Computer methods and programs in biomedicine最新文献_第10页

FRET-SAM: SAM_Med2D-based automatic FRET two-hybrid analysis FRET- sam：基于sam_med2d的自动FRET双混合分析。

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-12-13 DOI: 10.1016/j.cmpb.2025.109208

Jingzhen Wang , Yanling Xu , Beini Sun, Zhiqiang Wei, Rumeng Qu, Fengting Wang, Zhengfei Zhuang, Min Hu, Tongsheng Chen

Background and Objective:

The fluorescence resonance energy transfer (FRET) two-hybrid assay enables quantification of the stoichiometry and binding affinity of protein interactions directly in living cells, but its broader application remains constrained by labor-intensive manual image analysis and high computational complexity. This study leverages deep learning to accurately extract FRET two-hybrid image signals and automate the FRET two-hybrid analysis process, thereby eliminating subjective bias and enhancing the method’s efficiency and accuracy.

Methods:

Based on the Segment Anything Model (SAM), we developed FRET-SAM, an optimized analysis method adapting SAM_Med2D’s structure for automated regions of interest (ROI) selection and fluorescence signal extraction in FRET two-hybrid images. A comprehensive FRET image dataset was established, including six model plasmids (C4Y, C10Y, C40Y, C80Y, C32V and CVC) and three functional FRET pairs (Bcl-XL-CFP/Bak-YFP, EGFR-CFP/Grb2-YFP and RAF-CFP/RAS-YFP), for model training and validation. Model segmentation performance was assessed by comparing its mean pixel accuracy (MPA), mean intersection over union (MIoU), and Dice coefficient against the original SAM_Med2D model. To assess protein interaction results, FRET-SAM-derived values were compared to established literature values, using relative error as a key metric of consistency.

Results:

The FRET-SAM model exhibited enhanced segmentation accuracy, with MPA, MIoU, and Dice coefficient increasing by 2.88%, 2.36%, and 2.19%, respectively, compared to the original SAM_Med2D model. Validation experiments demonstrated high consistency between FRET-SAM-derived results and literature values, with all plasmid models exhibiting relative errors that were individually calculated and confirmed to be under 5%. Furthermore, FRET-SAM exhibited robust drug screening potential in three biomedical case studies: (1) EGFR-Grb2-targeted lung cancer intervention (gefitinib), (2) RAS-RAF-mediated hepatocellular carcinoma suppression (sorafenib), and (3) Bcl-XL inhibitors discovery (A-1331852). Mechanistic studies confirmed its ability to resolve drug-target interactions.

Conclusions:

By enabling automated analysis of FRET images, FRET-SAM significantly enhances the efficiency and accuracy of FRET two-hybrid assays, while eliminating subjective bias. The capability of FRET-SAM to resolve drug-target interactions establishes it as a promising tool for drug discovery.

背景和目的：荧光共振能量转移（FRET）双杂交分析能够直接定量测定活细胞中蛋白质相互作用的化学计量学和结合亲和力，但其广泛应用仍然受到劳动密集型人工图像分析和高计算复杂性的限制。本研究利用深度学习准确提取FRET双混合图像信号，实现FRET双混合分析过程的自动化，从而消除了主观偏差，提高了方法的效率和准确性。方法：基于片段任意模型（SAM），利用SAM_Med2D的结构，开发了一种用于自动选择感兴趣区域（ROI）和提取荧光信号的优化分析方法——FRET-SAM。建立了完整的FRET图像数据集，包括6个模型质粒（C4Y、C10Y、C40Y、C80Y、C32V和CVC）和3个功能FRET对（Bcl-XL-CFP/ bank - yfp、EGFR-CFP/Grb2-YFP和RAF-CFP/RAS-YFP），用于模型训练和验证。通过对比SAM_Med2D模型的平均像素精度（MPA）、平均交集比（MIoU）和Dice系数来评估模型的分割性能。为了评估蛋白质相互作用结果，将fret - sam衍生值与已建立的文献值进行比较，使用相对误差作为一致性的关键指标。结果：与原始SAM_Med2D模型相比，FRET-SAM模型的MPA、MIoU和Dice系数分别提高了2.88%、2.36%和2.19%，分割精度有所提高。验证实验表明，fret - sam衍生的结果与文献值高度一致，所有质粒模型都显示出单独计算并确认在5%以下的相对误差。此外，FRET-SAM在三个生物医学案例研究中显示出强大的药物筛选潜力：(1)egfr - grb2靶向肺癌干预（吉非替尼），(2)ras - raf介导的肝细胞癌抑制（索拉非尼），以及(3)Bcl-XL抑制剂发现（A-1331852）。机制研究证实了其解决药物-靶标相互作用的能力。结论：通过自动分析FRET图像，FRET- sam显著提高了FRET双杂交分析的效率和准确性，同时消除了主观偏差。FRET-SAM解决药物-靶标相互作用的能力使其成为药物发现的有前途的工具。

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引用次数: 0

A novel multimodal diagnostic framework integrating hyperspectral imaging and deep learning for predicting RET gene mutations in medullary thyroid carcinoma 结合高光谱成像和深度学习预测甲状腺髓样癌RET基因突变的新型多模态诊断框架。

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-12-13 DOI: 10.1016/j.cmpb.2025.109207

Zhenpeng Yang , Peng Su , Yuyang Zhang , Haitao Zheng , Yupeng Deng , Xiangfeng Lin , Changyuan Ding , Wei Li , Weili Liang , Bin Lv

Background and objective

Medullary thyroid carcinoma (MTC) is an aggressive malignancy driven predominantly by activating mutations in the RET proto-oncogene. Conventional genotyping using polymerase chain reaction (PCR) or next-generation sequencing (NGS) is often hampered by burdensome costs and prolonged turnaround times, hindering timely clinical decision-making.

Methods

We developed a rapid, cost-effective, multimodal deep-learning framework to predict RET mutations from standard H&E-stained slides. Our approach leverages hyperspectral imaging and integrates a 1D-CNN-LSTM network for spectral analysis with a Swin Transformer for spatial feature extraction. A cross-modal attention mechanism effectively fuses these representations. The model was trained and validated on 82 MTC cases from Qilu Hospital and externally tested on independent cohorts from two additional centers (n = 60).

Results

The proposed framework achieved an overall accuracy of 89.5 %, with a sensitivity of 90.2 % and specificity of 88.6 % for RET mutation classification. External validation confirmed robust generalizability, with performance surpassing single-modality benchmarks by 7.0–19.5 %.

Conclusions

This study presents a non-invasive and efficient alternative for predicting RET mutations in MTC, demonstrating the potential of hyperspectral imaging and integrated deep learning to advance precision oncology.

背景和目的：甲状腺髓样癌（MTC）是一种主要由RET原癌基因激活突变驱动的侵袭性恶性肿瘤。使用聚合酶链反应（PCR）或下一代测序（NGS）的传统基因分型常常受到沉重的成本和较长的周转时间的阻碍，阻碍了及时的临床决策。方法：我们开发了一种快速、经济、多模式的深度学习框架，从标准h&e染色的载玻片中预测RET突变。我们的方法利用高光谱成像，并将用于光谱分析的1D-CNN-LSTM网络与用于空间特征提取的Swin变压器集成在一起。跨模态注意机制有效地融合了这些表征。该模型在齐鲁医院的82例MTC病例中进行了训练和验证，并在另外两个中心的独立队列中进行了外部测试（n = 60）。结果：该框架对RET突变分类的总体准确率为89.5%，敏感性为90.2%，特异性为88.6%。外部验证证实了鲁棒的泛化性，性能优于单模态基准7.0- 19.5%。结论：本研究提出了一种非侵入性和有效的预测MTC RET突变的替代方法，展示了高光谱成像和集成深度学习在推进精准肿瘤学方面的潜力。

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引用次数: 0

Explainable feature selection combining particle swarm optimisation with adaptive LASSO for MRI radiogenomics: Predicting HPV status in oropharyngeal cancer 结合粒子群优化和自适应LASSO的MRI放射基因组学的可解释特征选择：预测口咽癌中的HPV状态。

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.cmpb.2025.109204

Milad Ahmadian , Zuhir Bodalal , Mary Adib , Seyed Sahand Mohammadi Ziabari , Paula Bos , Roland M. Martens , Georgios Agrotis , Conchita Vens , Luc Karssemakers , Abrahim Al-Mamgani , Pim de Graaf , Bas Jasperse , Ruud H Brakenhoff , C René Leemans , Regina G.H. Beets-Tan , Michiel W.M. van den Brekel , Jonas A. Castelijns

Background

Radiogenomic modelling faces a significant challenge due to the high-dimensional nature of quantitative radiomic features and limited sample sizes. Feature selection is therefore essential to eliminate irrelevant features and mitigate overfitting. Particle swarm optimisation (PSO) has shown promise for effectively navigating large feature spaces, yet its effectiveness in radiogenomics remains unexplored. This study investigates the value of PSO-based methods, both independently and in combination with other advanced techniques, for MRI-based prediction of human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC).

Materials and methods

Baseline contrast-enhanced T1-weighted MR scans from two centres were analysed: 153 patients in an internal cohort (randomly split into 80 % for training and 20 % for testing) and 157 patients in an external validation cohort. Radiomic features were extracted from manually segmented tumours and multiple feature selection methods, including PSO and its ensembles, filter-based methods, wrapper-based approaches, and shrinkage techniques were evaluated. Performance was measured and compared using the area under the receiver operating characteristic curve (AUC).

Results

PSO alone had a reasonable predictive power on the internal test set (AUC = 0.76, 95 % CI: 0.57–0.92, p = 0.092). When combined with adaptive LASSO using Shapley values, PSO’s performance improved (AUC = 0.81, 95 % CI: 0.61–0.94, p = 0.023). Recursive feature elimination (RFE) selected the most relevant features (AUC = 0.91, 95 % CI: 0.79–1.00, p < 0.001). Despite this, RFE failed to generalise well to the external cohort (AUC = 0.52, 95 % CI: 0.42–0.60, p = 1). Meanwhile, the PSO–adaptive LASSO combination maintained a robust AUC = 0.78 (95 % CI: 0.70–0.85, p < 0.001), indicating superior generalisability.

Conclusions

The explainable PSO–adaptive LASSO feature selection method provides generalisable radiogenomic signatures associated with HPV status in OPSCC, outperforming other feature selection approaches. This combination may serve as a robust strategy for developing transferable models in radiogenomics.

背景：由于定量放射组学特征的高维性质和有限的样本量，放射基因组学建模面临着重大挑战。因此，特征选择对于消除不相关特征和减轻过度拟合至关重要。粒子群优化（PSO）已经显示出有效导航大特征空间的希望，但其在放射基因组学中的有效性仍有待探索。本研究探讨了基于pso的方法，无论是独立的还是与其他先进技术相结合，在基于mri预测口咽鳞状细胞癌（OPSCC）中人乳头瘤病毒（HPV）状态的价值。材料和方法：分析了来自两个中心的基线对比增强t1加权MR扫描：153名患者在内部队列（随机分为80%用于训练和20%用于测试）和157名患者在外部验证队列。从人工分割的肿瘤中提取放射学特征，并评估了多种特征选择方法，包括PSO及其集合、基于过滤器的方法、基于包装的方法和收缩技术。使用接收器工作特性曲线（AUC）下的面积来测量和比较性能。结果：PSO单独在内部测试集上具有合理的预测能力（AUC = 0.76, 95% CI: 0.57-0.92, p = 0.092）。当使用Shapley值与自适应LASSO结合使用时，PSO的性能得到改善（AUC = 0.81, 95% CI: 0.61-0.94, p = 0.023）。递归特征消除（RFE）选择了最相关的特征（AUC = 0.91, 95% CI: 0.79-1.00, p < 0.001）。尽管如此，RFE未能很好地推广到外部队列（AUC = 0.52, 95% CI: 0.42-0.60, p = 1）。同时，pso -自适应LASSO组合保持稳健的AUC = 0.78 (95% CI: 0.70-0.85, p < 0.001)，表明具有较好的通用性。结论：可解释的pso自适应LASSO特征选择方法提供了与OPSCC中HPV状态相关的通用放射基因组特征，优于其他特征选择方法。这种组合可以作为在放射基因组学中开发可转移模型的有力策略。

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引用次数: 0

Multiscale simulation and parallel space–time adaptivity of calcium sparks in cardiac myocytes 心肌细胞钙火花的多尺度模拟及平行时空适应性研究。

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-11-07 DOI: 10.1016/j.cmpb.2025.109154

Wilhelm Neubert , Martin Falcke , Nagaiah Chamakuri

Background and Objective:

Calcium serves as the bidirectional link between the heart’s electrical excitation and contraction. Electrical excitation induces an influx of Calcium across the sarcolemma and T-tubular membrane, triggering calcium release from the sarcoplasmic reticulum. Calcium sparks, the fundamental events of calcium release from the SR, are initiated in specialized microdomains where Ryanodine Receptors and L-type calcium channels co-locate. The spatial heterogeneity of Calcium release and the random occurrence of strong release fluxes render simulations challenging. Developing mathematical models and efficient simulations of detailed calcium spark models is crucial to understanding heart function. In this paper, we introduce space–time adaptivity within a parallel computing framework into the multiscale simulation of calcium sparks in cardiac myocytes to improve the stability and performance of these simulations.

Methods:

We model intracellular calcium concentrations in both the cytoplasm and the SR domains using a set of coupled reaction–diffusion equations. Spatial grid adaptivity is implemented through multilevel finite element methods to account for the spatial heterogeneity of intracellular

{Ca}^{2 +}

release. Rosenbrock-type techniques handle small time steps for simulating stochastic channel opening and closing in the

{Ca}^{2 +}

release units (CRUs).

Results:

Our test cases demonstrate the superior efficiency of the space–time adaptive approach in optimizing computational resources. The parallel space–time adaptive method accelerates simulations of calcium sparks by a factor of 16.07.

Conclusions:

The efficiency and speed gains in Calcium spark simulations are significant and enable modeling based research into previously difficult to tackle questions with regard to sub-micrometer scale models, e.g with respect to local interactions between the Sodium Calcium Exchanger and RyR clusters.

背景与目的：钙在心脏的电兴奋和电收缩之间起着双向联系的作用。电兴奋诱导钙通过肌膜和t管膜流入，触发钙从肌浆网释放。钙火花是钙从SR释放的基本事件，在Ryanodine受体和l型钙通道共同定位的特定微域中启动。钙释放的空间异质性和强释放通量的随机发生使得模拟具有挑战性。建立数学模型和有效模拟详细的钙火花模型对了解心脏功能至关重要。本文在并行计算框架下，将时空自适应引入心肌细胞钙火花的多尺度模拟中，以提高模拟的稳定性和性能。方法：我们使用一组耦合反应-扩散方程来模拟细胞质和SR结构域的细胞内钙浓度。空间网格适应性是通过多层次的有限元方法来实现的，以解释细胞内Ca2+释放的空间异质性。rosenbrock型技术处理小时间步模拟Ca2+释放单元（cru）中的随机通道打开和关闭。结果：我们的测试用例证明了时空自适应方法在优化计算资源方面的优越效率。平行时空自适应方法将钙火花的模拟速度提高了16.07倍。结论：钙火花模拟的效率和速度的提高是显著的，并且使基于建模的研究能够解决以前难以解决的关于亚微米尺度模型的问题，例如关于钠钙交换器和RyR簇之间的局部相互作用。

{"title":"Multiscale simulation and parallel space–time adaptivity of calcium sparks in cardiac myocytes","authors":"Wilhelm Neubert , Martin Falcke , Nagaiah Chamakuri","doi":"10.1016/j.cmpb.2025.109154","DOIUrl":"10.1016/j.cmpb.2025.109154","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Calcium serves as the bidirectional link between the heart’s electrical excitation and contraction. Electrical excitation induces an influx of Calcium across the sarcolemma and T-tubular membrane, triggering calcium release from the sarcoplasmic reticulum. Calcium sparks, the fundamental events of calcium release from the SR, are initiated in specialized microdomains where Ryanodine Receptors and L-type calcium channels co-locate. The spatial heterogeneity of Calcium release and the random occurrence of strong release fluxes render simulations challenging. Developing mathematical models and efficient simulations of detailed calcium spark models is crucial to understanding heart function. In this paper, we introduce space–time adaptivity within a parallel computing framework into the multiscale simulation of calcium sparks in cardiac myocytes to improve the stability and performance of these simulations.</div></div><div><h3>Methods:</h3><div>We model intracellular calcium concentrations in both the cytoplasm and the SR domains using a set of coupled reaction–diffusion equations. Spatial grid adaptivity is implemented through multilevel finite element methods to account for the spatial heterogeneity of intracellular <span><math><msup><mrow><mtext>Ca</mtext></mrow><mrow><mn>2</mn><mo>+</mo></mrow></msup></math></span> release. Rosenbrock-type techniques handle small time steps for simulating stochastic channel opening and closing in the <span><math><msup><mrow><mtext>Ca</mtext></mrow><mrow><mn>2</mn><mo>+</mo></mrow></msup></math></span> release units (CRUs).</div></div><div><h3>Results:</h3><div>Our test cases demonstrate the superior efficiency of the space–time adaptive approach in optimizing computational resources. The parallel space–time adaptive method accelerates simulations of calcium sparks by a factor of 16.07.</div></div><div><h3>Conclusions:</h3><div>The efficiency and speed gains in Calcium spark simulations are significant and enable modeling based research into previously difficult to tackle questions with regard to sub-micrometer scale models, e.g with respect to local interactions between the Sodium Calcium Exchanger and RyR clusters.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109154"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145494854","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}

引用次数: 0

Drug repurposing through pathway perturbation dynamics: A systems biology approach for precision oncology 通过途径微扰动力学的药物再利用：精确肿瘤学的系统生物学方法。

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-11-20 DOI: 10.1016/j.cmpb.2025.109177

Xianbin Li , Wuxiang Ruan , Guoan Lu , Dingcheng Ban , Luming Tian , Binbin Wang , Tao Liu , Guodao Zhang , Chunping Wang , Jie Lin

Background and objective

Drug repurposing offers a cost-efficient strategy to discover new therapeutic applications for approved drugs. While current computational strategies prioritize candidates by targeting disease-related pathways, they often fail to quantitatively model pathway perturbation dynamics—a critical gap that limits mechanistic interpretability.

Methods

To address this issue, we presented PathPertDrug, a novel framework that systematically identifies cancer drug candidates by quantifying functional antagonism between drug-induced and disease-associated pathway perturbations (activation/ inhibition). By integrating drug-induced gene expression, disease-related gene expression, and pathway information, PathPertDrug evaluated pathway-level functional reversals, enabling precise prediction of drug-disease associations.

Results

Our method demonstrated superior predictive accuracy and robustness across pan-cancer benchmarks. It achieved a higher median AUROC (0.62 vs. 0.42–0.53) and a substantial improvement in AUPR (3–23 %) over existing methods. The consistent AUPR enhancement, particularly under class imbalance, underscores the robustness of our model in reliably prioritizing true positive associations. Validated by the comparative toxicogenmics database, PathPertDrug rediscovered 83 % of literature-supported cancer drugs (e.g., fulvestrant (Fulvestrant) for colorectal cancer) and predicted novel candidates (e.g., rifabutin–lung cancer).

Conclusions

This pathway-centric approach bridged mechanistic insights with translational applications, providing a paradigm shift for precision oncology drug discovery.

背景和目的：药物再利用为发现已批准药物的新治疗应用提供了一种具有成本效益的策略。虽然目前的计算策略通过针对疾病相关的途径来优先考虑候选者，但它们往往无法定量地模拟途径扰动动力学——这是一个限制机制可解释性的关键缺陷。方法：为了解决这个问题，我们提出了PathPertDrug，这是一个新的框架，通过量化药物诱导和疾病相关途径扰动（激活/抑制）之间的功能拮抗，系统地识别癌症候选药物。通过整合药物诱导的基因表达、疾病相关的基因表达和通路信息，PathPertDrug评估了通路水平的功能逆转，从而能够精确预测药物与疾病的关联。结果：我们的方法在泛癌症基准中表现出卓越的预测准确性和稳健性。与现有方法相比，该方法获得了更高的中位AUROC (0.62 vs. 0.42-0.53)，并且AUPR（3- 23%）得到了显著改善。一致的AUPR增强，特别是在阶级不平衡的情况下，强调了我们的模型在可靠地优先考虑真正关联方面的稳健性。通过比较毒理学数据库的验证，PathPertDrug重新发现了83%的文献支持的癌症药物（例如，用于结直肠癌的氟维司汀（fulvestrant）），并预测了新的候选药物（例如，利法布汀-肺癌）。结论：这种以途径为中心的方法将机制见解与转化应用联系起来，为精确的肿瘤药物发现提供了范式转变。

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引用次数: 0

GICAF-Net: A cross-attentional graph-image fusion network for hyperspectral pathological diagnosis of FNH and HCC GICAF-Net：用于FNH和HCC高光谱病理诊断的交叉注意图形图像融合网络。

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-11-19 DOI: 10.1016/j.cmpb.2025.109171

Yunze Li , Haiyan Chen , Baoxian Gong , Jiankang Han , Jun Cheng , Shuai Gao , Wei Li

Background and objective

Accurate intraoperative differentiation between focal nodular hyperplasia (FNH) and hepatocellular carcinoma (HCC) remains a major clinical challenge, especially in atypical cases where conventional imaging and histopathology are constrained by turnaround time, cost, or spectral resolution. This study aims to develop a novel deep learning framework to improve the precision and efficiency of hyperspectral pathological diagnosis for liver tumors.

Methods

We propose GICAF-Net, a Graph–Image Cross-Attentional Fusion Network, designed to leverage hyperspectral imaging (HSI) for capturing fine-grained spatial–spectral information. The network employs a dual-branch architecture: (1) a residual convolutional branch for extracting pseudo-color image features, and (2) a residual graph convolutional branch for modeling topological spatial–spectral features. A Topology-Aware Cross-Attention Fusion (TACA) module enables bidirectional information exchange between the two modalities, while a multi-constraint fusion loss—combining cross-entropy, prediction confidence, and cross-modal attention consistency—enhances classification stability. A balanced hyperspectral liver tumor dataset consisting of 60 HCC and 60 FNH cases was constructed and evaluated using ten-fold cross-validation.

Results

GICAF-Net achieved an AUC of 0.9571 ± 0.0068, accuracy of 88.34 % ± 1.10 %, and F1-score of 88.32 % ± 1.11 %, outperforming state-of-the-art baseline models. Ablation experiments further validated the contributions of both the TACA module and the multi-constraint loss function in enhancing cross-modal fusion and improving classification performance.

Conclusion

The integration of graph-based spectral–structural modeling with deep visual features through cross-attention provides a powerful approach for hyperspectral pathological diagnosis. The proposed GICAF-Net demonstrates strong potential for rapid, accurate, and minimally invasive intraoperative differentiation of FNH and HCC, offering valuable clinical support in liver tumor surgery.

背景和目的：术中准确区分局灶性结节增生（FNH）和肝细胞癌（HCC）仍然是一个主要的临床挑战，特别是在常规影像学和组织病理学受周转时间、成本或光谱分辨率限制的非典型病例中。本研究旨在开发一种新的深度学习框架，以提高肝脏肿瘤高光谱病理诊断的准确性和效率。方法：我们提出了图形图像交叉注意融合网络（GICAF-Net），旨在利用高光谱成像（HSI）捕获细粒度的空间光谱信息。该网络采用双分支架构：(1)残差卷积分支用于提取伪彩色图像特征，(2)残差图卷积分支用于建模拓扑空间光谱特征。拓扑感知交叉注意融合（TACA）模块实现了两种模式之间的双向信息交换，而多约束融合损失（结合交叉熵、预测置信度和交叉模态注意一致性）增强了分类稳定性。构建了一个平衡的高光谱肝脏肿瘤数据集，包括60例HCC和60例FNH病例，并使用十倍交叉验证进行评估。结果：GICAF-Net的AUC为0.9571±0.0068，准确率为88.34%±1.10%，f1评分为88.32%±1.11%，优于目前最先进的基线模型。烧蚀实验进一步验证了TACA模块和多约束损失函数在增强跨模态融合和提高分类性能方面的贡献。结论：通过交叉注意将基于图的光谱结构建模与深度视觉特征相结合，为高光谱病理诊断提供了一种强有力的方法。GICAF-Net在快速、准确、微创的术中分化FNH和HCC方面显示出强大的潜力，为肝肿瘤手术提供了宝贵的临床支持。

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引用次数: 0

CT-free attenuation correction of 13N-ammonia cardiac PET images using conditional denoising diffusion implicit model with logarithmic linear normalization 采用对数线性归一化条件去噪扩散隐式模型对13n -氨心脏PET图像进行无ct衰减校正。

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-12-01 DOI: 10.1016/j.cmpb.2025.109188

Hao Sun , Xiaotong Hong , Yijun Lu , Fanghu Wang , Amirhossein Sanaat , Weiping Xu , Shuxia Wang , Habib Zaidi , Lijun Lu

Background and objective

CT-based attenuation correction (CT-AC) is commonly used in cardiac PET but introduces additional radiation exposure, which is particularly problematic for pediatrics or repeat scans. This study aims to generate attenuation-corrected cardiac PET images from non-attenuation-corrected (NAC) PET input without CT using a conditional denoising diffusion implicit model (cDDIM).

Methods

The patient cohort included 60 rest-only and 14 rest-stress scans from Center 1, and 30 rest-stress scans from Center 2. All subjects underwent ¹³N-ammonia cardiac PET/CT scans, generating paired 3D NAC and CT-AC PET images. The middle slice and two adjacent axial slices were used as inputs to mitigate axial artifacts, forming a 2.5D cDDIM framework. Two versions were proposed: one with data logarithmic linear normalization (cDDIM_LLN) and another with linear normalization (cDDIM_LN). 2.5D and 3D generative adversarial network (GAN)-based AC methods were implemented for comparison. Model performances were evaluated using normalized mean square error (NMSE) and segment-wise absolute percent error (APE).

Results

Visual analysis indicated that cDDIM_LLN and cDDIM_LN outperformed GAN-based AC methods for both rest and stress cardiac PET images. cDDIM_LLN achieved significantly lower NMSE than cDDIM_LN in Center 1 (1.87 ± 1.25 % vs. 2.77 ± 2.37 %, p < 0.001) and Center 2 (4.63 ± 3.71 % vs. 5.67 ± 7.88 %, p < 0.001). cDDIM_LLN also showed smaller APE than other methods for both centers (Center 1: 5.78 ± 1.43 %, p < 0.001; Center 2: 9.10 ± 5.59 %, p < 0.001). cDDIM_LN demonstrated lower APE than GAN-based AC methods across both centers.

Conclusions

cDDIM-based AC methods synthesized tracer distributions highly similar to clinical CT-AC. Among the evaluated methods, cDDIM_LLN demonstrated the best performance. Overall, cDDIM-based AC outperformed traditional GAN-based methods.

背景和目的：基于ct的衰减校正（CT-AC）通常用于心脏PET，但会引入额外的辐射暴露，这对儿科或重复扫描尤其有问题。本研究旨在使用条件去噪扩散隐式模型（cDDIM）从无CT的非衰减校正（NAC） PET输入生成衰减校正的心脏PET图像。方法：患者队列包括来自中心1的60例静息扫描和14例静息应激扫描，以及来自中心2的30例静息应激扫描。所有受试者进行13n -氨氮心脏PET/CT扫描，生成配对的3D NAC和CT- ac PET图像。中间切片和两个相邻的轴向切片作为输入，以减轻轴向伪影，形成2.5D cDDIM框架。提出了数据对数线性归一化（cDDIM_LLN）和线性归一化（cDDIM_LN）两个版本。采用基于2.5D和3D生成对抗网络（GAN）的AC方法进行比较。使用归一化均方误差（NMSE）和分段绝对百分比误差（APE）评估模型性能。结果：视觉分析表明cDDIM_LLN和cDDIM_LN在休息和应激心脏PET图像上优于基于gan的AC方法。cDDIM_LLN在中心1（1.87±1.25%比2.77±2.37%,p < 0.001）和中心2（4.63±3.71%比5.67±7.88%,p < 0.001）的NMSE显著低于cDDIM_LN。cDDIM_LLN在两个中心的APE均小于其他方法（中心1:5.78±1.43%,p < 0.001；中心2:9.10±5.59%,p < 0.001）。cDDIM_LN在两个中心的APE均低于基于gan的AC方法。结论：基于cddim的AC方法合成的示踪剂分布与临床CT-AC高度相似。在评价的方法中，cDDIM_LLN的性能最好。总体而言，基于cddim的AC优于传统的基于gan的AC。

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引用次数: 0

A SAS macro for multilevel Cosinor analysis 用于多级余弦分析的SAS宏

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-11-14 DOI: 10.1016/j.cmpb.2025.109167

Margaret M. Doyle , Terrence E. Murphy , Brienne Miner , Melissa P. Knauert

Background and objective

Cosinor analysis allows for the fitting of a cosine curve to describe cyclical variation in periodic data. The analysis provides an intuitive set of estimates that includes the MESOR (Midline Estimating Statistic of Rhythm), i.e., the mid-point of the fitted outcome, the amplitude, i.e., one-half the distance between the MESOR and the peak for normally distributed outcomes, and the acrophase, i.e. the time at which the outcome reaches its peak. Traditionally, most published cosinor analyses were generated though a two-stage approach in which a curve was fit to each individual’s data and differences in the estimated cosinor parameters were compared in downstream analyses. More recently multilevel cosinor modeling software has been developed which allows for the simultaneous modeling of data from multiple individuals. In addition to simplifying the model building process, the advantage of multilevel vs. two-stage cosinor analysis includes the option to fit more complex models and, likely, an improvement in fit for each individual’s data. However, to our knowledge, there are no SAS procedures or macros that assist users with this analytical approach.

Methods

In this paper we introduce multilevel cosinor models and SAS macros we have developed to perform these analyses. In addition, we compare model fit between the multilevel and two-stage methods.

Results

The SAS macros presented in this paper allow users to select the best random variable specification for the unconditional cosinor model and add a dichotomous grouping variable to detect differences in parameters across groups. At each step of model building, parameter estimates, measures of model fit and graphical output help the user understand the model derived and its appropriateness for their data. Results of cross-validation analyses are presented that illustrate the superior fit of the multilevel over the single-level approach for the dataset utilized in the examples.

Conclusions

Multilevel cosinor analysis extends the single subject cosinor model by allowing for more convenient model selection and may provide a better fit for each individual’s data. We are hopeful that this manuscript will introduce more researchers to this analytical technique and allow them to apply it in their own research.

背景和目的余弦分析允许拟合余弦曲线来描述周期性数据的周期性变化。该分析提供了一组直观的估计，其中包括MESOR（节奏中线估计统计量），即拟合结果的中点，幅度，即正态分布结果的MESOR与峰值之间距离的一半，以及顶相，即结果达到峰值的时间。传统上，大多数已发表的余弦分析是通过两阶段方法生成的，其中曲线拟合每个个体的数据，并在下游分析中比较估计的余弦参数的差异。最近已经开发了多级余弦建模软件，它允许同时对来自多个个体的数据进行建模。除了简化模型构建过程之外，多层余弦分析与两阶段余弦分析的优势还包括适合更复杂模型的选项，并且可能改善每个个体数据的拟合。然而，据我们所知，没有SAS过程或宏可以帮助用户使用这种分析方法。方法在本文中，我们介绍了我们开发的用于这些分析的多层余弦模型和SAS宏。此外，我们比较了多级方法和两阶段方法的模型拟合。结果本文提出的SAS宏允许用户为无条件余弦模型选择最佳随机变量规格，并添加一个二分类分组变量来检测组间参数的差异。在模型构建的每个步骤中，参数估计、模型拟合度量和图形输出帮助用户理解所导出的模型及其对其数据的适当性。交叉验证分析的结果表明，对于示例中使用的数据集，多级方法优于单级方法的拟合。结论多层余弦分析扩展了单主体余弦模型，使模型选择更加方便，可以更好地拟合每个个体的数据。我们希望这篇文章能让更多的研究人员了解这种分析技术，并将其应用到自己的研究中。

{"title":"A SAS macro for multilevel Cosinor analysis","authors":"Margaret M. Doyle , Terrence E. Murphy , Brienne Miner , Melissa P. Knauert","doi":"10.1016/j.cmpb.2025.109167","DOIUrl":"10.1016/j.cmpb.2025.109167","url":null,"abstract":"<div><h3>Background and objective</h3><div>Cosinor analysis allows for the fitting of a cosine curve to describe cyclical variation in periodic data. The analysis provides an intuitive set of estimates that includes the MESOR (Midline Estimating Statistic of Rhythm), i.e., the mid-point of the fitted outcome, the amplitude, i.e., one-half the distance between the MESOR and the peak for normally distributed outcomes, and the acrophase, i.e. the time at which the outcome reaches its peak. Traditionally, most published cosinor analyses were generated though a two-stage approach in which a curve was fit to each individual’s data and differences in the estimated cosinor parameters were compared in downstream analyses. More recently multilevel cosinor modeling software has been developed which allows for the simultaneous modeling of data from multiple individuals. In addition to simplifying the model building process, the advantage of multilevel vs. two-stage cosinor analysis includes the option to fit more complex models and, likely, an improvement in fit for each individual’s data. However, to our knowledge, there are no SAS procedures or macros that assist users with this analytical approach.</div></div><div><h3>Methods</h3><div>In this paper we introduce multilevel cosinor models and SAS macros we have developed to perform these analyses. In addition, we compare model fit between the multilevel and two-stage methods.</div></div><div><h3>Results</h3><div>The SAS macros presented in this paper allow users to select the best random variable specification for the unconditional cosinor model and add a dichotomous grouping variable to detect differences in parameters across groups. At each step of model building, parameter estimates, measures of model fit and graphical output help the user understand the model derived and its appropriateness for their data. Results of cross-validation analyses are presented that illustrate the superior fit of the multilevel over the single-level approach for the dataset utilized in the examples.</div></div><div><h3>Conclusions</h3><div>Multilevel cosinor analysis extends the single subject cosinor model by allowing for more convenient model selection and may provide a better fit for each individual’s data. We are hopeful that this manuscript will introduce more researchers to this analytical technique and allow them to apply it in their own research.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109167"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615377","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}

引用次数: 0

Triggers and maintenance of idiopathic atrial fibrillation: A multiscale computational simulation study 特发性心房颤动的触发和维持：一项多尺度计算模拟研究

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-11-19 DOI: 10.1016/j.cmpb.2025.109173

Lian Xin , Li Haiying , Chen Yanhong , Yu Shiqi , He Linsheng , Wu Jian

Background and Objective

Idiopathic atrial fibrillation (IAF) is linked to electrical remodeling, yet prospective clinical cohort data isolating the individual contributions of candidate risk factors remain scarce. To address the gap, we used in-silico simulations to delineate the mechanisms and interactions underlying IAF.

Methods

We built a multi-scale atrial computational model on a 3D atrial anatomy integrating ion-channel kinetics, cellular electrophysiology, and tissue-level propagation. Atrial tissue conductivity (ATC), atrial effective refractory period (AERP), and sinus cycle length (SCL) were systematically varied at 6, 4, and 4 levels, respectively, within physiologic ranges. We recorded parameter sets that precipitated IAF and quantified dynamics by complexity, stability, and spatial disorder.

Results

In our simulations, ATC was represented by the diffusion coefficient D_scalar, and the action potential duration at −60 mV (APD-60mV) served as a surrogate for AERP. At SCL = 1000 ms, IAF initiated only when APD-60mV = 94 ms and D_scalar ≤ 0.0002 mm²/ms. With SCL shortening, the APD-60mV threshold for initiation decreased. SCL modulated susceptibility but was not sufficient alone. If either the AERP or ATC criterion was unmet, IAF did not initiate at any SCL. After initiation, both slow conduction and high frequency pacing increased arrhythmic complexity (spiral-wave count +63% and +129%); high frequency pacing enhanced stability (spiral-wave lifetimes up to +48%), whereas slow conduction worsened spatial disorder (organization index −27%). Spiral waves preferentially clustered along the interatrial septum.

Conclusions

IAF initiation requires both shortened AERP and reduced ATC, while maintenance is promoted by high-frequency pacing and slowed conduction. The interatrial septum emerges as a leading non–pulmonary-vein source. These findings provide mechanistic insight into IAF initiation and persistence and may inform early prevention.

背景与目的特发性心房颤动（IAF）与电重构有关，但分离候选危险因素个体贡献的前瞻性临床队列数据仍然很少。为了解决这一差距，我们使用了计算机模拟来描述IAF的机制和相互作用。方法建立三维心房解剖的多尺度心房计算模型，结合离子通道动力学、细胞电生理和组织水平的传播。心房组织电导率（ATC）、心房有效不应期（AERP）和窦循环长度（SCL）在生理范围内分别系统变化为6、4和4个水平。我们记录了沉淀IAF的参数集，并通过复杂性、稳定性和空间无序性量化了动态。结果在我们的模拟中，ATC用扩散系数d标量表示，动作电位持续时间在−60 mV （APD-60mV）时代表AERP。在SCL = 1000 ms时，只有当APD-60mV = 94 ms且Dscalar≤0.0002 mm2/ms时才会启动IAF。随着SCL的缩短，APD-60mV起始阈值降低。SCL可调节敏感性，但单独作用是不够的。如果AERP或ATC标准未满足，则IAF不会在任何SCL启动。起搏后，慢传导和高频起搏均增加心律失常复杂性（螺旋波计数分别为+63%和+129%）；高频起搏增强了稳定性（螺旋波寿命可达+48%），而慢传导恶化了空间紊乱（组织指数- 27%）。螺旋波优先沿心房间隔聚集。结论siaf的起始需要缩短AERP和降低ATC，而维持需要高频起搏和减慢传导。房间隔是主要的非肺静脉源。这些发现为IAF的发生和持续提供了机制见解，并可能为早期预防提供信息。

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引用次数: 0

Integration of quantum artificial intelligence in disease diagnosis: A review of methods and applications 量子人工智能在疾病诊断中的集成：方法与应用综述

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2026-02-01 Epub Date: 2025-11-19 DOI: 10.1016/j.cmpb.2025.109175

Shobha Sharma , Lokesh Sharma , Tapan Kumar Gandhi

Background and objective

Accurate disease diagnosis is vital for effective treatment and improved patient outcomes. While artificial intelligence (AI) has advanced medical diagnostics, conventional AI approaches often face limitations in real-time data processing, scalability, and managing high-dimensional biomedical data. Quantum Artificial Intelligence (QAI) integrates quantum computing with AI to address these challenges. This study explores

QAI models in disease diagnosis, highlighting their advantages over classical AI, their applications across diseases, and integration possibilities within diagnostic workflows.

Methods

A structured literature review was conducted using Scopus, PubMed, IEEE Xplore, and Google Scholar databases. A total of 37 peer-reviewed articles were selected based on relevance, methodological quality, and focus on QAI applications in diagnostics. The review analyzed key quantum machine learning (QML) models, including hybrid and quantum inspired techniques.

Results

The findings indicate that QAI demonstrates promising applications in diagnosing cancer, neurodegenerative disorders, cardiovascular diseases, COVID-19, and other conditions. Quantum algorithms enable faster and more accurate pattern recognition in complex medical datasets. Additionally, QAI can be integrated into various stages of the diagnostic pipeline, from feature engineering to optimization to provide clinical decision support. However, technical challenges such as quantum noise, hardware instability, and limited algorithm maturity were frequently noted.

Conclusions

QAI has the potential to revolutionize disease diagnosis by overcoming many limitations of classical AI systems. While significant progress has been made, real-world clinical integration requires further advancements in algorithm development and hardware scalability. Future research should focus on closing the gap between theoretical models and clinical implementation to fully realize the benefits of QAI in healthcare.

背景与目的准确的疾病诊断对于有效治疗和改善患者预后至关重要。虽然人工智能（AI）具有先进的医疗诊断技术，但传统的人工智能方法在实时数据处理、可扩展性和高维生物医学数据管理方面往往面临限制。量子人工智能（QAI）将量子计算与人工智能相结合，以应对这些挑战。本研究探讨了qai模型在疾病诊断中的应用，强调了它们相对于经典AI的优势，它们在疾病中的应用，以及在诊断工作流程中的集成可能性。方法采用Scopus、PubMed、IEEE Xplore和谷歌Scholar数据库进行结构化文献综述。根据相关性、方法学质量和对QAI在诊断中的应用的关注，共选择了37篇同行评议的文章。该综述分析了关键的量子机器学习（QML）模型，包括混合和量子启发技术。结果QAI在诊断癌症、神经退行性疾病、心血管疾病、COVID-19等疾病方面具有广阔的应用前景。量子算法能够在复杂的医疗数据集中实现更快、更准确的模式识别。此外，QAI可以集成到诊断流程的各个阶段，从特征工程到优化，以提供临床决策支持。然而，量子噪声、硬件不稳定性和有限的算法成熟度等技术挑战经常被注意到。结论人工智能克服了传统人工智能系统的诸多局限性，具有革新疾病诊断的潜力。虽然已经取得了重大进展，但现实世界的临床整合需要在算法开发和硬件可扩展性方面取得进一步的进展。未来的研究应着眼于缩小理论模型与临床实施之间的差距，以充分实现质量评价在医疗保健中的益处。

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引用次数: 0