Heart. 2025 Mar 23:heartjnl-2024-323947. doi: 10.1136/heartjnl-2024-323947. Online ahead of print.

ABSTRACT

Cardiac telemetry has evolved into a vital tool for continuous cardiac monitoring and early detection of cardiac abnormalities. In recent years, artificial intelligence (AI) has become increasingly integrated into cardiac telemetry, making a shift from traditional statistical machine learning models to more advanced deep neural networks. These modern AI models have demonstrated superior accuracy and the ability to detect complex patterns in telemetry data, enhancing real-time monitoring, predictive analytics and personalised cardiac care. In our review, we examine the current state of AI in cardiac telemetry, focusing on deep learning techniques, their clinical applications, the challenges and limitations faced by these models, and potential future directions in this promising field.

PMID:40122590 | DOI:10.1136/heartjnl-2024-323947

Neuroimage. 2025 Mar 21:121145. doi: 10.1016/j.neuroimage.2025.121145. Online ahead of print.

ABSTRACT

Magnetic resonance imaging (MRI)-derived hippocampus measurements have been associated with different cognitive domains. The knowledge of hippocampal structural deformations as we age has contributed to our understanding of the overall aging process. Different morphological hippocampal shape analysis methods have been developed, but it is unclear how their principles relate and how consistent are the published results in relation to cognition in the normal elderly in the light of the new deep-learning-based (DL) state-of-the-art modeling methods. We compared results from analyzing the hippocampal morphology using manually-generated binary masks and a Laplacian- based deformation shape analysis method, with those resulting from analyzing SynthSeg-generated hippocampal binary masks using a DL method based on the PointNet architecture, in relation to different cognitive domains. Whilst most previously reported statistically significant associations were also replicated, differences were also observed due to 1) differences in the binary masks and 2) differences in sensitivity between the methods. Differences in the template mesh, number of vertices of the template mesh, and their distribution did not impact the results.

PMID:40122476 | DOI:10.1016/j.neuroimage.2025.121145

J Neurosci Methods. 2025 Mar 21:110429. doi: 10.1016/j.jneumeth.2025.110429. Online ahead of print.

ABSTRACT

BACKGROUND: The Lateral Geniculate Nucleus (LGN) is an essential contributor to high-level visual processing despite being an early subcortical area in the visual system. Current LGN computational models focus on its basic properties, with less emphasis on its role in high-level vision.

NEW METHOD: We propose a high-level approach for encoding mouse LGN neural responses to natural scenes. This approach employs two deep neural networks (DNNs); namely VGG16 and ResNet50, as goal-driven models. We use these models as tools to better understand visual features encoded in the LGN.

RESULTS: Early layers of the DNNs represent the best LGN models. We also demonstrate that numerosity, as a high-level visual feature, is encoded, along with other visual features, in LGN neural activity. Results demonstrate that intermediate layers are better in representing numerosity compared to early layers. Early layers are better at predicting simple visual features, while intermediate layers are better at predicting more complex features. Finally, we show that an ensemble model of an early and an intermediate layer achieves high neural prediction accuracy and numerosity representation.

COMPARISON WITH EXISTING METHOD(S): Our approach emphasizes the role of analyzing the inner workings of DNNs to demonstrate the representation of a high-level feature such as numerosity in the LGN, as opposed to the common belief about the simplicity of the LGN.

CONCLUSIONS: We demonstrate that goal-driven DNNs can be used as high-level vision models of the LGN for neural prediction and as an exploration tool to better understand the role of the LGN.

PMID:40122470 | DOI:10.1016/j.jneumeth.2025.110429

J Neurosci Methods. 2025 Mar 21:110424. doi: 10.1016/j.jneumeth.2025.110424. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate brain tumor segmentation from MRI images is critical in the medical industry, directly impacts the efficacy of diagnostic and treatment plans. Accurate segmentation of tumor region can be challenging, especially when noise and abnormalities are present.

METHOD: This research provides a systematic review of automatic brain tumor segmentation techniques, with a specific focus on the design of network architectures. The review categorizes existing methods into unsupervised and supervised learning techniques, as well as machine learning and deep learning approaches within supervised techniques. Deep learning techniques are thoroughly reviewed, with a particular focus on CNN-based, U-Net-based, transfer learning-based, transformer-based, and hybrid transformer-based methods.

SCOPE AND COVERAGE: This survey encompasses a broad spectrum of automatic segmentation methodologies, from traditional machine learning approaches to advanced deep learning frameworks. It provides an in-depth comparison of performance metrics, model efficiency, and robustness across multiple datasets, particularly the BraTS dataset. The study further examines multi-modal MRI imaging and its influence on segmentation accuracy, addressing domain adaptation, class imbalance, and generalization challenges.

COMPARISON WITH EXISTING METHODS: The analysis highlights the current challenges in Computer-aided Diagnostic (CAD) systems, examining how different models and imaging sequences impact performance. Recent advancements in deep learning, especially the widespread use of U-Net architectures, have significantly enhanced medical image segmentation. This review critically evaluates these developments, focusing the iterative improvements in U-Net models that have driven progress in brain tumor segmentation. Furthermore, it explores various techniques for improving U-Net performance for medical applications, focussing on its potential for improving diagnostic and treatment planning procedures.

CONCLUSION: The efficiency of these automated segmentation approaches is rigorously evaluated using the BraTS dataset, a benchmark dataset, part of the annual Multimodal Brain Tumour Segmentation Challenge (MICCAI). This evaluation provides insights into the current state-of-the-art and identifies key areas for future research and development.

PMID:40122469 | DOI:10.1016/j.jneumeth.2025.110424

Hum Pathol. 2025 Mar 21:105762. doi: 10.1016/j.humpath.2025.105762. Online ahead of print.

ABSTRACT

Computational pathology has primarily focused on analyzing tissue slides, neglecting the valuable information contained in gross images. To bridge this gap, we proposed a novel approach leveraging the Swin Transformer architecture to develop a Swin-Transformer based Gross Features Detective Network (SGFD-network), which assist pathologists for locating diseased area in ovarian epithelial tumors based on their gross features. Our model was trained on 4129 gross images and achieved high accuracy rates of 88.9%, 86.4%, and 93.0% for benign, borderline, and carcinoma group, respectively, demonstrating strong agreement with pathologist evaluations. Notably, we trained a new classifier to distinguish between borderline tumors and those with microinvasion or microinvasive carcinoma, addressing a significant challenge in frozen section sampling. Our study was the first to propose a solution to this challenge, showcasing high accuracy rates of 85.0% and 92.2% for each group, respectively. To further elucidate the decision-making process, we employed Class Activation Mapping-grad to identify high-contribution zones and applied k-means clustering to summarize these features. The resulting clustered features can effectively complement existing knowledge of gross examination, improving the distinction between borderline tumors and those with microinvasion or microinvasive carcinoma. Our model identifies high-risk areas for microinvasion or microinvasive carcinoma, enabling pathologists to target sampling more effectively during frozen sections. Furthermore, SGFD-network requires only a single 4090 graphics card and completes a single interpretation task in 3 minutes. This study demonstrates the potential of deep learning-based analysis of gross features to aid in ovarian epithelial tumors sampling, especially in frozen section.

PMID:40122402 | DOI:10.1016/j.humpath.2025.105762

J Cardiovasc Magn Reson. 2025 Mar 21:101886. doi: 10.1016/j.jocmr.2025.101886. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with repaired tetralogy of Fallot (rTOF) are commonly followed with MRI and frequently develop right ventricular (RV) dysfunction, which can be severe enough to impact left ventricular (LV) function in some patients. In this study, we sought to characterize patterns of LV dysfunction in this patient population using Deep Learning Synthetic Strain (DLSS), a fully automated deep learning algorithm capable of measuring regional LV strain and dyssynchrony.

METHODS: We retrospectively collected cine SSFP MRI images from a multi-institutional cohort of 198 patients with rTOF and 21 healthy controls. Using DLSS, we measured LV strain and strain rate across 16 AHA segments from short-axis cine SSFP images and compared these values to controls. We then performed a clustering analysis to identify unique patterns of LV contraction, using segmental peak strain and several measures of dyssynchrony. We further characterized these patterns by assessing their relationship to traditional MRI metrics of volume and function. Lastly, we assessed their impact on subsequent progression to pulmonary valve replacement (PVR) through a multivariate analysis.

RESULTS: Overall, patients with rTOF had decreased septal radial strain, increased lateral wall radial strain, and increased dyssynchrony relative to healthy controls. Clustering of rTOF patients identified four unique patterns of LV contraction. Most notably, patients in cluster 1 (n=39) demonstrated an LV contraction pattern with paradoxical septal wall motion and severely reduced septal strain. These patients had significantly elevated RV end-diastolic volume relative to clusters 3 and 4 (153±34 vs. 127±34 and 126±31mL/m2, ANOVA p<0.01). In the multivariate analysis, this contraction pattern was the only LV metric associated with future progression to pulmonary valve replacement (HR = 2.69, p<0.005). A smaller subset of patients (cluster 2, n=29) showed reduced septal strain and LV ejection fraction despite synchronous ventricular contraction.

CONCLUSIONS: Patients with rTOF demonstrate four unique patterns of LV dysfunction. Most commonly, but not exclusively, LV dysfunction is characterized by septal wall motion abnormalities and severely reduced septal strain. Patients with this pattern of LV dysfunction had concomitant RV dysfunction and rapid progression to PVR.

PMID:40122390 | DOI:10.1016/j.jocmr.2025.101886

Magn Reson Imaging. 2025 Mar 21:110386. doi: 10.1016/j.mri.2025.110386. Online ahead of print.

ABSTRACT

Medical image registration plays a crucial role in medical imaging, with a wide range of clinical applications. In this context, brain MRI registration is commonly used in clinical practice for accurate diagnosis and treatment planning. In recent years, deep learning-based deformable registration methods have achieved remarkable results. However, existing methods have not been flexible and efficient in handling the feature relationships of anatomical structures at different levels when dealing with large deformations. To address this limitation, we propose a novel strategic integration registration network based on the pyramid structure. Our strategy mainly includes two aspects of integration: fusion of features at different scales, and integration of different neural network structures. Specifically, we design a CNN encoder and a Transformer decoder to efficiently extract and enhance both global and local features. Moreover, to overcome the error accumulation issue inherent in pyramid structures, we introduce progressive optimization iterations at the lowest scale for deformation field generation. This approach more efficiently handles the spatial relationships of images while improving accuracy. We conduct extensive evaluations across multiple brain MRI datasets, and experimental results show that our method outperforms other deep learning-based methods in terms of registration accuracy and robustness.

PMID:40122188 | DOI:10.1016/j.mri.2025.110386

EBioMedicine. 2025 Mar 22;114:105663. doi: 10.1016/j.ebiom.2025.105663. Online ahead of print.

ABSTRACT

BACKGROUND: We aim to predict outcomes of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC), a subtype of head and neck cancer characterized with improved clinical outcome and better response to therapy. Pathology and radiology focused AI-based prognostic models have been independently developed for OPSCC, but their integration incorporating both primary tumour (PT) and metastatic cervical lymph node (LN) remains unexamined.

METHODS: We investigate the prognostic value of an AI approach termed the swintransformer-based multimodal and multi-region data fusion framework (SMuRF). SMuRF integrates features from CT corresponding to the PT and LN, as well as whole slide pathology images from the PT as a predictor of survival and tumour grade in HPV-associated OPSCC. SMuRF employs cross-modality and cross-region window based multi-head self-attention mechanisms to capture interactions between features across tumour habitats and image scales.

FINDINGS: Developed and tested on a cohort of 277 patients with OPSCC with matched radiology and pathology images, SMuRF demonstrated strong performance (C-index = 0.81 for DFS prediction and AUC = 0.75 for tumour grade classification) and emerged as an independent prognostic biomarker for DFS (hazard ratio [HR] = 17, 95% confidence interval [CI], 4.9-58, p < 0.0001) and tumour grade (odds ratio [OR] = 3.7, 95% CI, 1.4-10.5, p = 0.01) controlling for other clinical variables (i.e., T-, N-stage, age, smoking, sex and treatment modalities). Importantly, SMuRF outperformed unimodal models derived from radiology or pathology alone.

INTERPRETATION: Our findings underscore the potential of multimodal deep learning in accurately stratifying OPSCC risk, informing tailored treatment strategies and potentially refining existing treatment algorithms.

FUNDING: The National Institutes of Health, the U.S. Department of Veterans Affairs and National Institute of Biomedical Imaging and Bioengineering.

PMID:40121941 | DOI:10.1016/j.ebiom.2025.105663

Comput Med Imaging Graph. 2025 Mar 21;123:102532. doi: 10.1016/j.compmedimag.2025.102532. Online ahead of print.

ABSTRACT

Multimodality is often necessary for improving object segmentation tasks, especially in the case of multilabel tasks, such as tumor segmentation, which is crucial for clinical diagnosis and treatment planning. However, a major challenge in utilizing multimodality with deep learning remains: the limited availability of annotated training data, primarily due to the time-consuming acquisition process and the necessity for expert annotations. Although deep learning has significantly advanced many tasks in medical imaging, conventional augmentation techniques are often insufficient due to the inherent complexity of volumetric medical data. To address this problem, we propose an innovative slice-based latent diffusion architecture for the generation of 3D multi-modal images and their corresponding multi-label masks. Our approach enables the simultaneous generation of the image and mask in a slice-by-slice fashion, leveraging a positional encoding and a Latent Aggregation module to maintain spatial coherence and capture slice sequentiality. This method effectively reduces the computational complexity and memory demands typically associated with diffusion models. Additionally, we condition our architecture on tumor characteristics to generate a diverse array of tumor variations and enhance texture using a refining module that acts like a super-resolution mechanism, mitigating the inherent blurriness caused by data scarcity in the autoencoder. We evaluate the effectiveness of our synthesized volumes using the BRATS2021 dataset to segment the tumor with three tissue labels and compare them with other state-of-the-art diffusion models through a downstream segmentation task, demonstrating the superior performance and efficiency of our method. While our primary application is tumor segmentation, this method can be readily adapted to other modalities. Code is available here : https://github.com/Arksyd96/multi-modal-mri-and-mask-synthesis-with-conditional-slice-based-ldm.

PMID:40121926 | DOI:10.1016/j.compmedimag.2025.102532

Respirology. 2025 Mar 23. doi: 10.1111/resp.70030. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Real-world data on lung function course of patients with progressive pulmonary fibrosis (PPF) treated with anti-fibrotic medication are limited. We evaluated forced vital capacity (FVC) decline in patients with PPF and idiopathic pulmonary fibrosis (IPF) who started anti-fibrotic treatment.

METHODS: This was a nationwide multi-centre registry study in 16 hospitals throughout the Netherlands. Patients treated with anti-fibrotic medication, with at least two in-hospital pulmonary function tests before and after the initiation of anti-fibrotic treatment, were included. Linear mixed-effects modelling was used to analyse lung function trajectories 1 year before and after the start of anti-fibrotic treatment.

RESULTS: Data from 538 patients (n = 142 with PPF, n = 396 with IPF) were analysed. In PPF, the mean annualised FVC decline was 412 mL (95% confidence interval [CI]: 308-517 mL) before the initiation of anti-fibrotic treatment, and 18 mL (95% CI: 9-124 mL) in the first year after. The corresponding declines for IPF were 158 mL (95% CI: 78-239 mL) and 38 mL (95% CI: 24-101 mL). In both groups, treatment significantly slowed down FVC decline, although the change was larger in the PPF group (p = 0.0006). In the first year after treatment initiation, 28.0% of patients with PPF and 27.4% with IPF had disease progression.

CONCLUSION: The FVC decline significantly slowed after the initiation of treatment for both IPF and PPF. Nevertheless, a significant proportion of patients exhibited disease progression, despite the start of anti-fibrotic treatment. Early identification of these patients is crucial for treatment adaptations and inclusion in clinical trials.

PMID:40122143 | DOI:10.1111/resp.70030

Ageing Res Rev. 2025 Mar 21:102737. doi: 10.1016/j.arr.2025.102737. Online ahead of print.

ABSTRACT

Partial or transient cellular reprogramming is defined by the limited induction of pluripotency factors without full dedifferentiation of cells to a pluripotent state. Comparing in vitro and in vivo mouse studies, and in vitro studies in humans, supported by visualizations of data interconnections, we show consistent patterns in how such reprogramming modulates key biological processes. Generally, partial reprogramming drives dynamic chromatin remodelling, involving histone modifications that regulate accessibility and facilitate pluripotency gene activation while silencing somatic identity. These changes are accompanied by modifications in stress response programs, such as inflammation, autophagy, and cellular senescence, as well as improved mitochondrial activity and dysregulation of extracellular matrix pathways. We also underscore the challenges in evaluating complex processes like aging and cellular senescence, given the variability in biomarkers used across studies. Overall, we highlight biological processes consistently influenced by reprogramming while noting that some effects are context-dependent, varying according to cell type, species, sex, recovery time, and the reprogramming method employed. These insights inform future research and potential therapeutic applications in aging and regenerative medicine.

PMID:40122394 | DOI:10.1016/j.arr.2025.102737

J Nutr Health Aging. 2025 Mar 22;29(4):100546. doi: 10.1016/j.jnha.2025.100546. Online ahead of print.

NO ABSTRACT

PMID:40121957 | DOI:10.1016/j.jnha.2025.100546

Cancer Genet. 2025 Mar 22;294-295:32-41. doi: 10.1016/j.cancergen.2025.03.005. Online ahead of print.

ABSTRACT

PURPOSE: Few effective immune prognostic models based on the tumor immune microenvironment (TIME) for glioblastoma have been reported. Therefore, this study aimed to construct an immune prognostic model for glioblastoma by analyzing enriched biological processes and pathways in tumors.

METHODS: A comprehensive single-sample gene set enrichment analysis (ssGSEA) of gene sets from the Molecular Signatures Database was performed using TCGA RNA sequencing data (141 glioblastoma cases). After evaluating gene sets associated with prognosis using univariable Cox regression, gene sets related to biological processes and tumor immunity in gliomas were extracted. Finally, the least absolute shrinkage and selection operator Cox regression refined the gene sets and a nomogram was constructed. The model was validated using CGGA (183 cases) and Aichi Cancer Center (42 cases) datasets.

RESULTS: The immune prognostic model consisted of three gene sets related to biological processes (sphingolipids, steroid hormones, and intermediate filaments) and one related to tumor immunity (immunosuppressive chemokine pathways involving tumor-associated microglia and macrophages). Kaplan-Meier curves for the training (TCGA) and validation (CGGA) cohorts showed significantly worse overall survival in the high-risk group compared to the low-risk group (p < 0.001 and p = 0.04, respectively). Furthermore, in silico cytometry revealed a significant increase in macrophages with immunosuppressive properties and T cells with effector functions in the high-risk group (p < 0.01) across all cohorts.

CONCLUSION: Construction of an immune prognostic model based on the TIME assessment using ssGSEA could potentially provide valuable insights into the prognosis and immune profiles of patients with glioblastoma and guide treatment strategies.

PMID:40121844 | DOI:10.1016/j.cancergen.2025.03.005

Pharmacoepidemiol Drug Saf. 2025 Mar;34(3):e70135. doi: 10.1002/pds.70135.

ABSTRACT

BACKGROUND: Direct Healthcare Professional Communications (DHPCs) are an important risk minimisation measure. Their effect has been shown to be variable and has been measured using different outcomes and study populations. Depending on the content of the message, the optimal outcome to measure a direct effect of the DHPC can differ. This systematic review investigates whether the effects of DHPCs differ according to the use of proximal outcomes and the inclusion of the targeted population.

METHODS: EMBASE and MEDLINE were searched for European DHPC effectiveness studies performed up to April 6, 2022, evaluating the impact of DHPCs issued from 2008. Outcomes and their impact were extracted, together with a classification of the message. The outcomes were categorised as knowledge/awareness, self-reported behaviour (prescribing/monitoring), prescribing of medication (including dosage changes), monitoring, or adverse events/other health outcomes, including hospitalisation. The outcomes closest to the message of the DHPC were defined as proximal. Outcomes were coded 1 when effective and 0 if not. If multiple outcomes were reported in a study, a composite outcome was created ranging from 0 to 1. Chi-square or Fisher exact tests were performed.

RESULTS: From 7063 (scientific) publications identified in our literature search, 60 publications evaluating 31 different DHPCs were selected for our review. As publications could study multiple messages with an outcome, from the 60 scientific publications, 103 outcomes were generated for the messages, of which 30 had a high impact on the composite outcome, with the proportion of analyses with a significant association between 0.75 and 1. When taking the target population into account, some messages were studied in more than one population, resulting in 115 outcomes, of which 33 had a high impact, that is, a composite outcome between 0.75 and 1.

CONCLUSION: Neither the use of proximal outcomes nor the restriction of the analysis to the targeted population significantly influenced the impact observed of the DHPC. These results stress the need for improving drug safety communication.

PMID:40122533 | DOI:10.1002/pds.70135

Orphanet J Rare Dis. 2025 Mar 22;20(1):141. doi: 10.1186/s13023-025-03619-1.

ABSTRACT

BACKGROUND: Chronic singultus lasting longer than one month is a rare disease. Due to its low prevalence, generating evidence about it is difficult. Patients with chronic diseases struggle with considerable restrictions in their quality of life. Chronic hiccups can lead to problems such as insomnia, anorexia, fatigue, exhaustion, weight loss, and depression. The aim of this study was to gain a better understanding of the quality of life of patients with chronic singultus and their experiences in contact with the healthcare system and with the general population.

METHODS: The data were collected using semi-structured interviews. The data analysis was carried out using qualitative structuring content analysis according to Kuckartz and Rädiker. Reliability was ensured by joint interprofessional evaluation of the interviews by experts, considering different perspectives.

RESULTS: Interviews from 20 patients with chronic singultus were analyzed. Analysis yielded 43 categories that could be assigned to five main topics. The disease burden of the patients was high. In addition to physical symptoms such as concomitant gastroenterological symptoms, shortness of breath, and fatigue, psychosocial consequences such as shame, social withdrawal, anxiety, depression, and even suicidality led to reduced quality of life.

CONCLUSIONS: Ignorance and helplessness among healthcare stakeholders in the case of chronic singultus could lead to a marginalization of the disease and patients. Referring patients to a center with the appropriate expertise can help to avoid underuse, overuse, or misuse of healthcare. Therefore, the awareness of the disease among stakeholders must raise.

PMID:40121512 | DOI:10.1186/s13023-025-03619-1

BMC Bioinformatics. 2025 Mar 22;26(1):87. doi: 10.1186/s12859-025-06105-4.

ABSTRACT

BACKGROUND: Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs-ultimately hindering the development of effective prioritisation tools.

RESULTS: In this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets.

CONCLUSIONS: PhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.

PMID:40121479 | DOI:10.1186/s12859-025-06105-4

Eur J Clin Nutr. 2025 Mar 22. doi: 10.1038/s41430-025-01589-y. Online ahead of print.

ABSTRACT

Elexacaftor/Tezacaftor/Ivacaftor (ETI) has led to improved lung function, life expectancy, and body mass index for people with Cystic Fibrosis (CF). The aim of this systematic review was to evaluate the impact that ETI has had on body composition in people with CF. A systematic review was performed using MEDLINE, EMBASE, CINAHL, and the Cochrane Central Register of Controlled Trials. Quality assessment using the Joanna Briggs Institute critical appraisal tools were performed. Results were summarised narratively. Five observational cohort studies involving a total of 185 participants were reviewed. Three studies showed an increase in fat mass (7.0-8.6 kg, 13.2-14.3 kg, and 13.4-15.5 kg). Two studies reported an increase in fat-free mass (49.4-50.1 kg, 52.5-55 kg), while one reported a decrease (50.5-48.9 kg). Two studies found an increase in fat mass index (4.1-6.3 kgm/2 and 4.7-5.4 kg/m2) and fat-free mass index (17.4-17.7 kg/m2 and 18.1-18.8 kg/m2). Two studies observed an increase in percentage body fat mass (12.1-15.4% and 23.1-27.6%). Four studies were classified as low quality, while one was considered medium quality. This review suggest that commencing ETI results in changes in body composition. Firm conclusions about the type and distribution of change in body composition cannot be made due to limited studies, high heterogeneity, and methodical weaknesses. It highlights the necessity for higher quality and longer-term studies to explore the impact that ETI is having on body composition.

PMID:40121317 | DOI:10.1038/s41430-025-01589-y

Insights Imaging. 2025 Mar 23;16(1):68. doi: 10.1186/s13244-025-01937-3.

ABSTRACT

OBJECTIVES: A novel risk stratification model based on Lung-RADS® v2022 and CT features was constructed and validated for predicting invasive pure ground-glass nodules (pGGNs) in China.

METHODS: Five hundred and twenty-six patients with 572 pulmonary GGNs were prospectively enrolled and divided into training (n = 169) and validation (n = 403) sets. Utilising the Lung-RADS® v2022 framework and the types of GGN-vessel relationships (GVR), a complementary Lung-RADS® v2022 was established, and the pGGNs were reclassified from categories 2, 3 and 4x of Lung-RADS® v2022 into 2, 3, 4a, 4b, and 4x of cLung-RADS® v2022. The cutoff value of invasive pGGNs was defined as the cLung-RADS® v2022 4a-4x. Evaluation metrics like recall rate, precision, F1 score, accuracy, Matthews correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUC) were employed to assess the utility of the cLung-RADS® v2022.

RESULTS: In the training set, compared with the Lung-RADS 1.0, the AUC of Lung-RADS® v2022 were decreased from 0.543 to 0.511 (p-value = 0.002), and compared to Lung-RADS 1.0 and Lung-RADS® v2022, the cLung-RADS® v2022 model exhibited the highest recall rate (94.9% vs 6.5%, 2.2%), MCC value (60.2% vs 5.4%, 6.3%), F1 score (92.5% vs 12.1%, 4.3%), accuracy (87.6% vs 23.1%, 19.5%), and AUC (0.718 vs 0.543, 0.511; p-value = 0.014, 0.0016) in diagnosing the invasiveness of pGGNs, and the similar performance was observed in the validation set.

CONCLUSION: The cLung-RADS® v2022 can effectively predict the invasiveness of pGGNs in real-world scenarios.

CRITICAL RELEVANCE STATEMENT: A complementary Lung-RADS® v2022 based on the Lung-RADS® v2022 and CT features can effectively predict the invasiveness of pulmonary pure ground-glass nodules and is applicable in clinical practice.

TRIAL REGISTRATION: Establishment and application of a multi-scale low-dose CT Lung cancer screening model based on modified lung-RADS1.1 and deep learning technology, 2022-KY-0137. Registered 24 January 2022. https://www.medicalresearch.org.cn/search/research/researchView?id=a97e67d8-1ee6-40fb-aab1-e6238dbd8f29 .

KEY POINTS: Lung-RADS® v2022 delayed lung cancer diagnosis for nodules appearing as pGGNs. Lung-RADS® v2022 showed lower accuracy and AUC than Lung-RADS 1.0. cLung-RADS® v2022 model effectively predicts the invasiveness of pulmonary pGGNs.

PMID:40121609 | DOI:10.1186/s13244-025-01937-3

BMC Musculoskelet Disord. 2025 Mar 22;26(1):287. doi: 10.1186/s12891-025-08538-7.

ABSTRACT

OBJECTIVES: Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) present similar symptoms in the early stage, complicating their differentiation. This study aims to develop a classification model using radiomic features from MRI T2-weighted Dixon sequences to increase the accuracy of distinguishing DMD and BMD in the early disease stage.

METHODS: We retrospectively analysed MRI data from 62 patients aged 36-60 months with muscular dystrophy, including 41 with DMD and 21 with BMD. Radiomic features were extracted from in-phase, opposed-phase, water, fat, and postprocessed fat fraction images. We employed a deep learning segmentation method to segment regions of interest automatically. Feature selection included the Mann‒Whitney U test for identifying significant features, Pearson correlation analysis to remove collinear features, and the LASSO regression method to select features with nonzero coefficients. These selected features were then used in various machine learning algorithms to construct the classification model, and their diagnostic performance was compared.

RESULTS: Our proposed radiomic and machine learning methods effectively distinguished early DMD and BMD. The machine learning models significantly outperformed the radiologists in terms of accuracy (81.2-90.6% compared with 69.4%), specificity (71.0-86.0% compared with 19.0%), and F1 score (85.2-92.6% compared with 80.5%), while maintaining relatively high sensitivity (85.6-95.0% compared with 95.1%).

CONCLUSION: Radiomics based on Dixon sequences combined with machine learning methods can effectively distinguish between DMD and BMD in the early stages, providing a new and effective tool for the early diagnosis of these muscular dystrophies.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40121488 | DOI:10.1186/s12891-025-08538-7

BMC Bioinformatics. 2025 Mar 22;26(1):88. doi: 10.1186/s12859-025-06101-8.

ABSTRACT

Binding proteins play a crucial role in biological systems by selectively interacting with specific molecules, such as DNA, RNA, or peptides, to regulate various cellular processes. Their ability to recognize and bind target molecules with high specificity makes them essential for signal transduction, transport, and enzymatic activity. Traditional experimental methods for identifying protein-binding peptides are costly and time-consuming. Current sequence-based approaches often struggle with accuracy, focusing too narrowly on proximal sequence features and ignoring structural data. This study presents Deep-ProBind, a powerful prediction model designed to classify protein binding sites by integrating sequence and structural information. The proposed model employs a transformer and evolutionary-based attention mechanism, i.e., Bidirectional Encoder Representations from Transformers (BERT) and Pseudo position specific scoring matrix -Discrete Wavelet Transform (PsePSSM -DWT) approach to encode peptides. The SHapley Additive exPlanations (SHAP) algorithm selects the optimal hybrid features, and a Deep Neural Network (DNN) is then used as the classification algorithm to predict protein-binding peptides. The performance of the proposed model was evaluated in comparison with traditional Machine Learning (ML) algorithms and existing models. Experimental results demonstrate that Deep-ProBind achieved 92.67% accuracy with tenfold cross-validation on benchmark datasets and 93.62% accuracy on independent samples. The Deep-ProBind outperforms existing models by 3.57% on training data and 1.52% on independent tests. These results demonstrate Deep-ProBind's reliability and effectiveness, making it a valuable tool for researchers and a potential resource in pharmacological studies, where peptide binding plays a critical role in therapeutic development.

PMID:40121399 | DOI:10.1186/s12859-025-06101-8