Computer methods and programs in biomedicine最新文献

{"title":"Combined model-driven and dual-cycle interactive strategy few-shot learning scheme for predicting breast cancer molecular subtypes based on DCE-MRI","authors":"Xiaoyan Hong ,&nbsp;Lei Zhang ,&nbsp;Longzhen Ding ,&nbsp;Ye Li ,&nbsp;Lihua Li ,&nbsp;Jikui Liu ,&nbsp;Yan Zhang ,&nbsp;Xiang Pan","doi":"10.1016/j.cmpb.2025.108923","DOIUrl":"10.1016/j.cmpb.2025.108923","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Deep learning has achieved significant success in solving classification problems in the medical field, which has, in turn, facilitated the development of continuous and real-time health monitoring systems. Yet medical images have extremely low data regimes, unlike natural images with a large amount of data. Hence, to classify medical images with a few per-class data samples is a challenging problem. In this paper, we propose a novel few-shot learning scheme, which jointly embeds a model-driven mechanism and dual-cycle interactive strategy, to predict breast cancer molecular subtypes based on a few DCE-MRI samples.</div></div><div><h3>Methods:</h3><div>We present a unique spatio-temporal recurrent network classifier (STRNC) that predicts breast cancer molecular subtypes by learning spatial correlations and temporal dependencies in DCE-MRI. Moreover, the proposed dual-cycle interactive strategy (DCIS) formulates <span><math><mi>N</mi></math></span>-way <span><math><mi>C</mi></math></span>-shot tasks by applying task-specific adaptation to the support set and conducting meta-level evaluation on the query set, thus improving the model’s generalization to unseen tasks.</div></div><div><h3>Results:</h3><div>Extensive experiments on public datasets have demonstrated that our proposed scheme achieves the best results, with a classification accuracy of 99.13 ±0.21 (%) for subtypes, and can accurately differentiate between individual molecular subtypes.</div></div><div><h3>Conclusions:</h3><div>Overall, our proposed method is able to learn spatial correlation and temporal dependence in DCE-MRI and has the potential to guide clinical typing prediction.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108923"},"PeriodicalIF":4.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144569848","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":"Spatial Transcriptomics Unveils Regional Heterogeneity and Subclonal Dynamics in the Lung Adenocarcinoma Microenvironment","authors":"Haoyuan An ,&nbsp;Wei Fang ,&nbsp;Haiyan Chen ,&nbsp;Weiying Huang ,&nbsp;Hongbin Liu ,&nbsp;Zhenlei Zhang ,&nbsp;Hanzhu Zhao ,&nbsp;Yanbing Zhang ,&nbsp;Miaoqing Zhao ,&nbsp;Jianfeng Qiu ,&nbsp;Wei Li","doi":"10.1016/j.cmpb.2025.108929","DOIUrl":"10.1016/j.cmpb.2025.108929","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Lung adenocarcinoma has high incidence and mortality rates. While single-cell transcriptomics reveals tumor cell heterogeneity, it lacks spatial detail, leaving the spatial dynamics and functional heterogeneity largely unexplored. This study aims to utilize spatial transcriptomics to provide a comprehensive cellular landscape of lung adenocarcinoma, addressing the limitations of single-cell analysis.</div></div><div><h3>Methods</h3><div>Utilizing cell type-specific markers and spatial transcriptomics data derived from single-cell transcriptomic analysis, we performed cell deconvolution, assessed tissue preferences, constructed trajectories, and analyzed spot-spot interactions to create a comprehensive cellular landscape of lung adenocarcinoma.</div></div><div><h3>Results</h3><div>We conducted unsupervised dimensionality reduction clustering on 125,203 single cells from single-cell transcriptomics, and used this data to deconvolute the cellular composition of each of the 3990 spots in a separately analyzed spatial transcriptomics sample. By constructing a trajectory from the tumor's periphery to its core, we discovered that pathways related to oxygen level responses were significantly upregulated, while immune response pathways were notably downregulated. Pseudotime analysis identified fibroblast areas closely associated with the tumor, with neighboring tumor areas exhibiting strong epithelial-mesenchymal transition and tumor migration characteristics, defined as the direction of tumor invasion. Further dimensionality reduction clustering within the tumor area differentiated six subgroups, each showing significant spatial and functional heterogeneity. Notably, the Ca_0 subgroup is closely linked to the tumor invasion process, whereas the Ca_1 subgroup is significantly associated with poor prognosis. Interaction analysis suggests that the Ca_1 subgroup may facilitate overall tumor progression by promoting angiogenesis and immune escape, rather than by directly participating in tumor invasion.</div></div><div><h3>Conclusions</h3><div>This study provides an in-depth analysis of the spatial-functional heterogeneity in lung adenocarcinoma and its microenvironment, revealing four functionally heterogeneous groups with significant spatial and functional diversity within the tumor microenvironment. The study also identifies key areas and gene expression changes closely associated with tumor invasion and progression, highlighting critical aspects of tumor dynamics.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108929"},"PeriodicalIF":4.9,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563259","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":"From clinical measurements to articulated foetus: Statistical shape modeling of bone contours using PLSR for enhanced childbirth simulation","authors":"Morgane Devismes-Ferrandini ,&nbsp;Tan-Nhu Nguyen ,&nbsp;Tien-Tuan Dao","doi":"10.1016/j.cmpb.2025.108934","DOIUrl":"10.1016/j.cmpb.2025.108934","url":null,"abstract":"<div><h3>Background and objective</h3><div>The prediction of childbirth-related complications requires realistic patient-specific childbirth simulations with articulated foetal bodies. However, the foetal models used in current simulations are oversimplified and generally based on scaled generic models. This study presents the development and evaluation of a full-body (hands and feet excluded) foetal statistical shape model (SSM) based on CT scans to generate fast and accurate foetal geometries and lower the simulation errors due to linear scaling.</div></div><div><h3>Methods</h3><div>The developed SSM was based on a dataset of 96 subjects under one year of age. All scans were segmented and articulated to put the subjects in a similar posture. Then, the bone contours were extracted using an alpha-shape algorithm to decrease the model’s complexity. Bones were individually fitted to a template mesh to obtain point correspondences, and a PCA was conducted on the concatenated skeletons to capture the morphological variations between subjects. A PLSR was trained to allow skeletal geometry prediction using a set of anthropometrical data and bone measurements.</div></div><div><h3>Results</h3><div>Our shape model predicted the bone geometry with a root mean square error (RMSE) of 2.10 ± 1.42 mm for the head-only model and with an RMSE = 6.69 ± 3.85 mm for the full-body model. For all models, the SSM performed better at predicting the bone geometries than the mean shape obtained by PCA, considered as a baseline.</div></div><div><h3>Conclusions</h3><div>The present study proposed a first complete foetal SSM, allowing a fast and accurate prediction of the bone geometries using a reduced set of easily accessible predictors. This opens new avenues in the realistic childbirth modeling with articulated foetus for predicting physiological delivery and associated complication scenarios.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108934"},"PeriodicalIF":4.9,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557260","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 Joint Multimodal User Authentication-based Privacy Preservation with Disease Prediction Framework in Modern Healthcare System Using Multi-Scale Cross Attention-based ResNet","authors":"C.S. Anita ,&nbsp;Ananthajothi K ,&nbsp;J. Joselin jeya sheela","doi":"10.1016/j.cmpb.2025.108928","DOIUrl":"10.1016/j.cmpb.2025.108928","url":null,"abstract":"<div><h3>Background/Introduction</h3><div>The disease prediction process plays a crucial part in a person’s life “to lead a healthy life.” The sudden spread of the data mining approach has generated the disease forecasting system. Secure transfer of medical data and effective storage is the major difficulty faced by recent healthcare management. Moreover, there is significant attention towards privacy preservation, especially for medical information, which is highly sensitive. For disease prediction, several prevailing privacy preservation approaches have been developed. “Moreover, although the disease prediction system is auspicious, its complexity may limit practical use, including information security and prediction efficiency<strong>.”</strong></div></div><div><h3>Methods</h3><div>Multimodal user authentication is performed by a Multi-scale Cross Attention-based Residual Network (MCARNet) to prevent unauthorized access to the healthcare system. Images and signals are converted into 2D images for performing the encryption using the Optimal Rossler Hyper Chaotic Encryption (ORHCE). The decrypted images are given to the same MCARNet for predicting the disease.</div></div><div><h3>Results</h3><div>The precision of the developed model was enhanced by 7.3% of DNN, 12.3% of RNN, 3.6% of LSTM, and 4.3% of GRU when taking the k fold value as 5.</div></div><div><h3>Conclusion</h3><div>The multimodal user authentication and disease detection using the proposed heuristic-based hybrid deep learning model enhanced its authentication and detection performance.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108928"},"PeriodicalIF":4.9,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587662","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":"Generalized degradation-based adversarial learning for unsupervised super-resolution of endomicroscopy images","authors":"Linghao Meng ,&nbsp;Yangxi Li ,&nbsp;Yuchao Zheng ,&nbsp;Yu Feng ,&nbsp;Fang Chen ,&nbsp;Longfei Ma ,&nbsp;Hongen Liao","doi":"10.1016/j.cmpb.2025.108914","DOIUrl":"10.1016/j.cmpb.2025.108914","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>In recent years, probe-based confocal laser endomicroscopy (pCLE) has become an emerging <em>optical biopsy</em> method for <em>in situ</em> imaging and diagnosis, which aids in the accurate early diagnosis of diseases like inflammation and cancer. However, due to physical constraints induced by the fiber bundle used for signal acquisition, obtaining pCLE images of high resolution is challenging. Consequently, in this study, we aim to improve pCLE image quality through the implementation of advanced post-processing techniques.</div></div><div><h3>Methods:</h3><div>Here we propose an unsupervised single image super-resolution framework, which is free of using high-resolution pCLE images as reference and improves image quality significantly. The framework consists of a degradation module, a style transformation module and a super resolution module. In the degradation module, we propose an innovative distribution assumption module to randomize the fiber optic position distribution, enabling us to simulate the imaging principles of pCLE and create synthetic pCLE images for training.</div></div><div><h3>Results:</h3><div>With the integration of modules, both quantitative and qualitative analyses highlight the remarkable efficiency of our pipeline in super-resolving images compared to state-of-the-art methods. Our framework also demonstrates strong generalization capability, effectively mitigating the impact of pCLE system’s intrinsic characteristics on image super-resolution. This feature is particularly advantageous as it allows the framework to circumvent redundant training when applied to various devices.</div></div><div><h3>Conclusions:</h3><div>With the outstanding super-resolution and generalization capability, our proposed methodology enables clearer observation of image details and more accurate localization of micro structures, which contributes to precise identification of lesion areas and diagnostic accuracy enhancement.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108914"},"PeriodicalIF":4.9,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557262","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":"Pharmacometric and Digital Twin modeling for adaptive scheduling of combination therapy in advanced gastric cancer","authors":"Michela Prunella ,&nbsp;Nicola Altini ,&nbsp;Rosalba D’Alessandro ,&nbsp;Annalisa Schirizzi ,&nbsp;Angela Dalia Ricci ,&nbsp;Claudio Lotesoriere ,&nbsp;Paolo Scarabaggio ,&nbsp;Raffaele Carli ,&nbsp;Mariagrazia Dotoli ,&nbsp;Gianluigi Giannelli ,&nbsp;Vitoantonio Bevilacqua","doi":"10.1016/j.cmpb.2025.108919","DOIUrl":"10.1016/j.cmpb.2025.108919","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Combining targeted therapeutics can significantly help address the dynamic changes in cancer biology abnormalities and thus improve the duration of response and outcome. However, the efficacy of such approaches is highly dependent on the combination, interactions, and timing between the administered drugs. Current clinical trials can test only a low number of schedules with fixed designs. Pharmacometric tools can assist in exploring and selecting the most effective drug dosages and schedules by modeling traits of patients with different clinical and biological characteristics.</div></div><div><h3>Methods:</h3><div>This study proposes a pharmacokinetic–pharmacodynamic model describing the networked system of tumor development and angiogenesis under the control of antiangiogenic and cytotoxic, i.e., Ramucirumab and Paclitaxel second-line combination therapy. A two-step scalable algorithm is proposed to calibrate model parameters and match virtual to real population therapy outcomes, followed by fine-tuning directly on the Progression-free Survival (PFS)-2 Kaplan–Meier curve. Two cohorts of advanced gastric cancer patients were considered: a calibration cohort from South Korea, and an external verification cohort from IRCCS “S. De Bellis”, an Italian research hospital. These real-world patients had heterogeneous clinical starting conditions. We perform prospective evaluations of new combination regimens that adhere to pharmacological constraints that are paramount for clinical translation, in which the administration time of the cytotoxic agent is triggered by the normalization window opening, monitored by a tumor microenvironment digital biomarker.</div></div><div><h3>Results:</h3><div>The calibration procedure led to the discovery of a new mathematical biomarker describing the influence of intrinsic tumor growth and angiogenesis on treatment outcomes. The predictive value was assessed through the log-rank test between two PFS-2 groups, which exhibited different (<span><math><mi>p</mi></math></span>-value <span><math><mrow><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>0001</mn></mrow></math></span>) therapy response trends. Our results showcase a new regimen that, by using 33% less cytotoxic drug, achieves indistinguishable PFS-2. Additionally, we present another regimen that extends PFS-2 from 49.2% to 60.9% after 121 days of therapy (<span><math><mi>p</mi></math></span>-value <span><math><mrow><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>0001</mn></mrow></math></span>), by using the same dosing as the standard protocol.</div></div><div><h3>Conclusions:</h3><div>This study proposes an in-silico quantitative platform for virtual expansion of real-world patient cohorts. Furthermore, the estimation of the efficacy of adaptive dose schedules of a combined therapy can complement and inform clinical trial design.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108919"},"PeriodicalIF":4.9,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549743","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":"HybridDLDR: A hybrid deep learning-based drug resistance prediction system of Glioblastoma (GBM) using molecular descriptors and gene expression data","authors":"Sajid Naveed ,&nbsp;Mujtaba Husnain ,&nbsp;Najah Alsubaie","doi":"10.1016/j.cmpb.2025.108913","DOIUrl":"10.1016/j.cmpb.2025.108913","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Glioblastoma (GBM), a very aggressive type of brain tumor, sometimes creates a chemoresistant state that compromises the effectiveness of chemotherapy and leads to serious clinical complications in treatment. Predicting drug resistance is crucial for the improvement of medication effect during cancer treatment. Assessing drug resistance is difficult due to the pricey chemotherapeutic trails and prolonged laboratory investigations. Deep learning plays a significant role in drug resistance prediction nowadays.</div></div><div><h3>Methods:</h3><div>This paper presents a novel deep learning model that combines Convolutional Neural Networks (CNN), Long Short Term Memory Networks (LSTM), and transformer architectures to predict drug resistance. The proposed application acts as a system that estimate the resistance of drugs based on gene expression details and chemical properties.</div></div><div><h3>Results:</h3><div>As compared with existing model for drug resistance prediction, proposed model achieved lower Mean Squared Error (MSE) of 0.4109 and Mean Absolute Error (MAE) of 0.5040, along with higher <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.9635 and pearson correlation of 0.9999.</div></div><div><h3>Conclusions:</h3><div>This work significantly advances the fields of pharmacogenomics and personalized medicine through an in-depth evaluation that includes complex performance metrics and visualizations.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108913"},"PeriodicalIF":4.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513675","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":"Graph-theoretic characterization of nuclear spatial organization in renal cell carcinoma images","authors":"Rohini Palanisamy ,&nbsp;Shruthi Gokul ,&nbsp;Gokul Manoj ,&nbsp;Abinaya Srinivasan ,&nbsp;Sandhya Sundaram ,&nbsp;Ramakrishnan Swaminathan","doi":"10.1016/j.cmpb.2025.108930","DOIUrl":"10.1016/j.cmpb.2025.108930","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Renal cell carcinoma (RCC) is a highly prevalent and aggressive kidney malignancy that necessitates accurate histopathological evaluation for effective diagnosis and treatment planning. While traditional diagnostic approaches primarily rely on nuclear morphology, emerging computational techniques offer alternative strategies to quantify nuclear spatial organization. This study leverages topological data analysis and graph theory to characterize nuclear aggregation patterns in RCC histopathological images.</div></div><div><h3>Methods</h3><div>Graph-based features, including Betti numbers (<em>β₀</em> and <em>β₁</em>) and clustering coefficients, were extracted to quantify nuclear connectivity and structural organization. Nuclear segmentation was performed across multiple intensity thresholds to assess the impact of threshold variation on feature extraction. The elbow method was used to determine the optimal threshold, balancing connectivity, and structural stability. Statistical significance between tumor and normal tissues was evaluated using the Mann-Whitney U test.</div></div><div><h3>Results</h3><div>Betti numbers (<em>β₀</em> and <em>β₁</em>) and clustering coefficients exhibited distinct trends across different threshold values, effectively differentiating RCC from normal renal tissue. Tumor tissues demonstrated higher <em>β₁</em> and clustering coefficient values, indicating increased nuclear aggregation and irregular connectivity, while normal tissues exhibited higher <em>β₀</em> values, suggesting a more fragmented nuclear distribution. The elbow method identified 100 pixels as the optimal threshold for feature extraction, and statistical analysis confirmed significant differences (<em>p</em> &lt; 0.05) between tumor and normal tissues.</div></div><div><h3>Conclusion</h3><div>The results validate the effectiveness of topological and graph-based descriptors in capturing tumor-associated structural variations. By systematically evaluating intensity thresholds and selecting the optimal one, this study enhances the reliability of nuclear aggregation-based differentiation. The proposed computational framework supports automated RCC diagnosis and improves histopathological assessment, demonstrating the potential of topological data analysis and graph theory in medical imaging.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108930"},"PeriodicalIF":4.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557261","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":"Mimicking cancer therapy in an agent-based model: The case of hepatoblastoma","authors":"Alessandro Ravoni ,&nbsp;Enrico Mastrostefano ,&nbsp;Roland Kappler ,&nbsp;Carolina Armengol ,&nbsp;Filippo Castiglione ,&nbsp;Christine Nardini","doi":"10.1016/j.cmpb.2025.108917","DOIUrl":"10.1016/j.cmpb.2025.108917","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Hepatoblastoma is the most common pediatric liver cancer and represents a serious clinical challenge as no effective therapies have yet been found for advanced states and relapses of the disease.</div></div><div><h3>Methods:</h3><div>In this work, we use a well-established agent-based model of the immune response now equipped with anti-cancer therapy response to study the evolution of the disease and the role of the immune system in its containment.</div></div><div><h3>Results:</h3><div>We simulate the course of hepatoblastoma over three years in a population of virtual patients, successfully mimicking clinical mortality and symptom onset rates, as well as observations on the main tumor transcriptomic subtypes.</div></div><div><h3>Conclusions:</h3><div>The capacity of the introduced framework to reproduce clinical data and the heterogeneity of hepatoblastoma, combined with the possibility of observing the dynamics of cellular entities at the microscopic scale and the key chemical signals involved in disease progression, makes the model a promising resource for future research on in silico trials.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108917"},"PeriodicalIF":4.9,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490874","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":"Predicting the heterogeneous chemo-mechano-biological degeneration of cartilage using 3-D biphasic finite elements.","authors":"Muhammed Masudur Rahman, Paul N Watton, Corey P Neu, David M Pierce","doi":"10.1016/j.cmpb.2025.108902","DOIUrl":"https://doi.org/10.1016/j.cmpb.2025.108902","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background and objective: &lt;/strong&gt;Osteoarthritis (OA), a debilitating joint disease, involves progressive cartilage degeneration and altered biomechanics. We established a novel chemo-mechano-biological (CMB) modeling framework that integrates biphasic mechanics with biochemical and biological processes to predict cartilage degeneration (i.e. loss of masses of constituents presenting as loss of thickness) under pathological conditions. Our framework captures time-dependent remodeling of cartilage constituents in 3-D driven by mechanical loading, biochemical signaling, and cellular metabolism.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We formulated a nonlinear, large-strain biphasic constitutive model coupled with a biochemical model of signaling pathways. Our framework incorporates depth-dependent metabolic activity, explicitly linking availability of oxygen to chondrocyte behavior and extracellular matrix (ECM) remodeling. We included interactions among mechanical stimuli, growth factors, pro-inflammatory cytokines, enzymes (collagenases and aggrecanases), and inhibitors (TIMP). We conducted nonlinear, biphasic finite element (FE) simulations in 3-D, allowing for realistic representations of intra-cartilage heterogeneity. We simulated cyclic, confined compression of full-thickness cartilage, a scenario mimicking conditions in vivo during walking or running.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our simulations spanning 24 months presented realistic patterns of cartilage degeneration including zonal variations in matrix composition and thickness loss. In healthy cartilage, interstitial fluid pressure resisted mechanical loading, maintaining ECM integrity. However, in degenerative overloading conditions, enzymatic activity and altered metabolic functions led to increased porosity, reduced fluid pressure, and heterogeneous degradation of ECM. Incorporating depth-dependent metabolic activity revealed pronounced degeneration in the superficial zone (SZ) and progressively reduced loss toward the deep zone (DZ). This outcome aligns with experimental evidence on progression of OA. Oxygen availability played a critical role, with higher levels exacerbating degradation, consistent with findings linking oxidative stress to cartilage degeneration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Our nonlinear, biphasic FE framework offers a robust tool for investigating mechanisms of cartilage degeneration and OA, and advancing therapeutic strategies. It uniquely integrates biphasic mechanics, signaling pathways, and metabolic activity in 3-D, providing insights into patterns of cartilage degeneration. We previously developed automated and publicly available tools to generate patient-specific knee models from MR Images, altogether enabling personalized diagnostics/prognostics and pre-/post-operative planning. Our CMB framework is also publicly available as a plugin for FEBio at https://github.uconn.edu/imLab/FEVGnR-Plugin, supporting broader research on OA and cartilage biom","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":" ","pages":"108902"},"PeriodicalIF":4.9,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526668","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}