Computer methods and programs in biomedicine最新文献

{"title":"LMTTM-VMI: Linked Memory Token Turing Machine for 3D volumetric medical image classification","authors":"Hongkai Wei ,&nbsp;Yang Yang ,&nbsp;Shijie Sun ,&nbsp;Mingtao Feng ,&nbsp;Rong Wang ,&nbsp;Xianfeng Han","doi":"10.1016/j.cmpb.2025.108640","DOIUrl":"10.1016/j.cmpb.2025.108640","url":null,"abstract":"<div><div>Biomedical imaging is vital for the diagnosis and treatment of various medical conditions, yet the effective integration of deep learning technologies into this field presents challenges. Traditional methods often struggle to efficiently capture the spatial characteristics and intricate structural features of 3D volumetric medical images, limiting memory utilization and model adaptability. To address this, we introduce a Linked Memory Token Turing Machine (LMTTM), which utilizes external linked memory to efficiently process spatial dependencies and structural complexities within 3D volumetric medical images, aiding in accurate diagnoses. LMTTM can efficiently record the features of 3D volumetric medical images in an external linked memory module, enhancing complex image classification through improved feature accumulation and reasoning capabilities. Our experiments on six 3D volumetric medical image datasets from the MedMNIST v2 demonstrate that our proposed LMTTM model achieves average ACC of 82.4%, attaining state-of-the-art (SOTA) performance. Moreover, ablation studies confirmed that the Linked Memory outperforms its predecessor, TTM’s original Memory, by up to 5.7%, highlighting LMTTM’s effectiveness in 3D volumetric medical image classification and its potential to assist healthcare professionals in diagnosis and treatment planning. The code is released at <span><span>https://github.com/hongkai-wei/LMTTM-VMI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"262 ","pages":"Article 108640"},"PeriodicalIF":4.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395211","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":"Methods in dynamic treatment regimens using observational healthcare data: A systematic review","authors":"David Liang ,&nbsp;Animesh Kumar Paul ,&nbsp;Daniala L. Weir ,&nbsp;Vera H.M. Deneer ,&nbsp;Russell Greiner ,&nbsp;Arno Siebes ,&nbsp;Helga Gardarsdottir","doi":"10.1016/j.cmpb.2025.108658","DOIUrl":"10.1016/j.cmpb.2025.108658","url":null,"abstract":"<div><div>We present a systematic review of methods used to estimate Dynamic Treatment Regimens (DTR) using observational healthcare data and provide a brief summary of their strengths and weaknesses, evaluation metrics, and suitable research problem settings. We considered all observational studies identified in PubMed or EMBASE between January 1950 until January 2022, including only studies that evaluated medical treatments or interventions as exposure and/or outcome in patients and where DTRs were estimated. 83 studies met our inclusion criteria; 44.6% estimating DTR utilizing reinforcement learning, 18.1% utilizing counterfactual-based models, 12.1% utilizing classification-based methods, and 9.6% utilized g-methods. Among the studies analyzed, 28.9% aimed to replicate human expert DTRs, while 71.1% aimed to refine and improve existing DTRs. Approximately two-thirds of studies (65.1%) reported the assumptions required for their applied methods, such as exchangeability, positivity, consistency, and Markov property. Most of the studies (83.1%) estimated DTRs with more than two treatment options; 50.6% mentioned time-varying confounders, only a few estimated conditional average treatment effects (7.2%). Most (85.5%) validated their methods, with 32.5% using expected outcomes (e.g., survival rates), 26.5% employing simulated data, and 25.3% conducting direct comparisons with observational data.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"263 ","pages":"Article 108658"},"PeriodicalIF":4.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An accurate and trustworthy deep learning approach for bladder tumor segmentation with uncertainty estimation","authors":"Jie Xu ,&nbsp;Haixin Wang ,&nbsp;Min Lu ,&nbsp;Hai Bi ,&nbsp;Deng Li ,&nbsp;Zixuan Xue ,&nbsp;Qi Zhang","doi":"10.1016/j.cmpb.2025.108645","DOIUrl":"10.1016/j.cmpb.2025.108645","url":null,"abstract":"<div><div><em>Background and Objective:</em> Although deep learning-based intelligent diagnosis of bladder cancer has achieved excellent performance, the reliability of neural network predicted results may not be evaluated. This study aims to explore a trustworthy AI-based tumor segmentation model, which not only outputs predicted results but also provides confidence information about the predictions.</div><div><em>Methods:</em> This paper proposes a novel model for bladder tumor segmentation with uncertainty estimation (BSU), which is not merely able to effectively segment the lesion area but also yields an uncertainty map showing the confidence information of the segmentation results. In contrast to previous uncertainty estimation, we utilize test time augmentation (TTA) and test time dropout (TTD) to estimate aleatoric uncertainty and epistemic uncertainty in both internal and external datasets to explore the effects of both uncertainties on different datasets.</div><div><em>Results:</em> Our BSU model achieved the Dice coefficients of 0.766 and 0.848 on internal and external cystoscopy datasets, respectively, along with accuracy of 0.950 and 0.954. Compared to the state-of-the-art methods, our BSU model demonstrated superior performance, which was further validated by the statistically significance of the t-tests at the conventional level. Clinical experiments verified the practical value of uncertainty estimation in real-world bladder cancer diagnostics.</div><div><em>Conclusions:</em> The proposed BSU model is able to visualize the confidence of the segmentation results, serving as a valuable addition for assisting urologists in enhancing both the precision and efficiency of bladder cancer diagnoses in clinical practice.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"263 ","pages":"Article 108645"},"PeriodicalIF":4.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418845","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":"Improving clinical decision making by creating surrogate models from health technology assessment models: A case study on Type 1 Diabetes Melitus","authors":"Rafael Arnay del Arco,&nbsp;Iván Castilla Rodríguez,&nbsp;Marco A. Cabrera Hernández","doi":"10.1016/j.cmpb.2025.108646","DOIUrl":"10.1016/j.cmpb.2025.108646","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Computerized clinical decision support systems (CDSS) that incorporate the latest scientific evidence are essential for enhancing patient care quality. Such systems typically rely on some kind of model to accurately represent the knowledge required to assess the clinicians. Although the use of complex and computationally demanding simulation models is common in this field, such models limit the potential applications of CDSSs, both in real-time applications and in simulation-in-the-loop optimization tools. This paper presents a case study on Type 1 Diabetes Mellitus (T1DM) to demonstrate the development of surrogate models from health technology assessment models, with the aim of enhancing the potential of CDSSs.</div></div><div><h3>Methods:</h3><div>The paper details the process of developing machine learning (ML) based surrogate models, including the generation of a dataset for training and testing, and the comparison of different ML techniques. A number of distinct groupings of comorbidities were utilized in the creation of models, which were trained to predict confidence intervals for the time to develop each complication.</div></div><div><h3>Results:</h3><div>The results of the intersection over union (IoU) analysis between the simulation model output and the surrogate models output for the comorbidities under study were greater than 0.9.</div></div><div><h3>Conclusion:</h3><div>The study concludes that ML-based surrogate models are a viable solution for real-time clinical decision-making, offering a substantial speedup in execution time compared to traditional simulation models.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"262 ","pages":"Article 108646"},"PeriodicalIF":4.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419941","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":"Methods for estimating resting energy expenditure in intensive care patients: A comparative study of predictive equations with machine learning and deep learning approaches","authors":"Christopher Yew Shuen Ang ,&nbsp;Mohd Basri Mat Nor ,&nbsp;Nur Sazwi Nordin ,&nbsp;Thant Zin Kyi ,&nbsp;Ailin Razali ,&nbsp;Yeong Shiong Chiew","doi":"10.1016/j.cmpb.2025.108657","DOIUrl":"10.1016/j.cmpb.2025.108657","url":null,"abstract":"<div><h3>Background</h3><div>Accurate estimation of resting energy expenditure (REE) is critical for guiding nutritional therapy in critically ill patients. While indirect calorimetry (IC) is the gold standard for REE measurement, it is not routinely feasible in clinical settings due to its complexity and cost. Predictive equations (PEs) offer a simpler alternative but are often inaccurate in critically ill populations. While recent advancements in machine learning (ML) and deep learning (DL) offer potential for improving REE estimation by capturing complex relationships between physiological variables, these approaches have not yet been widely applied or validated in critically ill populations.</div></div><div><h3>Methodology</h3><div>This prospective study compared the performance of nine commonly used PEs, including the Harris-Benedict (H-B1919), Penn State, and TAH equations, with ML models (XGBoost, Random Forest Regressor [RFR], Support Vector Regression), and DL models (Convolutional Neural Networks [CNN]) in estimating REE in critically ill patients. A dataset of 300 IC measurements from an intensive care unit (ICU) was used, with REE measured by both IC and PEs. The ML/DL models were trained using a combination of static (i.e., age, height, body weight) and dynamic (i.e., minute ventilation, body temperature) variables. A five-fold cross validation was performed to assess the model prediction performance using the root mean square error (RMSE) metric.</div></div><div><h3>Results</h3><div>Of the PEs analysed, H-B1919 yielded the lowest RMSE at 362 calories. However, the XGBoost and RFR models significantly outperformed all PEs, achieving RMSE values of 199 and 200 calories, respectively. The CNN model demonstrated the poorest performance among ML models, with an RMSE of 250 calories. The inclusion of additional categorical variables such as body mass index (BMI) and body temperature classes slightly reduced RMSE across ML and DL models. Despite data augmentation and imputation techniques, no significant improvements in model performance were observed.</div></div><div><h3>Conclusion</h3><div>ML models, particularly XGBoost and RFR, provide more accurate REE estimations than traditional PEs, highlighting their potential to better capture the complex, non-linear relationships between physiological variables and REE. These models offer a promising alternative for guiding nutritional therapy in clinical settings, though further validation on independent datasets and across diverse patient populations is warranted.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"262 ","pages":"Article 108657"},"PeriodicalIF":4.9,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402668","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":"Finite element analysis predicts a major mechanical role of epicardial adipose tissue in atherosclerotic coronary disease and angioplasty","authors":"Diana Marcela Muñoz Sarmiento ,&nbsp;Danna Yeisenia Ferreira Cortés ,&nbsp;Mariana Caicedo Pérez ,&nbsp;Oswaldo Esteban Llanos Eraso ,&nbsp;Cindy Vanessa Vargas Ruiz ,&nbsp;Cristian David Benavides Riveros ,&nbsp;Diana Paola Ahumada Riaño ,&nbsp;Carlos Julio Cortés Rodríguez","doi":"10.1016/j.cmpb.2025.108656","DOIUrl":"10.1016/j.cmpb.2025.108656","url":null,"abstract":"<div><h3>Background</h3><div>Understanding how atherosclerosis and angioplasty biomechanically affect the coronary artery wall is crucial for comprehending the pathophysiology of this disease and advancing potential treatments. However, acquiring this information experimentally or <em>in vivo</em> presents challenges. To overcome this, different computational methods have been employed. This research assessed the impact of atherosclerosis and angioplasty on the strains of each coronary artery tunic using the finite element method.</div></div><div><h3>Methods</h3><div>Anatomical data were used to create two three-dimensional models of the left anterior descending coronary artery: one representing a normal artery and the other with concentric atherosclerosis, which included the surrounding epicardial fat tissue (EFT) and the three arterial tunics (e.g., intima, media, and adventitia). Blood pressure was applied to both models, and angioplasty was performed in the atherosclerotic model. The mean maximum principal and minimum principal strains were obtained for each layer in each case, and the impact of EFT was analyzed by comparing the results of including and omitting it. Furthermore, a sensitivity analysis was conducted for EFT stiffness, EFT volume, and blood pressure.</div></div><div><h3>Results</h3><div>Noteworthy biomechanical alterations were observed in the atherosclerotic model before and after angioplasty, compared to the healthy state. After angioplasty, strains in the media and adventitia layers increased on average by up to fivefold, whereas the intima layer experienced a comparatively lower impact. Similarly, excluding EFT resulted in an average fourfold increase in strains in the tunics of both the healthy and atherosclerotic models. In addition, in both healthy and atherosclerotic models, a rise in blood pressure caused the most significant increase in arterial tunic strains, followed by reduced EFT stiffness and increased EFT volume, in order of impact.</div></div><div><h3>Conclusion</h3><div>Coronary artery wall strains are significantly altered by atherosclerosis and angioplasty, leading to cellular growth in the media and adventitia layers and subsequent reobstruction of the lumen after the procedure. EFT strongly influences coronary wall biomechanics, with low EFT stiffness and high volume predicted as risk factors for the development and severity of atherosclerosis. However, all the above may be modulated through interventions targeting epicardial adipose tissue.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"262 ","pages":"Article 108656"},"PeriodicalIF":4.9,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419942","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":"EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals","authors":"Madhav Acharya ,&nbsp;Ravinesh C Deo ,&nbsp;Prabal Datta Barua ,&nbsp;Aruna Devi ,&nbsp;Xiaohui Tao","doi":"10.1016/j.cmpb.2025.108652","DOIUrl":"10.1016/j.cmpb.2025.108652","url":null,"abstract":"<div><h3>Background and objective</h3><div>Deep learning models have gained widespread adoption in healthcare for accurate diagnosis through the analysis of brain signals. Neurodegenerative disorders like Alzheimer's Disease (AD) and Frontotemporal Dementia (FD) are increasingly prevalent due to age-related brain volume reduction. Despite advances, existing models often lack comprehensive multi-class classification capabilities and are computationally expensive. This study addresses these gaps by proposing EEGConvNeXt, a novel convolutional neural network (CNN) model for detecting AD and FD using electroencephalogram (EEG) signals with high accuracy.</div></div><div><h3>Materials and method</h3><div>In this research, we employ an open-access EEG signal public dataset containing three distinct classes: AD, FD, and control subjects. We then constructed a newly proposed EEGConvNeXt model comprised of a 2-dimensional CNN algorithm that firstly converts the EEG signals into power spectrogram-based images. Secondly, these images were used as input for the proposed EEGConvNeXt model for automated classification of AD, FD, and a control outcome. The proposed EEGConvNeXt model is therefore a lightweight model that contributes to a new image classification CNN structure based on the transformer model with four primary stages: a stem, a main model, downsampling, and an output stem.</div></div><div><h3>Results</h3><div>The EEGConvNeXt model achieved a classification accuracy of ∼95.70% for three-class detection (AD, FD, and control), validated using a hold-out strategy. Binary classification cases, such as AD versus FD and FD versus control, achieved accuracies exceeding 98%, demonstrating the model's robustness across scenarios.</div></div><div><h3>Conclusions</h3><div>The proposed EEGConvNeXt model demonstrates high classification performance with a lightweight architecture suitable for deployment in resource-constrained settings. While the study establishes a novel framework for AD and FD detection, limitations include reliance on a relatively small dataset and the need for further validation on diverse populations. Future research should focus on expanding datasets, optimizing architecture, and exploring additional neurological disorders to enhance the model's utility in clinical applications.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"262 ","pages":"Article 108652"},"PeriodicalIF":4.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379299","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":"Hybrid-noise generative diffusion probabilistic model for cervical spine MRI image generation","authors":"Enyuan Pan ,&nbsp;Yuan Zhong ,&nbsp;Ping Li ,&nbsp;Yi Yang ,&nbsp;Jin Zhou","doi":"10.1016/j.cmpb.2025.108639","DOIUrl":"10.1016/j.cmpb.2025.108639","url":null,"abstract":"<div><div>Medical imaging is crucial for artificial intelligence-based clinician decision-making. However, learning anatomical features from limited samples poses a challenge. To address this issue, recent studies have employed diffusion models to generate medical imaging data, demonstrating the potential for high-quality medical image generation through deep learning. In this paper, we propose a high-quality cervical MRI image generation method called the Cervical Spine MRI Diffusion Probabilistic Model (CSM-DPM). Considering the complexity of neck MRI image data, our method uses a hybrid of standard Gaussian noise and point noise obtained by sampling within 2D Gaussian noise fields to approximate the true distribution of the image data. Meanwhile, the cosine noise schedule is used to make the morphology of the generated vertebral blocks and other focal areas more visually natural and clear. Furthermore, to enhance the noise prediction capabilities of UNet in DDPM, we devise the Asa-ResUNet module, which incorporates an asymmetric attention mechanism. This mechanism includes spatial attention on different ResUNet layers to improve feature extraction and incorporates high-level semantic information. We further enhance the stability and robustness of the Asa-ResUNet by using an exponential weighting strategy (EMA). Experiments demonstrate that our method produces cervical spine MRI images with FID values that are up to 15.79%, 52.61%, and 46.57% lower than those produced by DDPM, DDIM, and F-PNDM, respectively, indicating superior image quality. Segmentation experiments confirm that the generated images can enhance the overall performance of segmentation models when used for training.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"262 ","pages":"Article 108639"},"PeriodicalIF":4.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning based finite element analysis for personalized prediction of pressure injury risk in patients with spinal cord injury","authors":"Ke Zhang ,&nbsp;Yufang Chen ,&nbsp;Chenglong Feng ,&nbsp;Xinhao Xiang ,&nbsp;Xiaoqing Zhang ,&nbsp;Ying Dai ,&nbsp;Wenxin Niu","doi":"10.1016/j.cmpb.2025.108648","DOIUrl":"10.1016/j.cmpb.2025.108648","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Patients with spinal cord injury (SCI), are prone to pressure injury (PI) in the soft tissues of buttocks. Early prediction of PI holds the potential to reduce the occurrence and progression of PI. This study proposes a machine learning model to predict soft tissue stress/strain and evaluate PI risk in SCI patients.</div></div><div><h3>Methods</h3><div>Based on the standard database from parametric models of buttock, the biomechanical response of soft tissues and risk factors affecting PI were analyzed. A comprehensive assessment of multiple machine-learning methods was performed to predict the risk of PI, the selected optimal model is explained locally and globally using Shapley additive explanations (SHAP).</div></div><div><h3>Results</h3><div>The proposed hybrid model for predicting PI consists of a backpropagation neural network and Extreme Gradient Boosting, performed the coefficient of determination (R²) of 0.977.</div></div><div><h3>Conclusion</h3><div>The model exhibits accurate performance which may be considered as the ideal method for predicting PI. Furthermore, it can be used with other health-monitoring equipment to improve the quality of patients with SCI or other dysfunctional diseases.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108648"},"PeriodicalIF":4.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350072","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":"Cross-modal alignment and contrastive learning for enhanced cancer survival prediction","authors":"Tengfei Li ,&nbsp;Xuezhong Zhou ,&nbsp;Jingyan Xue ,&nbsp;Lili Zeng ,&nbsp;Qiang Zhu ,&nbsp;Ruiping Wang ,&nbsp;Haibin Yu ,&nbsp;Jianan Xia","doi":"10.1016/j.cmpb.2025.108633","DOIUrl":"10.1016/j.cmpb.2025.108633","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Integrating multimodal data, such as pathology images and genomics, is crucial for understanding cancer heterogeneity, personalized treatment complexity, and enhancing survival prediction. However, most current prognostic methods are limited to a single domain of histopathology or genomics, inevitably reducing their potential for accurate patient outcome prediction. Despite advancements in the concurrent analysis of pathology and genomic data, existing approaches inadequately address the intricate intermodal relationships.</div></div><div><h3>Methods:</h3><div>This paper introduces the CPathomic method for multimodal data-based survival prediction. By leveraging whole slide pathology images to guide local pathological features, the method effectively mitigates significant intermodal differences through a cross-modal representational contrastive learning module. Furthermore, it facilitates interactive learning between different modalities through cross-modal and gated attention modules.</div></div><div><h3>Results:</h3><div>The extensive experiments on five public TCGA datasets demonstrate that CPathomic framework effectively bridges modality gaps, consistently outperforming alternative multimodal survival prediction methods.</div></div><div><h3>Conclusion:</h3><div>The model we propose, CPathomic, unveils the potential of contrastive learning and cross-modal attention in the representation and fusion of multimodal data, enhancing the performance of patient survival prediction.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"263 ","pages":"Article 108633"},"PeriodicalIF":4.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418850","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}