Computer methods and programs in biomedicine最新文献

{"title":"Decoding metabolic reprogramming heterogeneity across bladder cancer stages using single-cell and spatial multi-omics approaches","authors":"Jiang Zhao ,&nbsp;Jingwei Zhao ,&nbsp;Wei Huang ,&nbsp;Weijie Lin ,&nbsp;Kuangzheng Jie ,&nbsp;Zihang Wu ,&nbsp;Benyi Li ,&nbsp;Lixin Fan ,&nbsp;Xiangwei Wang","doi":"10.1016/j.cmpb.2026.109261","DOIUrl":"10.1016/j.cmpb.2026.109261","url":null,"abstract":"<div><h3>Background</h3><div>Reprogramming of metabolic pathways represents a central indicator in cancer pathogenesis, but the metabolic heterogeneity of bladder cancer (BLCA) at different stages is not well understood. This study aims to analyze metabolic reprogramming in BLCA across stages and its impact on patient survival.</div></div><div><h3>Methods</h3><div>Single-cell sequencing data were used to examine metabolic heterogeneity of epithelial cells and cell subpopulation differentiation in BLCA at various clinical stages. Spatial transcriptome data were analyzed for copy number variability and riboflavin metabolism in BLCA epithelial cells. Bulk RNA sequencing data from BLCA patients were used for riboflavin pathway expression analysis and prognostic biomarker identification. The effects of three biomarkers (ENPP1, ACP1, and RFK) on BLCA risk were validated using RT-qPCR, Mendelian randomization and co-localization analysis.</div></div><div><h3>Results</h3><div>Epithelial cells exhibited significant metabolic heterogeneity during bladder cancer progression. Compared to normal control stage, riboflavin metabolic activity progressively increased with disease stage, as validated by spatial transcriptomics and bulk RNA-seq. High expression of ENPP1, ACP1, and RFK (riboflavin pathway) strongly correlated with poor overall survival. RT-qPCR confirmed their high expression in tumours, increasing with stage. Mendelian randomisation/co-localisation indicated these genes localise to bladder epithelium, and their genetic variation associates negatively with BLCA risk.</div></div><div><h3>Conclusion</h3><div>Increased riboflavin metabolism is likely to be an important marker of malignant progression in BLCA. The ENPP1, ACP1 and RFK genes in this pathway may serve as valuable prognostic biomarkers for BLCA, with potential implications for early diagnosis, monitoring disease progression, and guiding personalized treatment strategies.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109261"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074614","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":"Effect of Doppler ultrasound and high-frame-rate ultrasound particle image velocimetry derived inlet boundary conditions on wall shear stress parameters in the stented superficial femoral artery","authors":"Lisa Rutten ,&nbsp;Lennart van de Velde ,&nbsp;Lente Pol ,&nbsp;Kartik Jain ,&nbsp;Michel M.P.J. Reijnen ,&nbsp;Michel Versluis","doi":"10.1016/j.cmpb.2026.109259","DOIUrl":"10.1016/j.cmpb.2026.109259","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Hemodynamic predictions by computational fluid dynamics (CFD) strongly depend on inlet boundary conditions (IBC). One-dimensional Doppler ultrasound (DUS) is typically used for estimating flow IBCs, despite its sensitivity to the operator, ultrasound hardware and assumptions in flow rate computation. An alternative is two-dimensional high-frame-rate ultrasound particle image velocimetry (echoPIV). This study investigated the differences between DUS and echoPIV-derived IBCs and their effect on wall shear stress parameters in the stented superficial femoral artery.</div></div><div><h3>Methods</h3><div>CFD simulations using DUS and echoPIV-derived IBCs were performed for three patients with a superficial femoral artery stenosis that were treated with a stent. Spatiotemporal velocity profiles were compared at 0 – 50 mm from the inlet. Differences were quantified with the root-mean-square error (RMSE). Regions of low time-averaged wall shear stress (TAWSS) and high oscillatory shear index (OSI) using a literature-based threshold of 0.4 Pa and 0.2, respectively, and an IBC-specific threshold (lower third and upper third, respectively) were determined. Co-localization was quantified using the Jaccard similarity index.</div></div><div><h3>Results</h3><div>The DUS and echoPIV-derived IBCs differed in flow rate and velocity profile, with the largest difference found at peak systole (RMSE: &gt; 50 cm/s). Using the literature-based threshold, similarity in low TAWSS was high for two patients (0.85 – 0.88) and low for one (0.57). Agreement in high OSI was low in two patients (0.45 – 0.48) and high in one patient (0.83). The IBC-specific threshold increased the agreement for both low TAWSS and high OSI (≥0.75).</div></div><div><h3>Conclusions</h3><div>Differences in DUS and echoPIV-derived IBCs affected the TAWSS and OSI magnitudes. Regions of low TAWSS and high OSI corresponded well using an IBC-specific threshold. The literature-based threshold resulted in lower similarity values and different interpretations of restenosis risk that may cause differences in follow-up intensity or medical management.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109259"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024464","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":"Physiological-model-based neural network for modeling the metabolic–heart rate relationship during physical activities","authors":"Yaowen Zhang ,&nbsp;Libera Fresiello ,&nbsp;Peter H. Veltink ,&nbsp;Dirk W. Donker ,&nbsp;Ying Wang","doi":"10.1016/j.cmpb.2026.109240","DOIUrl":"10.1016/j.cmpb.2026.109240","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background and Objective:&lt;/h3&gt;&lt;div&gt;Heart failure (HF) poses a significant global health challenge, with early detection offering opportunities for improved outcomes. Abnormalities in heart rate (HR), particularly during daily activities, may serve as early indicators of HF risk. However, existing HR monitoring tools for HF detection are limited by their reliability on population-based averages. The estimation of individualized HR serves as a dynamic digital twin, enabling precise tracking of cardiac health biomarkers.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;div&gt;This study introduces a novel physiological-model-based neural network (PMB-NN) framework to model the oxygen uptake (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mtext&gt;V&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;̇&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;O&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;)-HR relationship during physical activities, establishing a physiology-grounded intermediate module for future daily life HR estimation from body movement signals. PMB-NN embeds physiological constraints, derived from our proposed simplified metabolic–HR physiological model (PM), into the neural network training process. The framework was trained and tested on individual datasets from 25 participants engaged in activities including resting, cycling, and running. HR estimation performance was evaluated for PMB-NN, comparing with benchmark fully connected neural network (FCNN) and PM, across three dimensions: numerical accuracy, physiological plausibility, and physiological interpretability. Furthermore, sensitivity analysis was conducted to verify the model’s robustness against input uncertainty.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;div&gt;The PMB-NN model adheres to human physiological principles while achieving high estimation accuracy, with median R&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; score of 0.88, RMSE of 9.96 bpm and MAE of 8.87 bpm, even in the presence of intermittent data. Comparative statistical analysis demonstrates that the PMB-NN achieves performance on par with FCNN while significantly outperforming PM (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mo&gt;&lt;&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;001&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;). Meanwhile, PMB-NN reaches higher plausibility for HR-&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mtext&gt;V&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;̇&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;O&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; coupling (&lt;span&gt;&lt;math&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; = 1) than both FCNN (p = 0.028) and PM (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mo&gt;&lt;&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;001&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;). Furthermore, PMB-NN is adept at identifying personalized parameters of the PM, enabling reasonable HR reconstruction. Sensitivity analysis reveals that PMB-NN yields an RMSE within 15 bpm despite input uncertainties of up to 20% Gaussian noise, 4% outliers, and an 18 s time lag.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion:&lt;/h3&gt;&lt;div&gt;This study confirms the validity of the PMB","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109240"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145965421","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":"Automated hemodynamic modeling to explore arterial curvature effects on intracranial aneurysm initiation","authors":"Adi Konsens ,&nbsp;Alejandro F. Frangi ,&nbsp;Gil Marom","doi":"10.1016/j.cmpb.2026.109245","DOIUrl":"10.1016/j.cmpb.2026.109245","url":null,"abstract":"<div><h3>Background and objective</h3><div>Intracranial aneurysms (IA) cause hundreds of thousands of deaths annually, yet most remain undiagnosed until rupture due to their asymptomatic nature. Improved prediction of aneurysm initiation could enable earlier detection and intervention. While computational hemodynamic models can identify high-risk regions, previous studies were limited to small cohorts due to labor-intensive manual workflows. We developed the first semi-automated workflow to enable large-scale, patient-specific hemodynamic analysis of IA initiation.</div></div><div><h3>Methods</h3><div>Our workflow integrates automated centerline extraction for quantitative morphological characterization with computational fluid dynamics (CFD) simulations to derive wall shear stress patterns and hemodynamic markers. We tested the workflow's robustness across multiple IA types and anatomical locations, focusing primarily on sidewall aneurysms of the internal carotid artery (ICA).</div></div><div><h3>Results</h3><div>Our semi-automated workflow successfully processed 42 diverse cases, 5 of them initially failed but were subsequently resolved through manual reconstruction, demonstrating robust performance across sidewall ICA aneurysms (16 cases), bifurcation aneurysms (6 cases), and validation cohorts. Validation against published data showed consistent trends with mean normalized TAWSS values of 1.31±0.09 in aneurysmal cases versus 1.14±0.07 in controls, aligning with previous findings despite methodological differences.</div></div><div><h3>Conclusions</h3><div>The workflow's adaptability was confirmed across multiple anatomical configurations and region of interest selection methods. This scalable approach enables the statistical analysis necessary to identify reliable hemodynamic biomarkers for IA initiation, representing a critical advancement towards evidence-based prediction models for clinical risk stratification.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109245"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975305","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":"Machine learning for the prediction of atrial fibrillation recurrence after catheter ablation: A systematic review and meta-analysis","authors":"Sofia M. Monteiro ,&nbsp;Patrícia Bota ,&nbsp;Pedro S. Cunha ,&nbsp;Mário M. Oliveira ,&nbsp;Sérgio Laranjo ,&nbsp;Hugo Plácido da Silva","doi":"10.1016/j.cmpb.2026.109249","DOIUrl":"10.1016/j.cmpb.2026.109249","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>This systematic review evaluates the current state of Machine Learning (ML) methods for predicting Atrial Fibrillation (AF) recurrence following catheter ablation. With the growing use of ML, a systematic evaluation of performance and key influencing factors such as study design, data types, and reporting is needed. The main objectives are to provide an updated overview of current achievements of ML in this field, anticipate future challenges and opportunities, and derive methodological recommendations based on the findings.</div></div><div><h3>Methods:</h3><div>Seven databases were systematically searched, and studies proposing ML algorithms with well-documented implementation, testing, and reporting of performance metrics underwent a qualitative synthesis and risk-of-bias assessment. A meta-analysis of 17 studies was conducted using the Area Under the receiver operating characteristic Curve (AUC) as the most commonly reported performance metric.</div></div><div><h3>Results:</h3><div>The mean overall AUC was 0.81, indicating reasonable predictive accuracy, although there was substantial inter-study heterogeneity. Meta-regression identified sample size and input data type (clinical, imaging, or electrophysiological) as significant contributors to this heterogeneity. Subgroup analysis demonstrated that models incorporating complex data modalities achieved higher predictive accuracy and lower heterogeneity compared to those relying solely on simpler clinical variables.</div></div><div><h3>Conclusion:</h3><div>This review quantifies the performance of ML algorithms in predicting AF recurrence and establishes a benchmark for future research. It also highlights key challenges, including the lack of standardized datasets and limited generalizability. Incorporating more complex data sources may improve model performance, reduce inconsistencies, and enhance the potential clinical applicability of ML models in guiding patient management.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109249"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145965449","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":"Noninvasive real-time dynamic monitoring of white blood cells based on microscopic imaging and deep learning","authors":"Fan Zhang ,&nbsp;Bingchen Yu ,&nbsp;Jianwei Zuo ,&nbsp;Rui Xu ,&nbsp;Kai Pang ,&nbsp;Wei Jin ,&nbsp;Jiajia Luo","doi":"10.1016/j.cmpb.2026.109238","DOIUrl":"10.1016/j.cmpb.2026.109238","url":null,"abstract":"<div><h3>Background and Objective</h3><div>White blood cells (WBCs) are key biomarkers of immune status, but current monitoring still relies on intermittent blood sampling and hematology analyzers, which are invasive and lack real-time, dynamic information. This work aims to develop a noninvasive system that continuously monitors WBC dynamics in nailfold microcirculation by combining a compact optical imaging device with deep learning–based detection and tracking.</div></div><div><h3>Methods</h3><div>We designed a portable microscopic imaging system that records high-frame-rate videos of nailfold capillaries under 532 nm illumination, where WBCs appear as bright optical gaps against the red blood cell column. From videos of 22 volunteers, we constructed dedicated vessel and WBC datasets and trained a two-stage YOLOv8-based detection framework that first localizes vascular regions and then detects WBCs within these regions. To enhance temporal consistency, we integrated a Flow-Guided Feature Aggregation module, and employed the ByteTrack multi-object tracking algorithm to assign unique IDs to WBCs and achieve real-time counting from streaming video. System performance was evaluated using mean average precision (mAP), precision, recall and F1-score.</div></div><div><h3>Results</h3><div>The proposed framework achieved accurate and stable vessel and WBC detection, with detection results closely matching manual annotations and maintaining robustness under motion blur and partial occlusion. The complete “detect–track–count” pipeline supports real-time analysis on a general computing platform while using only a compact optical device.</div></div><div><h3>Conclusions</h3><div>This study demonstrates a portable, noninvasive AI system that enables continuous in vivo monitoring of WBC dynamics in nailfold microcirculation without blood sampling. The approach provides a promising tool for scenarios requiring frequent WBC surveillance, such as chemotherapy monitoring and immune function assessment, and offers a transferable framework for other cell detection and microcirculation studies in medical imaging.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109238"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921974","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":"Hemolysis performance investigation of aortic valve pump based on computational fluid dynamics and entropy production theory","authors":"Teng Jing ,&nbsp;Aidi Pan ,&nbsp;Fangqun Wang,&nbsp;Weimin Ru,&nbsp;Md Rakibuzzaman,&nbsp;Ling Zhou","doi":"10.1016/j.cmpb.2025.109218","DOIUrl":"10.1016/j.cmpb.2025.109218","url":null,"abstract":"<div><h3>Background and objective</h3><div>Heart failure is a significant cause of cardiovascular disease, resulting in pathological changes in human blood circulation. Aortic heart valve pump is well investigated to become an important influencing factor for hemolysis and increase blood circulation capacity. Energy loss is inevitable during the process of blood temperature rise. However, when analyzing the energy loss during the operation of artificial heart pumps, efficiency formulas are often used to indirectly evaluate the total hydraulic loss, which cannot directly determine the source and specific distribution of energy loss in different parts. Therefore, this research introduces the numerical simulation and entropy production theory to analyze artificial heart pumps.</div></div><div><h3>Methods</h3><div>This paper explores the aortic valve pump's flow field characteristics and hemolysis performance. The computational fluid dynamics (CFD) method with the hemolysis prediction model is performed. Furthermore, entropy production theory was employed to analysis the flow field and obtain more details about shearing and transporting effects. Different valve pump impeller structures were analyzed and compared based on entropy production theory. The temperature distribution, energy loss mechanism inside the blood pump, and blood damage characteristics were determined.</div></div><div><h3>Results</h3><div>(1) The flow vortex and the impact of fluid on the blade are important reasons for the significant local entropy generation loss in the region. The inlet and outlet flow fields of the blood pump impeller and rear guide vane are relatively disordered, the main concentration area of entropy generation loss. (2) The wall entropy generation value accounts for a more significant proportion of the aortic valve pump, followed by dense dissipative entropy generation, dominated by turbulent dissipative entropy generation; heat transfer dissipative entropy generation has the most minor proportion. (3) The hemolysis index mainly depends on shear stress and exposure time, while the areas with high entropy output values are primarily concentrated in areas with long exposure time or large velocity gradients, which leads to increased shear stress.</div></div><div><h3>Conclusions</h3><div>The combined analysis of Computational Fluid Dynamics and entropy production theory can provide a specific reference value for the blood cell damage mechanism and optimization of blood pumps.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109218"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881675","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":"Dynamic mode decomposition as a framework for denoising ultrafast power doppler images","authors":"Baptiste Pialot ,&nbsp;Francesco Guidi ,&nbsp;Pauline Muleki-Seya ,&nbsp;Enrico Boni ,&nbsp;Alessandro Ramalli ,&nbsp;François Varray","doi":"10.1016/j.cmpb.2025.109221","DOIUrl":"10.1016/j.cmpb.2025.109221","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Imaging the morphology and hemodynamics of microvessels is critically important for the diagnosis and monitoring of various pathologies. Ultrafast Power Doppler (UPD) ultrasound is an emerging imaging modality for this purpose, offering a unique combination of portability, non-invasiveness, high temporal resolution, and real-time capability. However, UPD relies on unfocused wave transmission, which introduces high levels of uncorrelated noise compared to conventional Doppler imaging.</div></div><div><h3>Method</h3><div>We introduce a novel denoising approach for UPD imaging based on Dynamic Mode Decomposition (DMD), a data-driven algorithm originally developed for the analysis of spatiotemporal patterns in fluid dynamics. Using a new framework that links dynamic modes to ultrasound acquisitions, temporal signals corresponding to noisy modes are removed from ultrasound data prior to the calculation of the final UPD image. Based on an energy criterion, the number of discarded modes is adapted at the pixel level, resulting in local noise filtering. The method operates after beamforming and clutter filtering, making it compatible with standard ultrafast ultrasound pipelines, and requires only a single energy-thresholding parameter.</div></div><div><h3>Results</h3><div>We validated the DMD-based denoising method through simulations, phantom studies, and in vivo experiments. Compared to standard UPD images, our approach improved the signal-to-noise ratio by up to 26.0 dB and the contrast-to-noise ratio by up to 15.6 dB in vivo.</div></div><div><h3>Conclusion</h3><div>These results demonstrate that our DMD-based framework significantly enhances UPD image quality, enabling improved visualization of vessels. Beyond denoising, this method provides a principled foundation for advanced dynamic analysis in vascular ultrasound imaging.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109221"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877913","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":"Resolving high frequency fluctuations in cerebral aneurysm hemodynamics: the critical role of high-fidelity simulations and heart rate effects","authors":"Jana Korte ,&nbsp;Abouelmagd Abdelsamie ,&nbsp;Baha Al Deen El-Khader ,&nbsp;Nikhil Shirdade ,&nbsp;Ephraim W. Church ,&nbsp;Melissa C. Brindise ,&nbsp;Philipp Berg","doi":"10.1016/j.cmpb.2025.109219","DOIUrl":"10.1016/j.cmpb.2025.109219","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Despite their assumed laminar flow conditions, intracranial aneurysm (IA) hemodynamics can exhibit high frequency fluctuations, which recent studies have related to rupture risk. However, accurate detection of these fluctuations is challenging. Therefore, investigation of low and highly resolved numerical simulations to identify increased blood flow frequencies is fundamental for enhancing rupture risk assessments.</div></div><div><h3>Methods</h3><div>Highly resolved direct numerical simulations (DNS) and lower-resolution numerical simulations (LRNS) were conducted to assess IA hemodynamics under three representative heart rate frequencies in a patient-specific IA model (HR1: 60 bpm, HR2: 100 bpm, HR3: 137 bpm). The simulated flow fields were validated against particle tracking velocimetry. Flow instabilities were quantified by the power spectral density.</div></div><div><h3>Results</h3><div>The velocity fields obtained from both numerical approaches closely matched experimental data (mean v_norm=1 m/s at similar plane through IA). However, LRNS failed to capture intra-aneurysmal vorticity structures, whereas DNS successfully reproduced experimentally observed vorticity patterns. Both methods showed comparable root mean square values and time-resolved probe-wise results (highest differences: ∆0.08 m/s (HR1), ∆0.09 m/s (HR2-3).</div></div><div><h3>Conclusions</h3><div>DNS uniquely identified high frequency fluctuations in velocity detected with power spectral density. These fluctuations strengthened with increasing heart rates and were not captured by LRNS. Thus, it is suggested to consider high-fidelity setups when addressing IA rupture risk assessment.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109219"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921988","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":"Fitting high-dimensional mixture cure models using the hdcuremodels R package","authors":"Kellie J. Archer ,&nbsp;Han Fu","doi":"10.1016/j.cmpb.2025.109212","DOIUrl":"10.1016/j.cmpb.2025.109212","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Time-to-event outcomes are often of interest in biomedical studies. When the dataset includes long-term survivors or subjects who will not experience the event of interest, mixture cure models (MCMs) should be fit. Further, it is clinically relevant to identify molecular features from high-throughput assays that are associated with time-to-event outcomes, both to elucidate important pathways and to identify molecular features that may be therapeutic targets or for developing improved risk stratification systems. Herein, we describe our <strong>hdcuremodels</strong> <span>R</span> package that can be used to model right-censored time-to-event data when a cured fraction is present and the predictor space is high-dimensional.</div></div><div><h3>Methods:</h3><div>We implemented two different optimization methods, the expectation–maximization and generalized monotone incremental forward stagewise algorithms, for fitting high-dimensional penalized Weibull, exponential, and Cox mixture cure models. Cross-validation functions for each optimization method are provided that can be run with or without controlling the false discovery rate. The modeling functions are flexible in that there is no requirement for the predictors to be the same in the incidence and latency components of the model. The package also includes functions for testing mixture cure modeling assumptions, evaluating performance, and generic functions that can be used to extract meaningful results.</div></div><div><h3>Results:</h3><div>We demonstrate fitting a high-dimensional penalized mixture cure model to an acute myeloid leukemia dataset, which had strong predictive performance on an independent test set.</div></div><div><h3>Conclusion:</h3><div>Our <strong>hdcuremodels</strong> package fits penalized mixture cure models that can accommodate datasets where the number of predictors exceeds the sample size.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109212"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881684","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}