Computer methods and programs in biomedicine最新文献

{"title":"DG-MSGAT: A Biologically-informed Differential Gene Multi-Scale Graph Attention Network for predicting neoadjuvant therapy response in rectal cancer.","authors":"Xu Luo, Pei Shu, Ning Liu, Dong Miao, Xiuding Cai, Yu Yao, Xin Wang","doi":"10.1016/j.cmpb.2025.108974","DOIUrl":"10.1016/j.cmpb.2025.108974","url":null,"abstract":"<p><strong>Background and objective: </strong>Accurate prediction of the efficacy of neoadjuvant therapy - particularly the likelihood of achieving a pathological complete response (pCR) - is critical to improving outcomes in patients with rectal cancer. The anticipation of therapeutic response prior to surgery enables the development of personalized treatment strategies and reduces unnecessary interventions for non-responders. While genetic profiling has been integrated into predictive models to enhance response estimation, many existing approaches overlook gene-gene interactions. Furthermore, they often struggle with the high dimensionality, noise, and sparsity inherent in gene expression data. To address these limitations, we propose a biologically informed model, the Differential Gene Multi-Scale Graph Attention Network (DG-MSGAT). This model integrates differential expression signals with multi-scale gene interaction patterns to improve the accuracy of treatment response prediction.</p><p><strong>Methods: </strong>By integrating gene expression profiles with differential expression signals, we construct a patient-specific gene graph whose edges are defined based on curated protein-protein interaction data. This graph is then processed by DG-MSGAT, a multi-scale graph attention network that utilizes stacked attention layers and residual connections to model hierarchical gene dependencies and preserve feature integrity. The resulting representation is subsequently used to estimate the probability of achieving a pathological complete response.</p><p><strong>Results: </strong>In patients with locally advanced rectal cancer, the DG-MSGAT model substantially outperformed conventional algorithms - including support vector machines, decision trees, and random forests - in predicting neoadjuvant therapy efficacy. Network analysis identified key genes (e.g., TP53, EGFR, CTNNB1) and immune-related pathways that are consistent with clinically established determinants of therapeutic response.</p><p><strong>Conclusion: </strong>The DG-MSGAT model offers a promising advancement in the prediction of neoadjuvant therapy outcomes in rectal cancer. By effectively modeling gene interactions and mitigating the limitations associated with high-dimensional gene expression data, it provides a clinically relevant tool to support personalized treatment decision-making.</p>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"271 ","pages":"108974"},"PeriodicalIF":4.8,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144803827","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":"Prediction of breast cancer HER2 status changes based on ultrasound radiomics attention network.","authors":"Jian Liu, Xinzheng Xue, Yuqi Yan, Qian Song, Yuhu Cheng, Liping Wang, Xuesong Wang, Dong Xu","doi":"10.1016/j.cmpb.2025.108987","DOIUrl":"10.1016/j.cmpb.2025.108987","url":null,"abstract":"<p><strong>Background and objective: </strong>Following Neoadjuvant Chemotherapy (NAC), there exists a probability of changes occurring in the Human Epidermal Growth Factor Receptor 2 (HER2) status. If these changes are not promptly addressed, it could hinder the timely adjustment of treatment plans, thereby affecting the optimal management of breast cancer. Consequently, the accurate prediction of HER2 status changes holds significant clinical value, underscoring the need for a model capable of precisely forecasting these alterations.</p><p><strong>Methods: </strong>In this paper, we elucidate the intricacies surrounding HER2 status changes, and propose a deep learning architecture combined with radiomics techniques, named as Ultrasound Radiomics Attention Network (URAN), to predict HER2 status changes. Firstly, radiomics technology is used to extract ultrasound image features to provide rich and comprehensive medical information. Secondly, HER2 Key Feature Selection (HKFS) network is constructed for retain crucial features relevant to HER2 status change. Thirdly, we design Max and Average Attention and Excitation (MAAE) network to adjust the model's focus on different key features. Finally, a fully connected neural network is utilized to predict HER2 status changes. The code to reproduce our experiments can be found at https://github.com/didadiuouo/URAN.</p><p><strong>Results: </strong>Our research was carried out using genuine ultrasound images sourced from hospitals. On this dataset, URAN outperformed both state-of-the-art and traditional methods in predicting HER2 status changes, achieving an accuracy of 0.8679 and an AUC of 0.8328 (95% CI: 0.77-0.90). Comparative experiments on the public BUS_UCLM dataset further demonstrated URAN's superiority, attaining an accuracy of 0.9283 and an AUC of 0.9161 (95% CI: 0.91-0.92). Additionally, we undertook rigorously crafted ablation studies, which validated the logicality and effectiveness of the radiomics techniques, as well as the HKFS and MAAE modules integrated within the URAN model. The results pertaining to specific HER2 statuses indicate that URAN exhibits superior accuracy in predicting changes in HER2 status characterized by low expression and IHC scores of 2+ or below. Furthermore, we examined the radiomics attributes of ultrasound images and discovered that various wavelet transform features significantly impacted the changes in HER2 status.</p><p><strong>Conclusions: </strong>We have developed a URAN method for predicting HER2 status changes that combines radiomics techniques and deep learning. URAN model have better predictive performance compared to other competing algorithms, and can mine key radiomics features related to HER2 status changes.</p>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"271 ","pages":"108987"},"PeriodicalIF":4.8,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144803828","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":"GAT-Enhanced TabNet model with heterogeneous tabular and dependency graph information feature fusion for multi-disease coexistence risk prediction.","authors":"Chengjie Li, Yanglin Wang, Mingxiu Li, Yi Zheng, Yijie Luo, Wen Zhong","doi":"10.1016/j.cmpb.2025.109080","DOIUrl":"https://doi.org/10.1016/j.cmpb.2025.109080","url":null,"abstract":"<p><strong>Background and objective: </strong>Modeling structured medical tabular data presents significant challenges due to complex sample dependencies and non-linear feature interactions. Existing methods, which primarily focus on single-disease prediction, often exhibit limited capability in forecasting critical progression in patients with multimorbidity. To address this, we propose GATET, a novel architecture that integrates graph neural networks, deep tabular learning, and population subgraph partitioning to improve predictive accuracy for multimorbid patients.</p><p><strong>Methods: </strong>GATET comprises three core modules: (1) Dependency Feature Extraction (DFE), which generates trainable adjacency matrices guided by medical prior knowledge; (2) Attentive Aggregation for Constructing Graphs (CGsA), which employs dual-channel graph attention networks to capture intricate relationships within the population graph, and (3) Feature Weighting based on TabNet (FWT), which preserves TabNet's interpretability while removing its global modeling mechanism to eliminate redundant computations. The implementation is publicly available at https://www.researchgate.net/profile/Chengjie-Li-7.</p><p><strong>Results: </strong>Extensive repeated experiments with statistical hypothesis testing, performed on clinical data from a tertiary hospital in Southwest China, demonstrate that GATET improves prediction accuracy by approximately 10% over baseline models and achieves superior performance across additional metrics. Domain adaptation experiments on multiple datasets confirm its effectiveness for other disease prediction tasks. Supplementary analyses, including parameter sensitivity studies and graph-aggregated feature selection, empirically validate the importance of age-based stratification in multimorbid populations.</p><p><strong>Conclusions: </strong>Comprehensive comparative evaluations highlight GATET's strong potential for predicting critical disease progression in multimorbid patients. This work presents an effective strategy for integrating prior medical knowledge into graph-based frameworks, advancing predictive analytics for structured tabular data and delivering tangible improvements for complex clinical prediction.</p>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"273 ","pages":"109080"},"PeriodicalIF":4.8,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145343954","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":"Corrigendum to “Segmentation of skin layers on HFUS images using the attention mechanism”, [Computer Methods and Programs in Biomedicine, 263 (2025) 108668]","authors":"Anna Slian ,&nbsp;Katarzyna Korecka ,&nbsp;Adriana Polańska ,&nbsp;Joanna Czajkowska","doi":"10.1016/j.cmpb.2025.109115","DOIUrl":"10.1016/j.cmpb.2025.109115","url":null,"abstract":"","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"273 ","pages":"Article 109115"},"PeriodicalIF":4.8,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318264","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":"Memory-driven modeling of herpes simplex virus type-1 and type-2 dynamics with neural network optimization.","authors":"Zhang Nan, Emmanuel Addai, Ridwan Amure, Rebecca Bottey, Enoch K Larrey, Mercy Ngungu","doi":"10.1016/j.cmpb.2025.109121","DOIUrl":"https://doi.org/10.1016/j.cmpb.2025.109121","url":null,"abstract":"<p><strong>Background and objective: </strong>Herpes Simplex Virus Types 1 and 2 (HSV-1 and HSV-2) are chronic viral infections with widespread prevalence and lasting health effects, including neurological and oncological complications. Traditional epidemiological models often fail to capture memory-dependent dynamics inherent in such infections. This study develops a novel modeling framework that incorporates memory effects to better understand HSV dynamics and evaluate intervention strategies.</p><p><strong>Methods: </strong>We constructed a fractional-order compartmental model using the Caputo derivative to describe HSV-1 and HSV-2 transmission. The population is divided into susceptible (with and without health education), infected (type-1 and type-2), and recovered groups. We examined the model's qualitative properties, including existence, uniqueness, and stability. The basic reproduction number was derived, and sensitivity analysis was performed using the Latin Hypercube Sampling-Partial Rank Correlation Coefficient method. Numerical simulations were conducted via the Adams-Bashforth-Moulton predictor-corrector scheme. Additionally, a deep neural network was implemented to approximate the model's behavior.</p><p><strong>Results: </strong>Our findings show that fractional-order dynamics substantially influence infection persistence, with lower fractional orders prolonging infectious periods. Sensitivity analysis identified transmission rates and public awareness as the most impactful parameters. Recovery rates were negatively correlated with the basic reproduction number. Simulations demonstrated that increased awareness reduces infection levels. The neural network achieved high predictive accuracy (R≈1) across all compartments, effectively modeling both peak and recovery phases.</p><p><strong>Conclusions: </strong>The incorporation of fractional derivatives improves model realism by capturing memory effects critical to HSV progression. Combining this with deep learning enables accurate simulation and real-time assessment of interventions. This integrated framework is a powerful tool for public health planning, particularly in optimizing awareness and treatment strategies for HSV-1 and HSV-2 control.</p>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"273 ","pages":"109121"},"PeriodicalIF":4.8,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145344022","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":"Self-supervised learning and hybrid deep models for predicting the progression of Fuchs’ endothelial corneal dystrophy after cataract surgery","authors":"Paola León-Tarife ,&nbsp;Francisco Arnalich-Montiel ,&nbsp;David Mingo-Botín ,&nbsp;Fernando Díaz de María ,&nbsp;Martha Stokking ,&nbsp;Nerea Saenz-Madrazo ,&nbsp;Jaime Etxebarria-Ecenarro ,&nbsp;Pedro Arriola-Villalobos ,&nbsp;Ana Boto de los Bueis ,&nbsp;Iván González-Díaz","doi":"10.1016/j.cmpb.2025.109100","DOIUrl":"10.1016/j.cmpb.2025.109100","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Fuchs’ endothelial corneal dystrophy (FECD) increases the risk of corneal decompensation after cataract surgery, often leading to endothelial keratoplasty (EK). Reliable prediction of EK is essential for surgical planning, yet traditional corneal biomarkers have limited prognostic value.</div></div><div><h3>Methods:</h3><div>We present a novel deep learning framework based on Scheimpflug tomographic imaging to improve FECD prognosis. The approach integrates three components to address data limitations: (1) clinical domain knowledge, (2) ensemble learning, and (3) self-supervised learning (SSL). A hybrid convolutional neural network (CNN) is proposed, combining a RANSAC-based algorithm for estimating best-fit sphere (BFS) elevation maps with a dual-branch design that incorporates Polar Pooling to mimic clinical reasoning. Robustness is enhanced through bootstrap aggregation, where multiple models are trained on different data subsets and their predictions averaged. To further reduce reliance on annotated data, we introduce a self-supervised contrastive pretraining task. This task distinguishes images from the same eye across time, using a weighted contrastive loss that emphasizes corneal irregularities.</div></div><div><h3>Results:</h3><div>On a multi-center dataset, the framework achieved an AUC of 0.94, outperforming biomarker-based models and demonstrating strong generalizability across hospitals.</div></div><div><h3>Conclusions:</h3><div>The proposed methodology provides a robust and interpretable decision-support tool for FECD management and cataract surgery planning.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"273 ","pages":"Article 109100"},"PeriodicalIF":4.8,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312580","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":"Molecular dynamics deconstruction of hyaluronic acid shear thinning: hydrogen bond network dynamics driving rheological response","authors":"Youpeng Li ,&nbsp;Bolin Tian ,&nbsp;Kexuan Li ,&nbsp;Peibiao Zhao ,&nbsp;Zhaolin Zuo ,&nbsp;Bin Cui ,&nbsp;Juntong Li ,&nbsp;Zhenyan Xia","doi":"10.1016/j.cmpb.2025.109120","DOIUrl":"10.1016/j.cmpb.2025.109120","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Hyaluronic acid (HA) is a typical non-Newtonian fluid possessing unique shear-thinning properties, which make it highly valuable in biomedical fields such as tissue engineering and drug delivery. However, existing research has primarily focused on characterizing macroscopic rheological behavior, with limited systematic analysis of the dynamic response mechanisms of microscopic hydrogen bond networks during shear deformation. To better explain the shear thinning properties of hyaluronic acid solutions at the microscopic level, this study employed molecular dynamics simulation methods and abstracted the rotational shear from macro-scale rheological experiments into linear shear.</div></div><div><h3>Methods</h3><div>A system composed of hyaluronic acid molecules and water molecules was used to simulate the conformational evolution of hyaluronic acid solutions under different shear rates. This study analyzed the number of hydrogen bonds formed between hyaluronic acid molecules and water molecules, hydrogen bond lengths, as well as hydrogen bond angles. Additionally, these findings were combined with macroscopic rheological experiments.</div></div><div><h3>Results</h3><div>The number of hydrogen bonds formed between hyaluronic acid molecules and water molecules decreased under shear stress. Furthermore, the hydrogen bond lengths increased, and the hydrogen bond angles decreased, resulting in an overall weakening of hydrogen bond strength.</div></div><div><h3>Conclusions</h3><div>The loss of hydrogen bond strength directly reduces fluid resistance and enhances solution flowability, which is consistent with the shear thinning characteristics observed in macroscopic rheological experiments.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"273 ","pages":"Article 109120"},"PeriodicalIF":4.8,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145328391","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":"A generative adversarial optimization strategy for predicting counterfactual trajectories of grey matter atrophy","authors":"Berardino Barile ,&nbsp;Enyi Chen ,&nbsp;Thomas Grenier ,&nbsp;Dominique Sappey-Marinier","doi":"10.1016/j.cmpb.2025.109095","DOIUrl":"10.1016/j.cmpb.2025.109095","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Counterfactual explanations offer valuable insights into the behavior of machine learning models by describing hypothetical scenarios that would lead to different outcomes. In the biomedical domain, such as neuroimaging for Multiple Sclerosis (MS), counterfactual reasoning has the potential to enhance understanding of disease mechanisms and treatment effects. However, generating anatomically plausible counterfactuals that generalize well to unseen data remains a major challenge.</div></div><div><h3>Methods:</h3><div>We propose an optimization-based adversarial framework for generating realistic counterfactual trajectories of cortical grey matter (GM) thickness in MS patients. The method uses the gradients of a pre-trained MS classifier to guide the generation process towards a desired disease state while enforcing anatomical constraints and disentangling disease-relevant signals from confounding factors such as age and sex.</div></div><div><h3>Results:</h3><div>Our approach successfully produces plausible counterfactual GM thickness maps that reflect known anatomical patterns of MS progression. The generated trajectories maintain consistency with biological structure and improve interpretability of model decisions. On held-out test data, our method achieves a classification AUC of 0.893 and demonstrates strong confounder preservation, with a Mean Absolute Deviation Error (MADE) of 8.72 years for age and 0.14 for sex, and a cosine distance of 0.11 when comparing original and counterfactual instances. The ability to alter the predicted disease state while preserving the confounding variables highlights the strong disentanglement capability of our model. These results confirm the method’s effectiveness in generating realistic and anatomically coherent counterfactuals, outperforming state-of-the-art baselines across multiple metrics.</div></div><div><h3>Conclusions:</h3><div>This study introduces a novel counterfactual generation method that provides interpretable, anatomically grounded explanations of MS progression. The framework serves as a powerful tool for hypothesis generation and model validation in biomedical imaging studies, particularly where understanding disease mechanisms is crucial.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"273 ","pages":"Article 109095"},"PeriodicalIF":4.8,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312504","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":"Exploring design strategies for patient-specific bone scaffolds to create a uniform mechanical environment in trabecular bone","authors":"Andrea Fresquet-Monter,&nbsp;Ricardo Belda,&nbsp;Ana Vercher-Martínez,&nbsp;Raquel Megías,&nbsp;Eugenio Giner","doi":"10.1016/j.cmpb.2025.109116","DOIUrl":"10.1016/j.cmpb.2025.109116","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Large bone defects cannot be repaired by the inherent regeneration mechanisms of bone due to their size, which makes medical intervention essential. Current therapeutic treatments have their limitations, which has led to the study and development of bone scaffolds that maintain structural integrity during bone healing. Novel designs are required to create a mechanical environment that promote osseointegration. In this work, we aim to analyse the effect of patient-specific designs on the creation of a uniform mechanical environment in bone-scaffold constructs.</div></div><div><h3>Methods:</h3><div>Novel patient-specific Triply Periodic Minimal Surface (TPMS) structures were designed according to the characterisation of the microstructure of healthy and osteoporotic human cancellous bone to mimic morphometry. In addition, the assessment of the TPMS representative volume element size was also considered for scaffold design. The interaction between bone and scaffold was analysed through finite element model simulation. In those bone-scaffold assemblies we evaluated three different design strategies: (1) matching bone microstructure; (2) similar apparent compression elastic modulus; and (3) mimicking both the morphometry and the apparent modulus of trabecular bone.</div></div><div><h3>Results:</h3><div>The stress distribution in patient-specific TPMS scaffolds is 83.86 % similar to that of the targeted bone, significantly outperforming the 54.41 % similarity of non-patient-specific solutions.</div></div><div><h3>Conclusions:</h3><div>The design of novel patient-specific scaffolds based on a microstructure similar to cancellous bone allows a uniform stress distribution. Hence, matching both the bone morphometry and apparent elastic modulus is a key issue to reducing stress shielding phenomena and inducing osseointegration.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"273 ","pages":"Article 109116"},"PeriodicalIF":4.8,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312568","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":"A novel simplified structure model for accurate and flexible simulation of radiation-induced DNA damage","authors":"Ling Hua ,&nbsp;Xianghui Kong ,&nbsp;Tian Li ,&nbsp;Youfang Lai ,&nbsp;Yibao Zhang","doi":"10.1016/j.cmpb.2025.109117","DOIUrl":"10.1016/j.cmpb.2025.109117","url":null,"abstract":"<div><h3>Purpose:</h3><div>This study aims to propose and validate a simplified and flexible DNA model for accurate calculations of double strand break (DSB) yields induced by ionizing radiation.</div></div><div><h3>Methods and Materials:</h3><div>By decoupling the macro- and micro-DNA spatial structures, a novel nuclear DNA model was integrated into the GPU-based gMicroMC code framework. A series of DNA models with varying dihedral angles between nucleotide pairs were uniformly distributed in the cell nucleus. The dihedral angle was optimized based on a cubic polynomial fit to the simulated DSB yield irradiated by Aluminum K-shell X-rays. The optimized model was validated using 4.5 keV monoenergetic electrons and proton beams with energies ranging from 1 MeV to 20 MeV, by comparing the simulated DSB yields with published data derived from experiments or calculations based on other models.</div></div><div><h3>Results:</h3><div>An optimal dihedral angle of 49° was determined from DSB yield induced by Aluminum K-shell X-rays in the V79 cell line. For 4.5 keV electrons, the simulated DSB yield based on the structure-optimized model differed by 1.6% from the experimental data, which was lower than that of other dihedral angles. The DSB yields for protons with initial energies ranging from 1 MeV to 20 MeV were also highly consistent with various calculated or experimental results in the other works.</div></div><div><h3>Conclusions:</h3><div>The proposed DNA model enabled more flexible simulation of radiation-induced DNA damage under various radiation conditions without sacrificing accuracy, potentially applicable to radiobiological research.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"273 ","pages":"Article 109117"},"PeriodicalIF":4.8,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318266","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}