Computer methods and programs in biomedicine最新文献

{"title":"Development of a predictive model for Ki-67 index of meningiomas by integrating deep-learning, radiomics and clinical features utilizing fully automated segmentation results","authors":"Xin Ma ,&nbsp;Yajing Zhao ,&nbsp;Kaiyue Zhang ,&nbsp;Nan Mei ,&nbsp;Xuanxuan Li ,&nbsp;Jin Cui ,&nbsp;Jie Chen ,&nbsp;Yuxi Xie ,&nbsp;Yiping Lu ,&nbsp;Bo Yin","doi":"10.1016/j.cmpb.2025.109205","DOIUrl":"10.1016/j.cmpb.2025.109205","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the efficacy of clinical information, traditional radiological, radiomics and deep-learning features combinations for constructing a predictive model for the Ki-67 index of meningiomas.</div></div><div><h3>Material and methods</h3><div>This study acquired retrospective (198 cases) and prospective (22 cases) meningioma data between 2015 and 2020. Within the retrospective data, 160 cases were utilized for training, while 38 were allocated to an independent test. Ki-67 expression levels were dichotomized into low and high groups using a 4% threshold based on previous research. The study developed and evaluated five classifier models combining clinical information, radiomics and deep-learning features to predict Ki-67 expression levels. Model performance was evaluated via the receiver operating characteristic (ROC) curves and the area under the curve (AUC), obtaining a 95% confidence interval (CI) using DeLong testing. Subsequently, the most effective model was validated using prospective data from 22 cases.</div></div><div><h3>Results</h3><div>The eXtreme Gradient Boosting (XGBoost) classifier model showed optimal performance among the five classifier models. The AUC for the independent test dataset was 0.717 (CI: 0.575-0.858). After optimization, the AUC of the test dataset is 0.767 (CI: 0.631-0.903). The AUC for the prospective test data set was 0.773 (CI: 0.590-0.955). Decision curve analysis (DCA) showed that combining clinical information, radiomics, and deep-learning features resulted in the best predictive performance of the XGBoost classifier.</div></div><div><h3>Conclusion</h3><div>An integrated radiomics model enables Ki-67 prediction and has great potential to estimate the risk of tumor regrowth and recurrence non-invasively.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109205"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787096","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":"The added value of radiomic analysis for predicting spontaneous preterm birth in the first trimester","authors":"William Cancino ,&nbsp;Carlos Hernan Becerra-Mojica ,&nbsp;Said Pertuz","doi":"10.1016/j.cmpb.2025.109181","DOIUrl":"10.1016/j.cmpb.2025.109181","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Preterm birth (PTB) is a public health problem. Researchers have worked to identify ways to detect women at risk for PTB early in pregnancy. Although existing biomarkers allow some women to be detected in the second trimester, detection in the first trimester is warranted to initiate earlier interventions. This study aims to explore the added value of radiomic analysis of transvaginal ultrasound (TVUS) images in the construction of first-trimester risk assessment models for predicting spontaneous preterm birth (sPTB).</div></div><div><h3>Methods:</h3><div>A retrospective cohort study was conducted including pregnant women who attended their screening ultrasound examination between 11+0 and 13+6 weeks. Data on medical history (MH), cervical length (CL), and cervical consistency index (CCI) were collected, along with TVUS images. These images were subjected to a computerized radiomic analysis to extract features, which were used to train different machine learning models incorporating a feature selection process. The area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI) was used to evaluate the models’ performance in predicting sPTB.</div></div><div><h3>Results:</h3><div>A total of 253 pregnant women were included in the study, where 225 had a term birth and 28 had a sPTB. In sPTB prediction, MH, CL, and CCI obtained AUCs of 0.68 (95% CI, 0.56–0.79), 0.61 (95% CI, 0.48–0.73) and 0.67 (95% CI, 0.55–0.79), respectively. Among the evaluated machine learning models, logistic regression achieved the highest AUC and was therefore used to build the radiomic feature–based model (RF), which reached an AUC of 0.67 (95% CI, 0.56–0.76). When combining RF with MH and CCI, AUCs of 0.73 (95% CI, 0.63–0.82) and 0.74 (95% CI, 0.64–0.83) were achieved, respectively. The best performance was obtained by combining RF with both MH and CCI, reaching an AUC of 0.76 (95% CI, 0.68–0.84).</div></div><div><h3>Conclusion:</h3><div>The performance of the models improved by adding radiomic features, highlighting the potential of radiomic analysis for sPTB prediction in the first trimester.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109181"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682518","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":"MDM-DTA: Message Passing Neural Network with molecular descriptors and Mixture of Experts for drug–target affinity prediction","authors":"Yang Dai ,&nbsp;Xiaoyu Tan ,&nbsp;Haoyu Wang ,&nbsp;Gengchen Ma ,&nbsp;Yujie Xiong ,&nbsp;Xihe Qiu","doi":"10.1016/j.cmpb.2025.109163","DOIUrl":"10.1016/j.cmpb.2025.109163","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Drug–target affinity (DTA) prediction is a pivotal task in computational drug discovery, enabling the estimation of binding affinities between small molecules and their target proteins. This process is essential for reducing the costs, development time, and risks inherent in traditional drug development pipelines. Current DTA prediction models primarily rely on separate extraction and concatenation of drug and protein features. However, these models often fail to account for the complex semantic relationships within protein sequences, which limits their ability to accurately predict affinity.</div></div><div><h3>Methods:</h3><div>In response to these challenges, we propose MDM-DTA, a novel framework leveraging a Mixture of Experts (MoE) strategy to integrate diverse molecular and protein representations. For drug representation, MDM-DTA utilizes molecular graphs, which are processed via Message Passing Neural Networks (MPNNs), alongside molecular descriptors that are passed through a three-layer convolutional neural network (CNN). Protein features are extracted using a deep convolutional network enhanced with Squeeze-and-Excitation (SE) mechanisms to capture inter-channel dependencies. Furthermore, protein sequence semantics are encoded through pre-trained embeddings from a knowledge-guided Bidirectional Encoder Representations from Transformers (BERT) model and the Evolutionary Scale Modeling 2 (ESM2) model, enabling the model to capture contextual relationships within protein sequences.</div></div><div><h3>Results:</h3><div>Extensive experiments on three benchmark datasets demonstrate that MDM-DTA consistently outperforms state-of-the-art models of similar complexity in terms of predictive accuracy. The incorporation of both structural and semantic features significantly enhances the model’s ability to predict drug–target binding affinities, highlighting the importance of a multi-modal representation approach.</div></div><div><h3>Conclusions:</h3><div>The proposed MDM-DTA framework effectively integrates both molecular and semantic protein representations, providing superior performance in DTA prediction tasks. The results underscore the potential of MDM-DTA to improve the accuracy of computational drug discovery models, facilitating the identification of novel drug candidates and advancing the field of in silico drug development.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109163"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145596058","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":"Benchmarking foundation models and parameter-efficient fine-tuning for prognosis prediction in medical imaging","authors":"Filippo Ruffini ,&nbsp;Elena Mulero Ayllón ,&nbsp;Linlin Shen ,&nbsp;Paolo Soda ,&nbsp;Valerio Guarrasi","doi":"10.1016/j.cmpb.2025.109196","DOIUrl":"10.1016/j.cmpb.2025.109196","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Despite the significant potential of Foundation Models (FMs) in medical imaging, their application to prognosis prediction remains challenging due to data scarcity, class imbalance, and task complexity, limiting their clinical adoption. This study introduces the first structured benchmark to assess the robustness and efficiency of transfer learning strategies for FMs compared with convolutional neural networks (CNNs) in predicting COVID-19 patient outcomes from chest X-rays. The goal is to systematically compare fine-tuning strategies, classical and parameter-efficient, under realistic clinical constraints related to data scarcity and class imbalance, offering empirical guidance for AI deployment in clinical workflows.</div></div><div><h3>Methods:</h3><div>Four publicly available COVID-19 chest X-ray datasets were used, covering mortality, severity, and ICU admission, with varying sample sizes and class imbalances. CNNs pretrained on ImageNet and FMs pretrained on general or biomedical datasets were adapted using full fine-tuning, linear probing, and parameter-efficient methods. Models were evaluated under full-data and few-shot regimes using Matthews Correlation Coefficient (MCC) and Precision–Recall AUC (PR-AUC) with cross-validation and class-weighted losses.</div></div><div><h3>Results:</h3><div>CNNs with full fine-tuning performed robustly on small, imbalanced datasets, while FMs with Parameter-Efficient Fine-Tuning (PEFT), particularly LoRA and BitFit, achieved competitive results on larger datasets. Severe class imbalance degraded PEFT performance, whereas balanced data mitigated this effect. In few-shot settings, FMs showed limited generalization, with linear probing yielding the most stable results.</div></div><div><h3>Conclusions:</h3><div>No single fine-tuning strategy proved universally optimal. CNNs remain dependable for low-resource scenarios, whereas FMs benefit from parameter-efficient methods when data are sufficient.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109196"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145676217","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":"Explainable multimodal fusion for breast carcinoma diagnosis: A systematic review, open problems, and future directions","authors":"Mohammad Mehedi Hassan ,&nbsp;Anika Tahsin ,&nbsp;Md Golam Rabiul Alam ,&nbsp;Deema Alzamil ,&nbsp;Sahil Garg ,&nbsp;Md. Zia Uddin ,&nbsp;Nurul Choudhury ,&nbsp;Giancarlo Fortino","doi":"10.1016/j.cmpb.2025.109152","DOIUrl":"10.1016/j.cmpb.2025.109152","url":null,"abstract":"<div><div>Breast carcinoma (BC) remains one of the most common and lethal malignancies in women worldwide, making an early and accurate diagnosis a public health priority. Recent artificial intelligence (AI) research has increasingly explored multimodal fusion, with imaging, clinical records, histopathology, and genomic data to produce richer, more reliable predictions. In parallel, explainable AI (XAI) techniques aim to address a key barrier to clinical use: transparency about how deep learning models make decisions. This systematic review examines 49 peer-reviewed studies published between 2015 and 2025, following the PRISMA guidelines to analyze the landscape of multimodal learning and XAI in the diagnosis and prognosis of BC. We categorize reported fusion strategies from simple feature concatenation to advanced attention-based, gated, and hybrid architectures, designed to manage data heterogeneity and missing modalities. We also document model designs, including transfer learning, transformers, graph neural networks (GNNs), autoencoders, and ensembles, supported by preprocessing methods like stain normalization and GAN-based augmentation, and the use of XAI techniques: Grad-CAM, SHAP, and attention weights, which help bridge the gap between complex AI systems and clinical workflows. Across included studies, multimodal models often outperformed unimodal baselines; however, effect sizes varied by dataset, validation design (cross-validation versus external), and the handling of missing modalities. Our review highlights persistent open problems, including the limited availability of multimodal datasets, inconsistent benchmarks, and the scarcity of interpretable models in real world settings. Future research on BC care should focus on developing ensemble-based fusion approaches, validating them in various clinics, and embedding clinician expertise. All of which are crucial for developing AI systems that are accurate, transparent, generalizable, and trustworthy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109152"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145511891","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":"Robust analysis of the tumor spectrum in a preclinical model of breast cancer reveals stable subtypes with distinct growth patterns","authors":"Sahar A Mohammed ,&nbsp;Siyavash Shabani ,&nbsp;Muhammad Sohaib ,&nbsp;Corina Nicolescu ,&nbsp;Garrett Winkelmaier ,&nbsp;William Chou ,&nbsp;Lin Ma ,&nbsp;Jinsong Chen ,&nbsp;Mary Helen Barcellos-Hoff ,&nbsp;Bahram Parvin","doi":"10.1016/j.cmpb.2025.109135","DOIUrl":"10.1016/j.cmpb.2025.109135","url":null,"abstract":"<div><h3>Background and objective</h3><div>The tumor microenvironment plays a crucial role in influencing tumor progression and responses to therapy, shaped by both inherent tumor features and external factors. We aim to develop a pipeline that computes tumor subtypes and growth patterns based on nuclear shape, spatial arrangement, and protein measurements in preclinical models. Preclinical models enable the investigation of exogenous perturbations on tumor development. In this context, accurately segmenting and classifying nuclei is vital. The main challenges include: (i) the presence of densely packed nuclei, and (ii) the need to characterize tumor diversity across a large set of mouse-derived tumor samples.</div></div><div><h3>Method</h3><div>The computational pipeline requires methods for nuclear segmentation and tumor heterogeneity characterization. For robust segmentation of nuclei, we developed LoG-based Saliency for Guided Encoding with Convolutional Block Attention Module (LoGSAGE-CBAM), a dual-encoder segmentation model that combines a Swin Transformer with a saliency encoder based on Laplacian of Gaussian (LoG) response. The outputs of these encoders are then fused through a CBAM module, and the model is trained with a curvature-aware loss function. Subsequently, the immune cells are classified, and their locations are recorded. To capture the tumor spectrum, cellular responses and localizations are binarized, and tumor subtypes are identified, which are then associated with preclinical variables using Cox regression.</div></div><div><h3>Results</h3><div>The integrated computational pipeline identified four stable tumor subtypes in 184 tumor-derived mice using computed indices from 2168,733 nuclei. At the same time, the LoGSAGE-CBAM achieved a segmentation performance with Dice 95.5 and RCE: 86.6. One of the subtypes is enriched in K14+ tumors and CD8+ lymphocytes and is associated with longer latency.</div></div><div><h3>Conclusion</h3><div>The proposed computational pipeline can provide both novel insights and automation for biomarker discovery in preclinical studies and pharmaceutical research.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109135"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145457901","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":"Developing a novel framework using optimized active stacking and explainable AI for heart disease prediction","authors":"Aymin Javed ,&nbsp;Nadeem Javaid ,&nbsp;Abdul Khader Jilani Saudagar ,&nbsp;Dragan Pamucar","doi":"10.1016/j.cmpb.2025.109169","DOIUrl":"10.1016/j.cmpb.2025.109169","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Heart disease is still the top driver of death worldwide, and developing accurate, interpretable, and efficient predictive systems is essential to enable early diagnosis in time for effective intervention. Although significant efforts have been made, the existing machine learning approaches are subject to problems including class imbalance, high-dimensional input features, complex hyperparameter tuning problems, low classification accuracy, and a limited amount of labeled data available. This article intends to overcome these challenges with a componental framework that is capable of robust heart disease prediction.</div></div><div><h3>Methods:</h3><div>The proposed framework is composed of three components. First, the proximity-weighted random affine shadow sampling technique is applied to mitigate class imbalance by generating synthetic samples for the minority class. Second, principal component analysis reduces feature dimensionality while preserving essential information. Third, three novel models are developed: (i) a stacking model that combines k-nearest neighbors and naïve Bayes as base learners with logistic regression as the meta-learner; (ii) an Optimized Stacking Model with Bayesian Optimization (OSM-BO) for systematic hyperparameter tuning; and (iii) an Entropy-based Active Learning Optimized Stacking Model (EAL-OSM), which uses entropy-based sampling to select the most informative samples for annotation. A paired t-test and 10-fold cross validation are applied for statistical evaluation. Local interpretable model-agnostic explanations and Shapley additive explanations are employed to ensure interpretability and support decision transparency.</div></div><div><h3>Findings:</h3><div>The stacking model improves accuracy by 3.66%, precision by 6.33%, recall by 3.57%, and Precision–Recall Area Under the Curve (PR-AUC) by 6.90%, while reducing Hamming loss by 12.50%. OSM-BO yields further improvements: 7.32% in accuracy, 7.59% in precision, 11.90% in recall, and 9.20% in PR-AUC, with a 31.25% reduction in Hamming loss. EAL-OSM achieves the best results with gains of 9.76% in accuracy, 11.39% in precision, 13.10% in recall, 11.49% in PR-AUC, and Hamming loss decreases by 37.50%.</div></div><div><h3>Conclusions:</h3><div>The proposed componental framework exhibits promising gain in classification performance, statistical robustness, and explainability, thus providing a clinically practical solution to predict heart disease.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109169"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145602790","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":"CellApop: A knowledge-guided decoupled distillation framework for label-efficient apoptotic cell segmentation and dynamic analysis in brightfield microscopy","authors":"Chi Dong ,&nbsp;Xuan Xie ,&nbsp;Shuai Yue ,&nbsp;Wei Ba ,&nbsp;Wanming Li ,&nbsp;Guangzhu Zhang ,&nbsp;Xinmeng Rong ,&nbsp;Hua Ai ,&nbsp;Jin Fang ,&nbsp;Xiran Jiang","doi":"10.1016/j.cmpb.2025.109156","DOIUrl":"10.1016/j.cmpb.2025.109156","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Conventional apoptosis detection methods primarily depend on fluorescence staining, which is labor-intensive, potentially cytotoxic, and unsuitable for real-time monitoring. To overcome these limitations, this study presents a segmentation-based deep learning (DL) framework for label-free, dynamic detection of apoptotic cells in bright-field microscopy images.</div></div><div><h3>Methods</h3><div>A comprehensive training dataset comprising 16,472 bright-field cell images was curated from four sources—three public datasets (BF-C2DL-MuSC, DIC<img>C2DH<img>HeLa, and LiveCell) and one proprietary apoptosis dataset. To achieve label-efficient learning, a Knowledge-guided Decoupled Distillation (KDD) framework was developed, wherein multiple expert models collectively guide the training of a lightweight student network, CellApop. The student model incorporates re-parameterization, depthwise separable convolutions, and an edge-aware module to improve segmentation accuracy under challenging conditions such as dense cellular overlap and indistinct boundaries. Performance was evaluated using the Dice similarity coefficient, Hausdorff Distance (HD), Intersection over Union (IoU), sensitivity, and specificity. Furthermore, CellApop was tested in an observer study for automated apoptosis-rate quantification across drug-treatment conditions, with its outputs compared against assessments by biological experts of varying experience levels.</div></div><div><h3>Results</h3><div>CellApop achieved Dice scores of 0.843 for general cells and 0.754 for apoptotic cells, while markedly reducing model complexity and inference latency. The KDD strategy decreased manual labeling requirements by approximately 80 % on the proprietary dataset. In the observer study, model<strong>-</strong>derived apoptosis rates demonstrated high concordance with ground truth and were comparable to a senior expert’s performance, surpassing those of junior and intermediate experts—particularly at early time points when apoptotic morphology was subtle.</div></div><div><h3>Conclusions</h3><div>The proposed CellApop framework delivers accurate, efficient, and label-free segmentation of apoptotic cells in bright-field microscopy, eliminating the need for fluorescent staining. Its robustness and scalability make it a promising tool for automated apoptosis quantification and drug-response assessment in routine experimental workflows.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109156"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145502571","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":"Automatic segmentation and CT-based deep learning radiomics nomogram for predicting overall survival in patients with small cell lung cancer: A multicenter cohort study","authors":"Xiaomin Zheng ,&nbsp;Kaicai Liu ,&nbsp;Mengmeng Zhao ,&nbsp;Li Tong ,&nbsp;Chang Rong ,&nbsp;Cuiping Li ,&nbsp;Shuai Li ,&nbsp;Na Shen ,&nbsp;Yali Wang ,&nbsp;Yichao Liu ,&nbsp;Xingwang Wu","doi":"10.1016/j.cmpb.2025.109161","DOIUrl":"10.1016/j.cmpb.2025.109161","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Accurate prediction of overall survival (OS) in patients with small cell lung cancer (SCLC) is crucial for personalized treatment. This study aimed to create a three-dimensional (3D) automatic segmentation model for identifying SCLC lesions in computed tomography (CT) images. Moreover, we sought to develop and validate a deep learning radiomics nomogram (DLRN) utilizing pretreatment CT images to predict OS in SCLC patients.</div></div><div><h3>Methods</h3><div>A total of 1061 SCLC patients from four hospitals in China were retrospectively enrolled. A 3D automatic segmentation model for SCLC lesions was developed using the nnU-Net neural network. Radiomics and deep learning features were extracted from the 3D tumor volume on the basis of pretreatment CT images of arterial phase (AP) and venous phase (VP). Subsequently, the radiomics score (Rad-score) and deep learning score (DL-score) were constructed. An integrated DLRN was constructed by combining the Rad-score and DL-score, followed by assessments of its discrimination, calibration, reclassification, and clinical usefulness.</div></div><div><h3>Results</h3><div>The Dice similarity coefficients of the 3D automatic segmentation model on the AP and VP image test sets were 0.878 and 0.872, respectively. The DLRN showed satisfactory predictive performance for OS and yielded concordance indices of 0.892, 0.873, and 0.872 for the internal validation cohort, external validation cohort 1, and external validation cohort 2, respectively, with good calibration in all cohorts. Furthermore, the DLRN outperformed the single model and significantly outperformed the clinical nomogram (all <em>P</em> &lt; 0.05). However, the addition of clinical factors did not improve the predictive efficacy of the DLRN on the basis of the net reclassification improvement and integrated discrimination improvement (all <em>P</em> &gt; 0.05).</div></div><div><h3>Conclusion</h3><div>The 3D automated segmentation model performed highly accurate segmentation of SCLC lesions, and a CT-based DLRN exhibited strong potential in predicting clinical outcomes for SCLC patients, potentially offering valuable insights for individualized therapy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109161"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145548573","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":"Ultrasound super-resolved hemodynamic estimation in microvessel using physics-informed neural networks and data assimilation","authors":"Meiling Liang ,&nbsp;Jiacheng Liu ,&nbsp;Hao Wang ,&nbsp;Shizhe An ,&nbsp;Chaonan Chen ,&nbsp;Hanbing Chu ,&nbsp;Mingting Zhu ,&nbsp;Xiao Su ,&nbsp;Ping Liang ,&nbsp;Yujin Zong ,&nbsp;Mingxi Wan","doi":"10.1016/j.cmpb.2025.109136","DOIUrl":"10.1016/j.cmpb.2025.109136","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Ultrasound super-resolution imaging (SRI) enables the visualization of microvascular structure and velocity, but enhancing the spatial resolution of instantaneous velocity field and simultaneously capturing pressure field remains challenging.</div></div><div><h3>Methods</h3><div>This study proposes a method combining physics-informed neural networks (PINN) with data assimilation to assist microvascular two-dimensional (2D) super-resolution velocity and pressure reconstruction in SRI. Specifically, long-time velocity vector set acquired via SRI is decomposed into short-time subsets, with vectors in each subset stacked and treated as simultaneous to enhance spatial information. These are then matched and fused with the hemodynamic simulation based on the SRI-derived structure and flow information via data assimilation, generating a new velocity field that effectively filling gaps in sparse measurements. This velocity is used to optimize the PINN encoded with the 2D Navier-Stokes equations to reconstruct the super-resolution velocity field and infer reliable pressure field.</div></div><div><h3>Results</h3><div>In vitro experiments validated the method’s performance and investigated the influence of the data amplification factor on the reconstruction accuracy, with the spatial vectors number increased by 6.48 times. Meanwhile, the super-resolution hemodynamic parameter reconstructions of rat brain microvessels and liver tumor peritumoral vessels aligned with the velocity measured by conventional SRI (rat brain vessels: radial resolution of 0.46 μm and axial resolution of 5.9 μm, liver tumor vessels: radial resolution of 5.5 μm and axial resolution of 123 μm), and the relative errors are 1.85% and 4.89%, respectively.</div></div><div><h3>Conclusions</h3><div>The proposed method reconstructs super-resolution microvascular velocity and pressure from sparse, inhomogeneous 2D SRI velocity data, showing powerful potential for aiding clinical diagnosis of microvascular diseases. (ClinicalTrials.gov (NCT06018142))</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109136"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464458","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}