Computer methods and programs in biomedicine最新文献

{"title":"EndoUSScan: Keyframe detection in transvaginal ultrasound imaging for measuring endometrial thickness","authors":"Yiyang Liu ,&nbsp;Boyuan Peng ,&nbsp;Qin Zhou ,&nbsp;Suzhen Yuan ,&nbsp;Wei Yan ,&nbsp;Li Fang ,&nbsp;Jingjing Jiang ,&nbsp;Shixuan Wang ,&nbsp;Xin Zhu ,&nbsp;Wenwen Wang","doi":"10.1016/j.cmpb.2025.108957","DOIUrl":"10.1016/j.cmpb.2025.108957","url":null,"abstract":"<div><h3>Background:</h3><div>Accurate measurement of endometrial thickness (ET) using transvaginal ultrasound (TVUS) imaging is essential for diagnosing various gynecological conditions. However, manual ET measurement remains challenging, especially for junior physicians, due to variability in image quality and patient characteristics.</div></div><div><h3>Methods:</h3><div>A prospective observational study was performed using a dataset of 976 uterine ultrasound videos (82,063 images) measured in 2014-2019 in Tongji Hospital, Huazhong University of Science and Technology. We developed EndoUSScan, a comprehensive system for automated image selection and keyframe identification. The system incorporates MSNet, an improved DenseNet169-based system, to select candidate images with accurate endometrial representation. We also designed a keyframe detection system to assist junior medical staff in identifying frames with the largest ET from the candidate images. Comparative evaluations involved six junior sonographers, who assessed both speed and accuracy.</div></div><div><h3>Findings:</h3><div>MSNet achieved an accuracy of 94.7% and a specificity of 96.7% in selecting candidate images, outperforming conventional models including ResNet50, ResNet101, DenseNet121, and DenseNet169. The automatically selected keyframes were consistent with the expert-defined gold standard. Compared with manual procedures by junior sonographers, EndoUSScan significantly improved both the speed and accuracy of keyframe selection.</div></div><div><h3>Interpretation:</h3><div>This study presents the first fully automated and clinically validated system for keyframe detection in TVUS videos to support ET measurement. By standardizing the image selection process and assisting junior sonographers, EndoUSScan enhances diagnostic efficiency and accuracy, ultimately contributing to improved patient care.</div></div><div><h3>Funding:</h3><div>This study was funded by the <span>National Key Research and Development Program of China</span> (grant number <span><span>2022YFC2704100)</span></span> and <span>Knowledge Innovation Program of Wuhan -Basic Research</span> (No. <span><span>2023020201010041</span></span>).</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"271 ","pages":"Article 108957"},"PeriodicalIF":4.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LivXAI-Net: An explainable AI framework for liver disease diagnosis with IoT-based real-time monitoring support","authors":"Deepak Kumar ,&nbsp;Brijesh Bakariya ,&nbsp;Chaman Verma ,&nbsp;Zoltan Illes","doi":"10.1016/j.cmpb.2025.108950","DOIUrl":"10.1016/j.cmpb.2025.108950","url":null,"abstract":"<div><h3>Background &amp; Objective</h3><div>: Liver disease remains a significant global health burden, often progressing silently until advanced stages such as cirrhosis or hepatic failure. Early detection is essential but remains hindered by the limitations of conventional diagnostics. This study presents LivXAI-Net, an explainable artificial intelligence (XAI) framework integrated with Internet of Things (IoT) biosensors, designed and evaluated in a simulated real-time setting using a historical dataset.</div></div><div><h3>Methods:</h3><div>LivXAI-Net simulates continuous data acquisition from wearable biosensors — including sweat, platelet, and prothrombin sensors — and processes this data using machine learning (ML) models trained on the Mayo Clinic primary biliary cirrhosis (PBC) dataset (n <span><math><mo>=</mo></math></span> 424, 1974–1984). Random Forest (RF) and XGBoost(XGB) classifiers were deployed with SHAP and Permutation Feature Importance (PFI) to enhance interpretability. A mobile application, Hepatic Health Tracker, delivers real-time risk predictions, supported by a secure data pipeline using TLS 1.3 and AES-256 encryption.</div></div><div><h3>Results:</h3><div>RF and XGB achieved accuracies of 84% and 82% respectively under 20-fold cross-validation. Key biomarkers — albumin, cholesterol, and triglycerides — were consistently identified by SHAP as influential in classification. The system achieved a total latency of 0.85 s in a simulated 5G environment, supporting near-instantaneous alert delivery via the mobile interface.</div></div><div><h3>Conclusion:</h3><div>LivXAI-Net combines interpretable ML with real-time biosensor data to enable proactive liver disease management. While currently validated using historical data in a simulated environment, future work will involve deployment with live sensor input and clinical trials to validate utility and generalizability in real-world settings.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108950"},"PeriodicalIF":4.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing cardiac hemodynamic and pulsatility in heart failure via deep reinforcement learning: An in-silico and in-vitro validation study of percutaneous ventricular assist devices","authors":"Yuyang Shi ,&nbsp;Zhike Xu ,&nbsp;Chenghan Chen ,&nbsp;Feng He ,&nbsp;Pengfei Hao ,&nbsp;Xiwen Zhang","doi":"10.1016/j.cmpb.2025.108975","DOIUrl":"10.1016/j.cmpb.2025.108975","url":null,"abstract":"<div><h3>Background and objective</h3><div>Percutaneous ventricular assist devices (pVADs) are critical for bridging heart failure (HF) patients to recovery or transplantation, yet existing control strategies—constant speed control and preprogrammed pulsatile control—lack adaptability to dynamic physiological variations, leading to reduced pulsatility and hemodynamic mismatch.</div><div>This study proposes a deep reinforcement learning (DRL)-based adaptive control framework to optimize pVAD performance. The goal is to restore physiological pulsatile hemodynamics while autonomously adjusting to different HF conditions, heart rate fluctuations, and intra-cycle ejection phase variability.</div></div><div><h3>Methods</h3><div>Following a dual-validation pathway designed to bridge simulation with physical testing, a cardiovascular-pVAD in-silico model was developed and its fidelity confirmed against an in-vitro pulsatile mock circulatory loop. This validated platform was then used to design and test the DRL controller. A modified Deep Deterministic Policy Gradient (DDPG) algorithm with embedded LSTM layers was designed to capture temporal characteristics in aortic pressure (AOP) and aortic flow(AF) waveforms. The reward function integrated hemodynamic recoverability, pulsatile waveform similarity, and control stability and safety penalty.</div></div><div><h3>Results</h3><div>Comparative simulations and experiments demonstrated the DRL controller’s superiority over conventional strategies. Under the moderate HF condition, DRL controller achieved near-physiological AOP (DTW-AOP: 1.17 vs. 16.42 for constant speed control; 2.72 for preprogrammed pulsatile control) and AF (DTW-AF: 21.23 vs. 71.74/48.96), with pulsatility indices (PI: 1.69 vs. 1.05/1.54) and pulse pressures (PP: 34.42 mmHg vs. 3.20/24.90 mmHg) closely matching healthy reference. The framework exhibited robust adaptability to heart rate shifts (75→120 bpm) and ejection phase delays (0.1 s), maintaining stability despite sensor noise and physiological perturbations.</div></div><div><h3>Conclusions</h3><div>This DRL controller enables real-time synchronization with native cardiac cycles and generalization across pathologies, paving the way for precision pVAD support and future clinical translation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108975"},"PeriodicalIF":4.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fluid-structure interaction study of penetrating atherosclerotic ulcers: Assessing progression and rupture risks","authors":"Tian Wu ,&nbsp;Hong Zhu ,&nbsp;Haoyu Zhuo ,&nbsp;Jiang Li ,&nbsp;Jie Xiao","doi":"10.1016/j.cmpb.2025.108973","DOIUrl":"10.1016/j.cmpb.2025.108973","url":null,"abstract":"<div><h3>Background and objective</h3><div>Penetrating atherosclerotic ulcer (PAU) is a distinct pathology in acute aortic syndrome that may progress to aortic dissection or rupture. Conservative management, which primarily focuses on controlling blood pressure and heart rate, is prone to failure. The construction of numerical simulation models to predict and analyze the PAU progression and rupture risks will aid clinicians in understanding and improving PAU management and treatment.</div></div><div><h3>Methods</h3><div>In this study, fluid-structure interaction simulations were employed to investigate the biomechanical characteristics of PAUs under various conditions, aiming to assess their risks of rupture and progression. Further correlation analyses were conducted to identify dominant factors influencing ulcer progression. A quantitative comparison of progression risk was conducted between a single big PAU and two small PAUs with equivalent total volumes.</div></div><div><h3>Results</h3><div>The results demonstrate that as the PAU radius increases to 10 mm, the time-averaged wall shear stress (TAWSS) in the PAU dome region gradually decreases, falling significantly below the physiological range (1.0–3.0 Pa). Elevated blood pressure and heart rate primarily promote PAU rupture by influencing von Mises stress and displacement. Additionally, correlation analyses demonstrate that neither reducing blood pressure nor heart rate is sufficient to restore TAWSS to the physiological range (1.0–3.0 Pa). A single big PAU exhibits lower TAWSS in the dome region, indicating a higher progression risk compared to two small PAUs.</div></div><div><h3>Conclusion</h3><div>The evidence quantitatively supports the limitations of conservative management in halting PAU progression. Notably, a single big PAU carries a higher progression risk compared to two small PAUs, necessitating increased clinical intervention and monitoring.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108973"},"PeriodicalIF":4.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Muscle force imbalances predict clubfoot recurrence risk: A musculoskeletal modelling approach","authors":"Christian Greve ,&nbsp;Bryce A. Killen ,&nbsp;G.J.F. Joyce Bos ,&nbsp;Han Houdijk ,&nbsp;Sophie Moerman ,&nbsp;Alessio Murgia","doi":"10.1016/j.cmpb.2025.108972","DOIUrl":"10.1016/j.cmpb.2025.108972","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Idiopathic clubfoot, primarily treated with the Ponseti method, recurs in 20–40 % of cases, often within the first two years post-treatment. This recurrence is commonly linked to imbalances in the inverting and everting muscle and ligament forces surrounding the foot, disrupting ankle-foot joint alignment during gait. This study aimed to evaluate the biomechanical factors contributing to clubfoot recurrence using advanced musculoskeletal modeling.</div></div><div><h3>Methods</h3><div>Experimental data, including 3D segment positions, lower limb muscle activity, and ground reaction forces, were collected from a typically developing child walking at a comfortable speed. Optimal muscle fiber lengths and tendon slack lengths of ankle-foot muscles were subsequently optimized on a lower extremity musculoskeletal model. To address the research question, maximum isometric evertor muscle strength and invertor muscle stiffness were then systematically manipulated on the musculoskeletal model. Additionally, ankle-foot joint geometry was altered to mimic progressive clubfoot recurrence. The impact of these changes on the ankle-foot joint moment balances was assessed by inverse dynamic analysis.</div></div><div><h3>Results</h3><div>The musculoskeletal modelling approach was sensitive to subtle changes in muscle strength, joint stiffness, and joint geometry (10 %, 5 % and 3 degrees). In the absence of deforming inversion forces, only 30 % of normal evertor muscle strength was required to maintain typical ankle-foot kinematics. Increasing invertor muscle stiffness significantly increased evertor strength demands. An increase in inversion joint stiffness by &gt;30 % leads to an inability of the model to maintain typical ankle-foot kinematics. Changes in ankle, subtalar, and midfoot joint alignment mimicking clubfoot recurrence further influenced muscle moment arm balances, though increasing deformity did not reduce evertor muscle moment arms compared to invertor muscle moment arms.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the potential of advanced musculoskeletal modeling to uncover mechanical factors underlying clubfoot recurrence. The findings provide a foundation for incorporating patient-specific data in future research to validate clinical predictions. This approach could facilitate more personalized treatment strategies, improving long-term outcomes for children with recurrent clubfoot.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108972"},"PeriodicalIF":4.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep system prior based graph convolution network for NIR-II fluorescence molecular tomography","authors":"Beilei Wang ,&nbsp;Shuangchen Li ,&nbsp;Heng Zhang ,&nbsp;Jia Li ,&nbsp;Lizhi Zhang ,&nbsp;Jingjing Yu ,&nbsp;Xiaowei He ,&nbsp;Hongbo Guo","doi":"10.1016/j.cmpb.2025.108948","DOIUrl":"10.1016/j.cmpb.2025.108948","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Fluorescence molecular tomography (FMT) is a promising imaging technique that can quantify the internal distribution of tumor in the early stage. However, due to the ill-posed inverse problem caused by the severe photon scattering effect, the promotion of efficiency and accuracy is still an issue for FMT and the reconstruction of the morphological performance is still difficult to meet the practical requirement.</div></div><div><h3>Methods:</h3><div>In this paper, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering to alleviated ill-posedness, and a deep system prior based graph convolution network (DSPGN) was proposed for FMT, which fully takes the morphology represented by graph-structure into the reconstruction process to improve the morphological performance of FMT. Specifically, besides the single fluorescence image, the spatial prior of system represented by the nodes association and imaging system is also input into the network. Through feature extraction and embedding, more prior knowledge is incorporated into the reconstruction region. Then, a graph convolution network is adopted to make full use of the topological information of FMT data, coupled with an attention mechanism, the fluorescence source is reconstructed.</div></div><div><h3>Results:</h3><div>To evaluate the performance of DSPGN, numerical simulation and <em>in vivo</em> experiments were carried out. The results show that, compared to existing methods, DSPGN can achieve superior performance in terms of location accuracy and especially shape recovery capability.</div></div><div><h3>Conclusions:</h3><div>The proposed DSPGN has good ability for location and morphological fluorescence source recovery and has the potential to promote the application of FMT in NIR-II application.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108948"},"PeriodicalIF":4.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-track collaboration: Joint processing of heterogeneous positive and negative graph convolutional network for whole-slide image analysis","authors":"Meiyan Liang ,&nbsp;Xikai Wang ,&nbsp;Bo Li ,&nbsp;Shupeng Zhang ,&nbsp;Muhammad Hamza Javed ,&nbsp;Yuxuan Wang ,&nbsp;Xiaojun Jia ,&nbsp;Lin Wang","doi":"10.1016/j.cmpb.2025.108970","DOIUrl":"10.1016/j.cmpb.2025.108970","url":null,"abstract":"<div><h3>Background and objective</h3><div>Graph-based methods are widely applied in whole-slide histopathology images (WSI) analysis since they can effectively capture spatial relationship between nodes. However, existing methods focus on promoting positive nodes to have similar representations while ignoring the expression of negative samples of each node, failing to fully utilize various diagnostic information for comprehensive analysis.</div></div><div><h3>Methods</h3><div>In this paper, we propose a Dual Collaboration Heterogeneous Graph Convolutional Network (DCH-GCN) framework that considers both positive and negative samples implicit in whole-slide images (WSIs). Specifically, the framework consists of two complementary graphs: a positive edge homogeneous subgraph (PEH-subgraph), constructed using positive samples, and a negative edge heterogeneous subgraph (NEH-subgraph), built from negative samples. These two subgraphs collaboratively capture discriminative patch features within WSIs. The PEH-subgraph encourages spatially adjacent patches to learn similar feature representations, whereas the NEH-subgraph utilizes negative samples to enhance difference for patches exhibiting distinct morphology. In addition, we introduce a negative sample selection principle based on k-DPP and a two-stage instance clustering process to ensure the diversity and rationality of selected negative samples.</div></div><div><h3>Results</h3><div>Our method was evaluated on three public datasets, achieving an ACC of 0.937, AUC of 0.943, and F1 score of 0.952 on CAMELYON16 for cancer identification; an ACC of 0.923, AUC of 0.965, and F1 score of 0.926 on TCGA-NSCLC for subtype classification; and an ACC of 0.453, AUC of 0.648, and F1 score of 0.445 on TCGA-COAD for cancer staging.</div></div><div><h3>Conclusions</h3><div>Selecting appropriate negative and positive samples for each patch to construct DCH-GCN can more comprehensively represent the topological information of WSI images and improve the overall prediction performance.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108970"},"PeriodicalIF":4.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing gastric cancer prognosis prediction via multi-step multi-modality ensemble survival modeling of HE-stained images and mIHC data","authors":"Ruitian Gao ,&nbsp;Xin Yuan ,&nbsp;Yihan Sun ,&nbsp;Yifan Wang ,&nbsp;Yujia Xia ,&nbsp;Ting Wei ,&nbsp;Dakang Xu ,&nbsp;Zhangsheng Yu","doi":"10.1016/j.cmpb.2025.108971","DOIUrl":"10.1016/j.cmpb.2025.108971","url":null,"abstract":"<div><h3>Background</h3><div>Gastric cancer's profound molecular and phenotypic heterogeneity hinders precision therapy. Delineating tumor microenvironment (TME) heterogeneity enables improved prognosis and tailored treatment strategies. In this study, we propose a Multi-step Multi-modality Ensemble Survival (MMES) model for gastric cancer prognosis based on HE-stained histological images and multiplex immunohistochemistry (mIHC) data.</div></div><div><h3>Methods</h3><div>The MMES model employs a multi-step approach to decompose the complex overall survival (OS) prediction task into smaller, manageable subtasks, enabling the use of lightweight models at each stage. To integrate multi-modal information, the model leverages a pathology foundation model to extract tissue texture features from HE-stained images and utilizes hypergraph neural networks (HGNN) to capture spatial distribution pattern of immune-related molecules from cell-hypergraphs constructed from mIHC data. The MMES model was trained and tested using data of 456 gastric cancer patients across five batches of tissue microarrays (TMAs) from multiple centers. We further expanded the input of the MMES model from TMAs to HE-stained whole slide images (WSIs) of 354 patients in The Cancer Genome Atlas (TCGA) gastric cancer cohort.</div></div><div><h3>Results</h3><div>Experimental results on our in-house TMA dataset demonstrate that the proposed approach surpasses traditional end-to-end multi-modality modeling strategies, with a 9.9 % increase in concordance index (C-index) and an 11.3 % improvement in mean time-dependent AUC, underscoring the advantages of multi-step modeling. The integration of multi-modal data further enhances the robustness of survival prediction, achieving a 5.4 % higher C-index compared to the average performance of single-modality modeling strategies. Notably, the risk score generated by the MMES model serves as an independent prognostic indicator for gastric cancer patients. Beyond this, results on TCGA data show that the risk score extracted from WSIs is also a strong independent prognostic marker.</div></div><div><h3>Conclusion</h3><div>The MMES model, through multi-step modeling and the effective integration of HE-stained images and mIHC data, demonstrates significant potential as a reliable and independent prognostic tool for gastric cancer, with promising future applications in WSI-scale multimodal data analysis.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108971"},"PeriodicalIF":4.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of mechanical wall stress and wall shear stress on coronary artery disease","authors":"Aikaterini Tziotziou ,&nbsp;Amalia de Juana Fabra ,&nbsp;Ayla Hoogendoorn ,&nbsp;Suze-Anne Korteland ,&nbsp;Aad van der Lugt ,&nbsp;Antonius F.W. van der Steen ,&nbsp;Daniel Bos ,&nbsp;Jolanda J. Wentzel ,&nbsp;Ali C. Akyildiz","doi":"10.1016/j.cmpb.2025.108968","DOIUrl":"10.1016/j.cmpb.2025.108968","url":null,"abstract":"<div><h3>Background and objective</h3><div>Although the association of wall shear stress (WSS) with coronary artery disease has been well studied, that of mechanical wall stress (MWS) is mainly overlooked. In this study, we performed in-silico artery-specific modeling to investigate the involvement of both MWS and WSS in coronary artery disease.</div></div><div><h3>Methods</h3><div>Fifteen coronary arteries from five adult familial hypercholesterolemic pigs were imaged by coronary computed tomography angiography, intravascular ultrasound, and optical coherence tomography at three time points (3, 9, and 12 months). Local WSS and MWS in 3 mm/45° sectors were determined using artery-specific computational models. The relationship of WSS and MWS with wall thickness change (ΔWT) over time was statistically analyzed using Generalized Linear Mixed models.</div></div><div><h3>Results</h3><div>A positive ΔWT was measured in all sectors, where plaque sectors presented a greater ΔWT rate compared to plaque-free sectors. In plaque-free sectors, low WSS was associated with a higher ΔWT rate (<em>p</em> &lt; 0.001). In plaque sectors, high MWS was associated with a higher ΔWT rate (<em>p</em> &lt; 0.05), where ΔWT rate was, although slightly, even greater in the plaque sectors with lipid-rich necrotic core (<em>p</em> &lt; 0.05).</div></div><div><h3>Conclusions</h3><div>Our results from in-silico coronary-specific models suggest that WSS and MWS may play a dominant role at different stages of coronary artery disease. WSS may be more critical in the early stages of plaque formation while MWS might have greater significance in the progression of existing plaques.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108968"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-silico patient-specific modelling of prostate cancer: Predicting PSA dynamics and treatment response","authors":"Ángela Pérez-Benito ,&nbsp;Adrián Galiana-Bordera ,&nbsp;Pedro-Miguel Martínez-Gironés ,&nbsp;Gemma Urbanos ,&nbsp;Anna Nogué Infante ,&nbsp;María José Gómez-Benito ,&nbsp;María Ángeles Pérez","doi":"10.1016/j.cmpb.2025.108931","DOIUrl":"10.1016/j.cmpb.2025.108931","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Prostate cancer remains a significant global health concern, with treatment response varying among patients. Radiotherapy, often combined with hormone therapy, is a key treatment approach, but predicting individual outcomes remains challenging. Computational models have emerged as valuable tools to simulate tumour behaviour and optimise treatment strategies. This study presents a patient-specific computational model designed to predict tumour response by associating Prostate-Specific Antigen (PSA) dynamics with tumour biological behaviour under therapy.</div></div><div><h3>Methods:</h3><div>The model integrates patient-specific clinical data and imaging biomarkers from a retrospective study, including apparent diffusion coefficient values from diffusion-weighted imaging to represent tumour cellularity and perfusion parameters from dynamic contrast-enhanced MRI to characterise vascular properties. Clinical data from five patients undergoing radiotherapy, hormone therapy, or combination therapy are used for model development and validation. Due to the limited availability of patient data, PSA is the only parameter used for calibration and validation. One patient is used for calibration, while six serve for validation. Model performance is evaluated by calculating the mean absolute error (MAE) between simulated and observed PSA values post-treatment. The model also estimates tumour shrinkage, though this cannot be directly validated. To assess predictive capacity, two patients are selected for additional analysis simulating different treatment strategies and their impact on PSA dynamics and tumour shrinkage.</div></div><div><h3>Results:</h3><div>The model successfully replicates PSA trends, with MAE values of 0.1, 0.08, 0.23, 0.14, 0.11 and 0.15 ng/mL and RMSE of 0.18, 0.15, 0.24, 0.18, 0.12 and 0.15 ng/mL for the six validation patients, with Patient C showing the closest correspondence to clinical data (MAE = 0.08). Overall, the MAE ranges from 0.08 ng/mL to 0.23 ng/mL, indicating the model’s ability to approximate treatment response. In the two selected patients, simulated treatment variations result in distinct PSA dynamics and estimated tumour shrinkage, highlighting interpatient variability in treatment response.</div></div><div><h3>Conclusions:</h3><div>This computational model provides a predictive framework for assessing prostate cancer treatment response based on patient-specific PSA dynamics and imaging biomarkers. While tumour shrinkage estimates cannot be validated, the model offers insights into treatment-induced PSA fluctuations. The findings support the potential of <em>in-silico</em> tools in personalised medicine, aiding clinical decision-making by evaluating different therapeutic strategies. Further validation with larger datasets is necessary for clinical integration.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108931"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}