Computer methods and programs in biomedicine最新文献

{"title":"Evaluation of the linear and nonlinear classifiers for distinguishing between healthy subjects and patients with valvular heart diseases based on electrocardiograms, seismocardiograms, and gyrocardiograms.","authors":"Szymon Sieciński, Marcin Grzegorzek","doi":"10.1016/j.cmpb.2025.108925","DOIUrl":"https://doi.org/10.1016/j.cmpb.2025.108925","url":null,"abstract":"<p><strong>Background and objective: </strong>Heart rate variability (HRV) is a prognostic marker in numerous cardiovascular and non-cardiovascular conditions. Valvular heart disease (VHD) is a cardiovascular disease that affects the heart valves (aortic valve, mitral valve, pulmonic valve and tricupsid valve) and is the third most common cardiovascular disease. Traditional methods, such as echocardiography, computed tomography, and magnetic resonance imaging, are effective, but their limitations in outpatient monitoring have led to the exploration of alternative techniques, such as electrocardiography (ECG), seismocardiography (SCG) and gyrocardiography (SCG). In this study, we evaluated seven methods for differentiation between healthy volunteers and patients with valvular heart diseases: three linear classifiers (Logistic Regression, Support Vector Machine with a linear kernel, Ridge Regression) and four decision tree-based models (Random Forest, Bagged Trees, Gradient Boosting, Extreme Gradient Boosting).</p><p><strong>Methods: </strong>The study was carried out in two publicly available data sets with concurrent electrocardiographic (ECG), seismocardiographic (SCG), and gyrocardiographic (GCG) signals (Mechanocardiograms with ECG reference and An Open-access Database for the Evaluation of Cardio-mechanical Signals from Patients with Valvular Heart Diseases) that have 29 and 30 simultaneous recordings, respectively. All classifiers were trained on HRV indices calculated from concurrent ECG, SCG, and GCG signals from both datasets. Heartbeats in the SCG and GCG signals were detected as local maxima delayed from the locations of QRS complexes in the ECG signal by maximally 150 ms.</p><p><strong>Results: </strong>The results showed that linear and tree-based classifiers that work on HRV indices derived from ECG, SCG and GCG signals (accuracy of 0.9492 in the best case, 0.7627 in the worst case) could be a useful tool to differentiate between different heart diseases.</p><p><strong>Conclusions: </strong>The use of multimodal recordings provides more comprehensive information on the state of the cardiovascular system that, in combination with machine learning-based classifiers, could help diagnose cardiovascular conditions more efficiently.</p>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":" ","pages":"108925"},"PeriodicalIF":4.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764693","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":"Computational methods used to investigate atherosclerosis progression in coronary arteries: structural FEA, CFD or FSI","authors":"Vittorio Lissoni ,&nbsp;Giulia Luraghi ,&nbsp;Marco Stefanati ,&nbsp;Jose Felix Rodriguez Matas ,&nbsp;Francesco Migliavacca","doi":"10.1016/j.cmpb.2025.108959","DOIUrl":"10.1016/j.cmpb.2025.108959","url":null,"abstract":"<div><h3>Background and objectives</h3><div>In recent years, computational simulations have emerged as valuable tools for the evaluation of atherosclerosis progression in coronary anatomies, although only a few studies have utilized more realistic Fluid-Structure Interaction (FSI) simulations. This work aims to compare the results of Computational Fluid Dynamics (CFD), Structural Finite Element Analysis (structural FEA) and FSI simulations in order to assess differences in plaque progression indices estimation.</div></div><div><h3>Methods</h3><div>We performed structural FEA, CFD and FSI on five patient-specific epicardial coronary anatomies using the commercial software LS-Dyna. To account for the vessel pre-stress, the zero-pressure configuration was calculated for each anatomy with an inverse elastostatic algorithm. CFD, structural FEA and FSI simulations were performed applying boundary conditions based on physiological values.</div></div><div><h3>Results</h3><div>The comparison between structural FEA and FSI showed similar stress distribution and vessel expansions, with differences found only in the distal parts of the coronaries, where pressure reduction due to pressure loss affects the vessel walls. The elastic walls of the coronaries impact blood flow, resulting in a more disturbed flow. However, time averaged wall shear stress (TAWSS) and oscillatory shear index (OSI) distributions are similar across each coronary between CFD and FSI; TAWSS is slightly higher in CFD while OSI peaks are higher in FSI.</div></div><div><h3>Conclusion</h3><div>In conclusion, given the significantly higher computational costs of FSI, we believe that CFD and structural FEA offer a more practical and cost-effective approach, providing results comparable to those of FSI, making them preferable options.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108959"},"PeriodicalIF":4.9,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614151","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":"Modeling changes in genetic heterogeneity using games with resources","authors":"Katarzyna Hajdowska ,&nbsp;Andrzej Swierniak ,&nbsp;Damian Borys","doi":"10.1016/j.cmpb.2025.108916","DOIUrl":"10.1016/j.cmpb.2025.108916","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>This study explores an extension of the classic Hawk and Dove evolutionary game model by considering the influence of environmental or external resources on the players’ fitness. This allows us to model the resulting heterogeneous population dynamics, which is of great importance for simulating cancer population growth and optimizing anti-cancer therapies.</div></div><div><h3>Methods:</h3><div>To model population heterogeneity, we are using an extension of classical spatial evolutionary game theory by introducing multidimensional spatial evolutionary games (MSEG). This allows for the study of genetic heterogeneity on a multidimensional lattice. The classic Hawk and Dove model is modified to reflect the impact of external resources. Various types and shapes of resource functions were included in the payoff matrix and then simulated to examine their impact on the model’s dynamics and population heterogeneity.</div></div><div><h3>Results:</h3><div>The results are presented in time-dependent plots for both mean-field and spatial models. Additionally, spatial 2D and 3D matrices are presented to show the spatial distribution of both phenotypes analyzed in the extended Hawk and Dove model. The results reveal significant differences between the mean-field and spatial models for the same parameter values. Furthermore, differences are observed when comparing models with different resource functions.</div></div><div><h3>Conclusion:</h3><div>The two-phenotype model was used to show the influence of external, time- and phenotype-specific resource functions on the dynamics of the game’s phenotypes. Moreover, the study highlights that spatial models, which provide more accurate information about population heterogeneity, can yield significantly different results compared to mean-field models.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108916"},"PeriodicalIF":4.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613864","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":"Blind super-resolution for handheld ultrasound image: Two-stage degradation based unpaired deep learning","authors":"Zhencun Jiang ,&nbsp;Kangrui Ren ,&nbsp;Kefan Wang ,&nbsp;Zhongjie Wang","doi":"10.1016/j.cmpb.2025.108956","DOIUrl":"10.1016/j.cmpb.2025.108956","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Handheld ultrasound devices are widely used in clinical diagnostics and examinations due to their portability. However, their imaging quality is often inferior to that of large-scale ultrasound devices due to hardware limitations.</div></div><div><h3>Methods</h3><div>To enhance the image quality of handheld ultrasound devices, a blind super-resolution method based on two-stage degradation is proposed. The first degradation stage, referred to as frequency probabilistic degradation, is designed to mitigate the structural distortion and texture loss commonly introduced by general probabilistic degradation. In this stage, high-quality ultrasound images acquired from large-scale ultrasound devices are decomposed into high-frequency and low-frequency components using wavelet transform. These two components are respectively processed with blur kernels and noise, both generated by neural networks, and then recombined to produce synthetic images. In the second degradation stage, Gaussian blur kernels and speckle noise are randomly generated and applied to the synthetic images, further degrading their quality and enhancing the diversity of the training samples. Additionally, recognizing that the general perceptual loss function is insufficient to capture the unique characteristics of ultrasound images, a new ultrasound perceptual loss function is introduced.</div></div><div><h3>Results</h3><div>Eventually, supervised learning is performed using the EDSR model on the synthetic images after two-stage degradation and high-quality images, and blind super-resolution of low-quality ultrasound images is realized.</div></div><div><h3>Conclusion</h3><div>Experiments are carried out on public datasets to demonstrate the proposed method, the experimental results show that the proposed method outperforms state-of-the-art techniques in terms of image quality improvement.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108956"},"PeriodicalIF":4.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672666","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":"Artificial intelligence in forensic pathology: Multi-organ postmortem pathomics for estimating postmortem interval","authors":"Guoshuai An ,&nbsp;Yu Gao ,&nbsp;Siyuan Cheng ,&nbsp;Na Li ,&nbsp;Kang Ren ,&nbsp;Qiuxiang Du ,&nbsp;Rufeng Bai ,&nbsp;Junhong Sun","doi":"10.1016/j.cmpb.2025.108949","DOIUrl":"10.1016/j.cmpb.2025.108949","url":null,"abstract":"<div><h3>Background</h3><div>Accurate estimation of the postmortem interval is crucial in forensic investigations. Pathomics presents a promising advancement by leveraging whole-slide images as a novel data modality for the diagnosis and prognosis of diseases in clinical situations. The extended application of this technology in forensic postmortem image analysis is expected to give rise to postmortem pathomics as an important subfield.</div></div><div><h3>Objective</h3><div>This study aimed to develop a three-level hierarchical strategy using pathomics to analyze postmortem histological images data, develop multi-organ integrated model for the postmortem interval estimation, and lay the foundation for postmortem pathomics.</div></div><div><h3>Methods</h3><div>Twelve Bama miniature pigs were euthanized, and liver, kidney, and skeletal muscle tissues were collected at 6, 24, 48, and 96 h postmortem. Hematoxylin and eosin stained whole slide images were divided into 512 × 512 pixel patches. Low-quality patches were excluded using Otsu thresholding, and color normalization was applied using the Vahadane algorithm to minimize staining variability. Deep learning models were trained on patch-level data using transfer learning and evaluated for interpretability with Grad-CAM. Slide-level predictions were obtained via organ-specific deep feature aggregation and machine learning models, while a multi-organ integrated model was developed using a stacking ensemble combining above machine learning models and a logistic regression. Four additional pigs were introduced for external validation at the multi-organ integrated individual-level.</div></div><div><h3>Results</h3><div>DenseNet121 demonstrated superior performance for liver and kidney, while VGG16 performed best for skeletal muscle tissue. These models were designated as postmortem-liver-net, postmortem-kidney-net, and postmortem-muscle-net, respectively, and employed to extract pathomics features from images. Slide-level models trained on these features vectors achieved accuracies of 81.25% (liver), 87.5% (kidney), and 62.5% (muscle). A stacking model integrating probability output of these three slide-level models achieved internal and external test accuracies at multi-organ integrated individual-level of 93.75% and 87.5%, respectively.</div></div><div><h3>Conclusion</h3><div>This study demonstrated the potential of pathomics and deep learning for postmortem interval estimation. The proposed three-level framework effectively integrated multi-organ features, introducing whole-slide images as a novel modality and offering an innovative strategy for postmortem interval estimation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108949"},"PeriodicalIF":4.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596151","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":"Retrieval-based adaptive fusion strategy for medical report generation","authors":"Yingying Huang ,&nbsp;Yang Si ,&nbsp;Bingliang Hu ,&nbsp;Jiang Shen ,&nbsp;Linshen Xie ,&nbsp;Dongsheng Wu ,&nbsp;Quan Wang","doi":"10.1016/j.cmpb.2025.108907","DOIUrl":"10.1016/j.cmpb.2025.108907","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Retrieval-based medical report generation methods attempt to improve efficiency by reusing historical reports, but their fixed feature-concatenation strategies often introduce cross-case redundancy. Moreover, most methods are designed for low-resolution X-ray images, and their evaluation metrics rely on textual similarity and overlook the implications of misdescription. To address these, we first constructed a high-resolution CT-report dataset, comprising 9 categories of chest CT scans and corresponding reports from 505 patients. Then, we propose RAFS, a retrieval-based adaptive fusion strategy, to dynamically balance contributions from generation and retrieval modules. Finally, we propose DICE, a dual-perspective integrated clinical evaluation including consensus-based positive scoring and penalties of misdescription.</div></div><div><h3>Methods:</h3><div>RAFS integrates an attention module to calculate the similarity between the current generated word’s hidden state and the retrieved text, passing the result through a fully connected layer to obtain retrieval probabilities. After, obtained attention weights are feed the Sigmoid function and its result for fusing the generation probabilities and retrieval probabilities.</div></div><div><h3>Results:</h3><div>RAFS achieves superior performance with BLEU-4, METEOR, ROUGE_L, CIDEr and the average of DICE scores of 45.8, 32.9, 59.1, 79.3 and 64.6 in the CT report generation task, outperforming existing methods. methods.</div></div><div><h3>Conclusion:</h3><div>RAFS significantly enhances the clinical interpretability of generated reports, with future work dedicated to optimizing the characterization of local pathological lesions.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108907"},"PeriodicalIF":4.9,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579665","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":"TAC-ECG: A task-adaptive classification method for electrocardiogram based on cross-modal contrastive learning and low-rank convolutional adapter","authors":"Rongjia Wang ,&nbsp;Xunde Dong ,&nbsp;Xiuling Liu ,&nbsp;Yihai Fang ,&nbsp;Jianhong Dou ,&nbsp;Yupeng Qiang ,&nbsp;Yang Yang ,&nbsp;Fei Hu","doi":"10.1016/j.cmpb.2025.108918","DOIUrl":"10.1016/j.cmpb.2025.108918","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cardiovascular diseases are one of the major health threats to humans. Researchers have proposed numerous deep learning-based methods for the automatic analysis of electrocardiogram (ECG), achieving encouraging results. However, many existing methods are limited to task-specific model training and require retraining or full fine-tuning when confronted with a new ECG classification task, thus lacking flexibility in clinical applications.</div></div><div><h3>Methods:</h3><div>In this study, we propose a <strong>T</strong>ask-<strong>A</strong>daptive <strong>C</strong>lassification method for ECG (TAC-ECG) based on cross-modal contrastive learning and low-rank convolutional adapters. TAC-ECG comprises two main phases. In the first phase, inspired by the Contrastive Language-Image Pre-training, we design the <strong>C</strong>ontrastive <strong>E</strong>CG-<strong>T</strong>ext <strong>P</strong>re-training (CETP) to pre-train a robust ECG encoder. In the second phase, the pre-trained ECG encoder is frozen and integrated with a lightweight plug-in, the <strong>L</strong>ow-<strong>R</strong>ank <strong>C</strong>onvolutional Adapter (LRC-Adapter), forming an extensible ECG classification model. The frozen encoder extracts more discriminative features from the ECG signal, while the LRC-Adapter enables task-specific adaptation. For diverse ECG classification tasks, TAC-ECG only requires training the LRC-Adapter. This mechanism enables TAC-ECG to efficiently perform different ECG classification tasks, significantly reducing resource consumption and deployment costs in multi-tasking scenarios compared to traditional fully fine-tuned methods.</div></div><div><h3>Results:</h3><div>We conducted extensive experiments using six different network architectures as ECG encoders. Specifically, we performed ECG classification experiments on four datasets: CPSC2018, Cinc2017, PTB-XL, and Chapman, targeting 9-category, 3-category, 5-category, and 4-category classifications respectively. The TAC-ECG achieved highly competitive results using only approximately 3% of the trainable parameters and approximately 25% of the total parameters compared to the fully fine-tuned method. These results demonstrates the effectiveness and practicality of the TAC-ECG method.</div></div><div><h3>Conclusion:</h3><div>The TAC-ECG offers a flexible and efficient method for ECG classification, enabling rapid adaptation to diverse tasks and enhancing clinical diagnostic practicality.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108918"},"PeriodicalIF":4.9,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587664","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 numerical simulation study of soft tissue resection for low-damage precision cancer surgery","authors":"Yonghang Jiang ,&nbsp;Justicia Kyeremeh ,&nbsp;Xichun Luo ,&nbsp;Zhengjian Wang ,&nbsp;Ka Zhang ,&nbsp;Faxian Cao ,&nbsp;Lisa Asciak ,&nbsp;Asimina Kazakidi ,&nbsp;Grant D. Stewart ,&nbsp;Wenmiao Shu","doi":"10.1016/j.cmpb.2025.108937","DOIUrl":"10.1016/j.cmpb.2025.108937","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Precision cancer surgery aims to minimize tissue damage while ensuring effective tumor removal. The paper presented a numerical simulation study and its experimental validation to reveal the influence of surgical parameters on tissue fracture towards establishing precision cancer surgical procedure for achieving low tissue damage.</div></div><div><h3>Methods</h3><div>A mechanical tensile test was conducted on a clinically certified 3D-printed kidney model to characterize its biomechanical properties and determine constitutive model parameters. Based on these findings, we developed an advanced soft tissue resection simulation model, which can accurately capture the contact interactions between surgical tools and biological soft tissue. Additionally, we implemented a computational program that automates the selection of viscoelastic and hyperelastic properties, significantly reducing the need for repeated manual modeling. The accuracy of this simulation was validated through experimental resection tests.</div></div><div><h3>Results</h3><div>This method saves about 40 % of time compared to traditional simulation methods. The study analyzed the effects of different resection angles, depths, and velocities on tissue damage. The results indicate that minimal tissue damage occurs at a higher resection speed (30 mm/s), a smaller depth, and an angle of 15° for horizontal cutting.</div></div><div><h3>Conclusions</h3><div>Higher resection speeds enhance fracture toughness, making tissue easier to fracture with less internal deformation, while smaller cutting angles reduce fiber breakage and energy dissipation, leading to minimal tissue damage. These findings suggest that optimizing resection parameters can significantly reduce tissue damage. The study provides insights into refining precision cancer surgical techniques and contributes to developing improved resection strategies that minimize collateral tissue damage.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108937"},"PeriodicalIF":4.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579457","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 temporal convolutional network-based approach and a benchmark dataset for colonoscopy video temporal segmentation","authors":"Carlo Biffi ,&nbsp;Giorgio Roffo ,&nbsp;Pietro Salvagnini ,&nbsp;Andrea Cherubini","doi":"10.1016/j.cmpb.2025.108782","DOIUrl":"10.1016/j.cmpb.2025.108782","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Following recent advancements in computer-aided detection and diagnosis systems for colonoscopy, the automated reporting of colonoscopy procedures is set to further revolutionize clinical practice. A crucial yet underexplored aspect in the development of these systems is the creation of computer vision models capable of autonomously segmenting full-procedure colonoscopy videos into anatomical sections and procedural phases. In this work, we aim to create the first open-access dataset for this task and propose a state-of-the-art approach, benchmarked against competitive models.</div></div><div><h3>Methods:</h3><div>We annotated the publicly available REAL-Colon dataset, consisting of 2.7 million frames from 60 complete colonoscopy videos, with frame-level labels for anatomical locations and colonoscopy phases across nine categories. We then present ColonTCN, a learning-based architecture that employs custom temporal convolutional blocks designed to efficiently capture long temporal dependencies for the temporal segmentation of colonoscopy videos. We also propose a dual k-fold cross-validation evaluation protocol for this benchmark, which includes model assessment on unseen, multi-center data.</div></div><div><h3>Results:</h3><div>ColonTCN achieves state-of-the-art performance in classification accuracy while maintaining a low parameter count when evaluated using the two proposed k-fold cross-validation settings, outperforming competitive models. We report ablation studies to provide insights into the challenges of this task and highlight the benefits of the custom temporal convolutional blocks, which enhance learning and improve model efficiency.</div></div><div><h3>Conclusions:</h3><div>We believe that the proposed open-access benchmark and the ColonTCN approach represent a significant advancement in the temporal segmentation of colonoscopy procedures, fostering further open-access research to address this clinical need. Code and data are available at: <span><span>https://github.com/cosmoimd/temporal_segmentation</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108782"},"PeriodicalIF":4.9,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572924","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":"Deep fusion model integrating whole slide images and preoperative MRIs for survival prediction in IDH wild-type glioblastoma","authors":"Xinji Guo ,&nbsp;Mingyao Lai ,&nbsp;Jie Zhang ,&nbsp;Lichao Wang ,&nbsp;Haiyang Huang ,&nbsp;Lijun Huang ,&nbsp;Hainan Li ,&nbsp;Linbo Cai ,&nbsp;Jiuxing Liang","doi":"10.1016/j.cmpb.2025.108936","DOIUrl":"10.1016/j.cmpb.2025.108936","url":null,"abstract":"<div><h3>Background</h3><div>Glioblastoma (GBM) is an aggressive and highly malignant brain tumor, with isocitrate dehydrogenase (IDH) wild-type GBM being particularly notable for its poor prognosis. We developed the IDH wild-type Glioblastoma Integrated Survival Model (iGISM), a multimodal framework combining magnetic resonance imaging (MRI) and whole slide imaging (WSI) data for survival prediction in this subtype.</div></div><div><h3>Methods</h3><div>This retrospective study enrolled 180 patients with IDH wild-type GBM. The dataset included 180 H&amp;E-stained WSIs and 720 preoperative MRI scans (T1, T1C, T2, and FLAIR sequences), and was randomly split into training (<em>N</em> = 144) and validation (<em>N</em> = 36) cohorts. The iGISM comprises of two pathways: an MRI pathway and a WSI pathway. In the MRI pathway, a ResNet18 backbone was employed, augmented with coordinate attention module and feature aggregation module. In the WSI pathway, we introduced a novel patch sampling strategy that combines image information entropy and nuclear density. An MRI-guided Co-Attention module was designed to facilitate cross-modality interactions. The extracted MRI and WSI features were integrated to generate the final multimodal risk score.</div></div><div><h3>Results</h3><div>The iGISM achieved a concordance index (C-index) of 0.791 in the validation cohort, outperforming the MRI-only model (C-index: 0.713) and the WSI-only models (C-index range: 0.530–0.591). It also yielded the lowest integrated Brier score (IBS, 0.129), with time-dependent AUCs at 1, 2, and 3 years (0.821, 0.686, and 0.860, respectively). By comparison, the proposed WSI sampling strategy achieved the highest 17.7 % improvement in predictive performance over established methods.</div></div><div><h3>Conclusion</h3><div>The iGISM advances prognostic accuracy for IDH wild-type GBM by integrating MRI and WSI data, providing a valuable tool for guiding personalized treatment strategies in this patient population.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108936"},"PeriodicalIF":4.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144569847","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}