 0.925 ± 0.007, and 0.746 ± 0.013, respectively.

Conclusions

Our study reveals the effectiveness of the proposed MMGCN, which deeply explores patient similarities related to different modalities from a broad perspective, in enhancing the performance of multi-modal cancer prognosis prediction. The source code is publicly available at https://github.com/ping-y/MMGCN.

背景和目的对癌症患者进行准确的预后预测在制定治疗策略方面发挥着重要作用,对个性化医疗产生了重大影响。该领域的最新进展表明,整合遗传和临床数据等各种模式的信息并开发多模式深度学习模型可以提高预测的准确性。然而,大多数现有的多模态深度学习方法要么忽略了有利于预后预测的患者相似性,要么由于从单一角度测量患者相似性而无法有效捕捉各种信息。为了解决这些问题,我们提出了一种用于癌症预后预测的新型框架,称为多模态多视角图卷积网络(MMGCN)。方法最初,我们利用相似性网络融合(SNF)算法,将利用基因表达、拷贝数改变和临床数据单独构建的患者相似性网络(PSN)合并为一个融合的PSN,以整合多模态信息。为了从不同角度捕捉患者的相似性,我们将融合后的患者相似性网络视为多视图图,将每个单边型子图视为视图图,并提出了具有视图级关注机制的多视图卷积网络(GCN)。此外,还设计了边缘同亲预测模块,以减轻异亲边缘对 GCN 表示力的不利影响。结果实验结果表明,在 METABRIC、TCGA-BRCA、TCGA-LGG 和 TCGA-LUSC 等四个公共数据集上,MMGCN 的表现优于最先进的基线,接收者操作特征曲线下面积分别为 0.结论我们的研究揭示了所提出的 MMGCN 在提高多模态癌症预后预测性能方面的有效性,它从广阔的视角深入探讨了与不同模态相关的患者相似性。源代码可在 https://github.com/ping-y/MMGCN 公开获取。
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引用次数: 0
An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions 在稳定和搏动条件下模拟冠状动脉血流的自动省时框架
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-06 DOI: 10.1016/j.cmpb.2024.108415

Background and objective

Invasive fractional flow reserve (FFR) measurement is the gold standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits computational fluid dynamics (CFD) for non-invasive evaluation of FFR, simulating coronary flow in virtual geometries reconstructed from computed tomography (CT), but suffers from cost-intensive computing process and uncertainties in the definition of patient specific boundary conditions (BCs). In this work, we investigated the use of time-averaged steady BCs, compared to pulsatile to reduce the computational time and deployed a self-adjusting method for the tuning of BCs to patient-specific clinical data.

Methods

133 coronary arteries were reconstructed form CT images of patients suffering from CAD. For each vessel, invasive FFR was measured. After segmentation, the geometries were prepared for CFD simulation by clipping the outlets and discretizing into tetrahedral mesh. Steady BCs were defined in two steps: (i) rest BCs were extrapolated from clinical and image-derived data; (ii) hyperemic BCs were computed from resting conditions. Flow rate was iteratively adjusted during the simulation, until patient's aortic pressure was matched. Pulsatile BCs were defined exploiting the convergence values of steady BCs. After CFD simulation, lesion-specific hemodynamic indexes were computed and compared between group of patients for which surgery was indicated and not. The whole pipeline was implemented as a straightforward process, in which each single step is performed automatically.

Results

Steady and pulsatile FFR-CT yielded a strong correlation (r = 0.988, p < 0.001) and correlated with invasive FFR (r = 0.797, p < 0.001). The per-point difference between the pressure and FFR-CT field predicted by the two methods was below 1 % and 2 %, respectively. Both approaches exhibited a good diagnostic performance: accuracy was 0.860 and 0.864, the AUC was 0.923 and 0.912, for steady and pulsatile case, respectively. The computational time required by steady BCs CFD was approximatively 30-folds lower than pulsatile case.

Conclusions

This work shows the feasibility of using steady BCs CFD for computing the FFR-CT in coronary arteries, as well as its computational and diagnostic performance within a fully automated pipeline.

背景和目的有创分数血流储备(FFR)测量是诊断冠状动脉疾病(CAD)的金标准方法。FFR-CT 利用计算流体动力学(CFD)对 FFR 进行无创评估,在由计算机断层扫描(CT)重建的虚拟几何图形中模拟冠状动脉流动,但其计算过程成本高昂,且在定义患者特定边界条件(BCs)时存在不确定性。在这项工作中,我们研究了使用时间平均稳定边界条件(而不是脉冲边界条件)来减少计算时间,并采用了一种根据患者特定临床数据调整边界条件的自调整方法。对每条血管都进行了有创 FFR 测量。分割后,通过剪切出口并离散成四面体网格,为 CFD 模拟准备几何图形。稳定 BC 分两步定义:(i) 根据临床和图像数据推断静态 BCs;(ii) 根据静态条件计算充血 BCs。在模拟过程中反复调整流速,直到与患者的主动脉压力相匹配。脉动 BCs 是利用稳定 BCs 的收敛值定义的。CFD 模拟结束后,计算出病变特异性血流动力学指标,并在有手术指征和无手术指征的患者组之间进行比较。整个过程简单明了,每个步骤都是自动完成的。结果稳定和搏动 FFR-CT 产生了很强的相关性(r = 0.988,p <0.001),并与有创 FFR 相关(r = 0.797,p <0.001)。两种方法预测的压力场和 FFR-CT 场的每点差异分别低于 1 % 和 2 %。两种方法都表现出良好的诊断性能:准确率分别为 0.860 和 0.864,AUC 分别为 0.923 和 0.912。稳定 BCs CFD 所需的计算时间比脉冲式低约 30 倍。
{"title":"An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions","authors":"","doi":"10.1016/j.cmpb.2024.108415","DOIUrl":"10.1016/j.cmpb.2024.108415","url":null,"abstract":"<div><h3>Background and objective</h3><p>Invasive fractional flow reserve (FFR) measurement is the gold standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits computational fluid dynamics (CFD) for non-invasive evaluation of FFR, simulating coronary flow in virtual geometries reconstructed from computed tomography (CT), but suffers from cost-intensive computing process and uncertainties in the definition of patient specific boundary conditions (BCs). In this work, we investigated the use of time-averaged steady BCs, compared to pulsatile to reduce the computational time and deployed a self-adjusting method for the tuning of BCs to patient-specific clinical data.</p></div><div><h3>Methods</h3><p>133 coronary arteries were reconstructed form CT images of patients suffering from CAD. For each vessel, invasive FFR was measured. After segmentation, the geometries were prepared for CFD simulation by clipping the outlets and discretizing into tetrahedral mesh. Steady BCs were defined in two steps: (<em>i</em>) rest BCs were extrapolated from clinical and image-derived data; (<em>ii</em>) hyperemic BCs were computed from resting conditions. Flow rate was iteratively adjusted during the simulation, until patient's aortic pressure was matched. Pulsatile BCs were defined exploiting the convergence values of steady BCs. After CFD simulation, lesion-specific hemodynamic indexes were computed and compared between group of patients for which surgery was indicated and not. The whole pipeline was implemented as a straightforward process, in which each single step is performed automatically.</p></div><div><h3>Results</h3><p>Steady and pulsatile FFR-CT yielded a strong correlation (<em>r</em> = 0.988, <em>p</em> &lt; 0.001) and correlated with invasive FFR (<em>r</em> = 0.797, <em>p</em> &lt; 0.001). The per-point difference between the pressure and FFR-CT field predicted by the two methods was below 1 % and 2 %, respectively. Both approaches exhibited a good diagnostic performance: accuracy was 0.860 and 0.864, the AUC was 0.923 and 0.912, for steady and pulsatile case, respectively. The computational time required by steady BCs CFD was approximatively 30-folds lower than pulsatile case.</p></div><div><h3>Conclusions</h3><p>This work shows the feasibility of using steady BCs CFD for computing the FFR-CT in coronary arteries, as well as its computational and diagnostic performance within a fully automated pipeline.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724004085/pdfft?md5=041392929b671b0f9c1c0a15b7e05d66&pid=1-s2.0-S0169260724004085-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification Conv-RGNN:用于心电图分类的高效卷积残差图神经网络
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-03 DOI: 10.1016/j.cmpb.2024.108406

Background and objective:

Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular diseases (CVDs). It is important to consider both temporal and spatial features in ECG analysis to improve automated CVDs diagnosis. Significant progress has been made in automated CVDs diagnosis based on ECG with the continuous development of deep learning. Current most researches often treat 12-lead ECG signals as synchronous sequences in Euclidean space, focusing primarily on extracting temporal features while overlooking the spatial relationships among the 12-lead. However, the spatial distribution of 12-lead ECG electrodes can be more naturally represented using non-Euclidean data structures, which makes the relationships among leads more consistent with their intrinsic characteristics.

Methods:

This study proposes an innovative method, Convolutional Residual Graph Neural Network (Conv-RGNN), for ECG classification. The first step is to segment the 12-lead ECG into twelve single-lead ECG, which are then mapped to nodes in a graph that captures the relationships between the different leads through spatial connections, resulting in the 12-lead ECG graph. The graph is then used as input for Conv-RGNN. A convolutional neural network with a position attention mechanism is used to extract temporal sequence information and selectively integrate contextual information to enhance semantic features at different positions. The spatial features of the 12-lead ECG graph are extracted using the residual graph neural network.

Results:

The experimental results indicate that Conv-RGNN is highly competitive in two multi-label datasets and one single-label dataset, demonstrating exceptional parameter efficiency, inference speed, model performance, and robustness.

Conclusion:

The Conv-RGNN proposed in this paper offer a promising and feasible approach for intelligent diagnosis in resource-constrained environments.

背景和目的:心电图(ECG)分析是诊断心血管疾病(CVDs)的关键。在心电图分析中同时考虑时间和空间特征对于改善心血管疾病的自动诊断非常重要。随着深度学习的不断发展,基于心电图的心血管疾病自动诊断取得了重大进展。目前大多数研究通常将 12 导联心电图信号视为欧几里得空间中的同步序列,主要侧重于提取时间特征,而忽略了 12 导联之间的空间关系。然而,使用非欧几里得数据结构可以更自然地表示 12 导联心电图电极的空间分布,从而使导联之间的关系更符合其内在特征:本研究提出了一种用于心电图分类的创新方法--卷积残差图神经网络(Conv-RGNN)。第一步是将 12 导联心电图分割成 12 个单导联心电图,然后将这些单导联心电图映射到图中的节点,通过空间连接捕捉不同导联之间的关系,从而形成 12 导联心电图图。然后将该图作为 Conv-RGNN 的输入。具有位置关注机制的卷积神经网络用于提取时间序列信息,并选择性地整合上下文信息,以增强不同位置的语义特征。使用残差图神经网络提取 12 导联心电图图的空间特征:实验结果表明,Conv-RGNN 在两个多标签数据集和一个单标签数据集中具有很强的竞争力,在参数效率、推理速度、模型性能和鲁棒性方面都表现出了卓越的性能:本文提出的 Conv-RGNN 为资源受限环境下的智能诊断提供了一种前景广阔的可行方法。
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引用次数: 0
SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging SleepGCN:基于图卷积网络的睡眠分期过渡规则学习模型。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-02 DOI: 10.1016/j.cmpb.2024.108405

Background and Objective:

Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully explored the sleep transition rules that are essential for sleep experts to identify sleep stages. Therefore, one objective of this paper is to develop an automatic sleep staging model to capture the transition rules between sleep stages.

Methods:

In this paper, we propose a novel sleep staging model named SleepGCN. It utilizes the deep features of electroencephalogram (EEG) and electrooculogram (EOG) signals extracted by the sleep representation learning (SRL) module, in conjunction with the transition rules learned by the sleep transition rule learning (STRL) module to identify sleep stages. Specifically, the SRL module utilizes the residual network (ResNet) and Long Short Term Memory (LSTM) structure to capture the deep time-invariant features and temporal information of each sleep stage from the two-channel EEG-EOG, and then applies a feature enhancement block to obtain the refined features. The STRL module employs a Graph Convolutional Network (GCN) and a transition rule matrix to capture transition rules between sleep stages based on the sequence labels of the input signals.

Results:

We evaluate SleepGCN on five public datasets: SleepEDF-20, SleepEDF-78, SHHS, DOD-H and DOD-O. Overall, SleepGCN achieves an accuracy of 89.70%, 87.70%, 86.16%, 82.07%, and 81.20%, alongside a macro-average F1-score of 85.20%, 82.70%, 77.69%, 72.44%, and 72.93% across these datasets, respectively.

Conclusions:

The results achieved by our proposed model are much better than those of all other compared models. The ablation study validates the contributions of the SRL and STRL modules proposed in SleepGCN to the sleep staging tasks. Additionally, it shows that the sleep staging model using two-channel EEG-EOG outperforms the model using single-channel EEG or EOG. Overall, SleepGCN is an effective solution for sleep staging using two-channel EEG-EOG.

背景和目的:自动睡眠分期对于评估和诊断睡眠障碍至关重要,可为数百万睡眠障碍患者提供服务。最近提出了许多睡眠分期模型,但其中大多数都没有充分探讨睡眠过渡规则,而睡眠过渡规则对于睡眠专家识别睡眠阶段至关重要。因此,本文的目标之一就是开发一种自动睡眠分期模型,以捕捉睡眠阶段之间的过渡规则:本文提出了一种名为 SleepGCN 的新型睡眠分期模型。它利用睡眠表征学习(SRL)模块提取的脑电图(EEG)和脑电图(EOG)信号的深度特征,结合睡眠转换规则学习(STRL)模块学习到的转换规则来识别睡眠阶段。具体来说,SRL 模块利用残差网络(ResNet)和长短期记忆(LSTM)结构,从双通道脑电图-眼电图中捕捉每个睡眠阶段的深层时变特征和时间信息,然后应用特征增强模块获得细化特征。STRL模块采用图卷积网络(GCN)和过渡规则矩阵,根据输入信号的序列标签捕捉睡眠阶段之间的过渡规则:我们在五个公共数据集上对 SleepGCN 进行了评估:结果:我们在五个公开数据集上对 SleepGCN 进行了评估:SleepEDF-20、SleepEDF-78、SHHS、DOD-H 和 DOD-O。总体而言,SleepGCN 在这些数据集上的准确率分别为 89.70%、87.70%、86.16%、82.07% 和 81.20%,宏观平均 F1 分数分别为 85.20%、82.70%、77.69%、72.44% 和 72.93%:我们提出的模型所取得的结果远远优于所有其他比较模型。消融研究验证了 SleepGCN 中提出的 SRL 和 STRL 模块对睡眠分期任务的贡献。此外,研究还表明,使用双通道 EEG-EOG 的睡眠分期模型优于使用单通道 EEG 或 EOG 的模型。总之,SleepGCN 是使用双通道 EEG-EOG 进行睡眠分期的有效解决方案。
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引用次数: 0
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Computer methods and programs in biomedicine
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