BMC Cancer. 2025 Jan 29;25(1):170. doi: 10.1186/s12885-025-13432-5.

ABSTRACT

BACKGROUND: The integration of immune checkpoint inhibitors (ICIs) into routine gynecologic cancer treatment requires a thorough understanding of how to manage immune-related adverse events (irAEs) to ensure patient safety. However, reports on real-world clinical experience in the management of ICIs in gynecologic oncology are very limited. The aim of this survey was to provide a real-world overview of the experiences and the current state of irAE management of ICIs in Germany, Switzerland, and Austria.

METHODS: We designed a questionnaire consisting of 34 items focused on physicans' clinical experiences with ICIs and their management of irAEs. The survey was distributed between October 2022 and May 2023 to medical professionals with experience in the field of gynecologic oncology.

RESULTS: A total of 221 gynecologists participated in the study. Most respondents (n = 130, 59.1%) were primarily engaged in gynecologic oncology at the time of the survey, with an average of ten years of clinical experience. Individual experiences with regard to irAEs varied significantly. When asked which irAEs they had observed "frequently" or "very frequently", respondents most commonly reported thyroiditis (37.2%), followed by skin reactions (23.6%), and pneumonitis (10.6%). A total of n = 16 (7.4%) reported at least one death of a patient due to irAEs. Feeling "unconfident" or "very unconfident" about managing irAEs was reported by 35.6% (n = 78). With regard to clinical management of adverse events after discontinuation of treatment, 32.4% (n = 68) ceased to inquire about irAEs after six months.

CONCLUSION: The results of this survey provide valuable insights into physicians' real-world experiences with irAEs associated with ICI treatment. Dealing with serious immune-related and potentially life-threatening side effects has become a routine aspect of clinical practice. Many physicians, however, express a lack of sufficient familiarity with irAEs and their management. Therefore, it is essential to improve medical education, specialized oncological training, and close interdisciplinary collaboration to improve patient care.

PMID:39881252 | DOI:10.1186/s12885-025-13432-5

Insights Imaging. 2025 Jan 29;16(1):25. doi: 10.1186/s13244-024-01896-1.

ABSTRACT

PURPOSES: The presence of clinically significant prostate cancer (csPCa) is equivocal for patients with prostate imaging reporting and data system (PI-RADS) category 3. We aim to develop deep learning models for re-stratify risks in PI-RADS category 3 patients.

METHODS: This retrospective study included a bi-parametric MRI of 1567 consecutive male patients from six centers (Centers 1-6) between Jan 2015 and Dec 2020. Deep learning models with double channel attention modules based on MRI (AttenNet) for predicting PCa and csPCa were constructed separately. Each model was first pretrained using 1144 PI-RADS 1-2 and 4-5 images and then retrained using 238 PI-RADS 3 images from three training centers (centers 1-3), and tested using 185 PI-RADS 3 images from the other three testing centers (centers 4-6).

RESULTS: Our AttenNet models achieved excellent prediction performances in testing cohort of center 4-6 with the area under the receiver operating characteristic curves (AUC) of 0.795 (95% CI: [0.700, 0.891]), 0.963 (95% CI: [0.915, 1]) and 0.922 (95% CI: [0.810, 1]) in predicting PCa, and the corresponding AUCs were 0.827 (95% CI: [0.703, 0.952]) and 0.926 (95% CI: [0.846, 1]) in predicting csPCa in testing cohort of center 4 and center 5. In particular, 71.1% to 92.2% of non-csPCa patients were identified by our model in three testing cohorts, who can spare from invasive biopsy or RP procedure.

CONCLUSIONS: Our model offers a noninvasive screening clinical tool to re-stratify risks in PI-RADS 3 patients, thereby reducing unnecessary invasive biopsies and improving the effectiveness of biopsies.

CRITICAL RELEVANCE STATEMENT: The deep learning model with MRI can help to screen out csPCa in PI-RADS category 3.

KEY POINTS: AttenNet models included channel attention and soft attention modules. 71.1-92.2% of non-csPCa patients were identified by the AttenNet model. The AttenNet models can be a screen clinical tool to re-stratify risks in PI-RADS 3 patients.

PMID:39881076 | DOI:10.1186/s13244-024-01896-1

Nat Cancer. 2025 Jan 29. doi: 10.1038/s43018-024-00904-z. Online ahead of print.

ABSTRACT

The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility. Here we demonstrate NePSTA (neuropathology spatial transcriptomic analysis) for comprehensive morphological and molecular neuropathological diagnostics from single 5-µm tissue sections. NePSTA uses spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 participants with CNS malignancies and healthy donors across four medical centers, NePSTA predicts tissue histology and methylation-based subclasses with high accuracy. We demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on tissue with minimal requirements, inadequate for conventional molecular diagnostics, demonstrating the potential to enhance tumor subtype identification with implications for fast and precise diagnostic workup.

PMID:39880907 | DOI:10.1038/s43018-024-00904-z

Sci Rep. 2025 Jan 29;15(1):3640. doi: 10.1038/s41598-025-86532-8.

ABSTRACT

Air pollution in cities, especially NO2, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO2 sensors. Using a predictive graph deep model, we utilised traffic flow from London's cameras in addition to environmental and spatial factors, generating NO2 predictions from over 133 million frames. Our analysis of London's mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO2 levels, sometimes with temporal lags of up to 6 h. For instance, if trucks only drive at night, their effects on NO2 levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO2 and other pollutants.

PMID:39880905 | DOI:10.1038/s41598-025-86532-8

Commun Biol. 2025 Jan 29;8(1):141. doi: 10.1038/s42003-025-07568-0.

ABSTRACT

In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit profound differences in metabolic and transcriptional profiles that ultimately determine meiotic competence and developmental potential. Here, we developed a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live mouse oocytes. Our Fluorescence prediction and Classification on Bright-field (Fluo-Cast-Bright) pipeline achieved 91.3% accuracy in the classification of chromatin state in fixed oocytes and 85.7% accuracy in live oocytes. Importantly, transcriptome analysis following non-invasive selection revealed that meiotically competent oocytes exhibit a higher expression of transcripts associated with RNA and protein nuclear export, maternal mRNA deadenylation, histone modifications, chromatin remodeling and signaling pathways regulating microtubule dynamics during the metaphase-I to metaphase-II transition. Fluo-Cast-Bright provides fast and non-invasive selection of meiotically competent oocytes for downstream research and clinical applications.

PMID:39880880 | DOI:10.1038/s42003-025-07568-0

Stem Cell Res. 2025 Jan 15;83:103660. doi: 10.1016/j.scr.2025.103660. Online ahead of print.

ABSTRACT

NHD/PLOSL is an orphan disease characterized by progressive presenile dementia associated with recurrent fractures due to polycystic bone lesions. In this study, we generated the human induced pluripotent stem cell (hiPSC) line BIHi292-A from a 30-year-old women diagnosed with NHD/PLOSL, carrying two compound heterozygous frameshift mutations [c.313del (p.Ala105fs) and c.199del (p.His67fs)] in the TREM2 (triggering receptor expressed on myeloid cells 2) gene. BIHi292-A hiPSCs are karyotypically normal, express typical markers for the undifferentiated state and have pluripotent differentiation potential. BIHi292-A cells will provide a valuable tool for investigating pathogenic mechanisms of NHD/PLOSL and TREM2-related research questions.

PMID:39879812 | DOI:10.1016/j.scr.2025.103660

J Cyst Fibros. 2025 Jan 28:S1569-1993(25)00012-8. doi: 10.1016/j.jcf.2025.01.012. Online ahead of print.

ABSTRACT

Pancreatic insufficiency is a major complication of cystic fibrosis (CF), which traditionally has been managed with pancreatic enzyme replacement therapy in the vast majority of CF patients, even in the era of highly effective cystic fibrosis transmembrane conductance regulator modulator (CFTRm) therapy. We report on a 1.7 year old male infant with CF who was exposed to ETI both in utero and postpartum, via breast milk and oral granules. Repeated faecal elastase analyses were carried out to monitor pancreatic function closely, with normal levels at birth. Although faecal elastase values fluctuated over time, it never dropped below 100 µg/g for several subsequent measurements, while the infant continued to receive breast milk. However, at the age of 8 months PERT was initiated. ETI was introduced at 9 months of age in the form of crushed tablets as an individualised treatment, following a sustained increase in faecal elastase to >200µg/g to date. 3 weeks after starting oral ETI therapy, PERT was discontinued. With this case report we would like to show that continuous pre- and postnatal ETI exposure can maintain pancreatic function in CF for at least 1.7 years.

PMID:39880765 | DOI:10.1016/j.jcf.2025.01.012

J Cyst Fibros. 2025 Jan 28:S1569-1993(25)00011-6. doi: 10.1016/j.jcf.2025.01.011. Online ahead of print.

ABSTRACT

BACKGROUND: Males with cystic fibrosis (MwCF) face general and disease-specific sexual and reproductive health (SRH) concerns. Using concept mapping (CM), this study identified the SRH topics valued by members of the CF community.

METHODS: MwCF 18 years and older, parents and partners of MwCF, and healthcare providers participated in an online CM study. Participants individually brainstormed, sorted, and rated SRH topics important for MwCF. Using multidimensional scaling, hierarchical cluster analyses, and t tests to assess rating differences, participants interpreted results during an online meeting.

RESULTS: Eighty-nine participants (32 MwCF; 6 parents; 9 partners; and 42 providers) generated 125 statements on male SRH in CF. Seventy-eight percent completed sorting and 73% rated statements based on importance. During interpretation, 20 participants named six clusters of SRH topics: 1) Family building and fertility; 2) Psychosocial aspects of SRH; 3) Being a parent or partner as a MwCF; 4) Sexual development, function, and treatments; 5) SRH education, communication, and awareness; and 6) SRH risks, comorbidities, and aging. Participants rated family building and fertility as highest in importance (mean = 4.06±0.36 of 5). Providers issued higher importance ratings compared to MwCF and parent/partner participants. Participants identified patient-centered outcomes for each cluster and focused on enhancing SRH knowledge, decision-making, and patient-provider communication in CF care.

CONCLUSIONS: The SRH topics, importance, and patient-centered outcomes identified in this study can inform future interventions and research to optimize the comprehensive clinical care delivery for MwCF.

PMID:39880764 | DOI:10.1016/j.jcf.2025.01.011

Int J Pediatr Otorhinolaryngol. 2025 Jan 24;189:112238. doi: 10.1016/j.ijporl.2025.112238. Online ahead of print.

ABSTRACT

PURPOSE: Cystic fibrosis (CF) is the most common autosomal recessive disorder in the Caucasian population. Otolaryngological manifestations pose a significant impact on the quality of life of children with CF. The primary aim of this review is to provide a state of the art update of current literature on the otolaryngological manifestations of CF in children.

METHODS: We systematically reviewed the PubMed, Cochrane Library, Embase and Web of Science databases, for prospective studies including pediatric patients with cystic fibrosis, reporting on otolaryngological manifestations. After assessment of the risk of bias and quality of the included studies, data were extracted.

RESULTS: The search retrieved 6745 unique items after duplicate removal. After three selection rounds and quality assessment, 38 articles were ultimately retained for data extraction. The total number of participants in the studies was 1981. Most studies were prospective cohort studies (n = 28). The articles were divided into six groups (ear/speech disorders - aminoglycoside ototoxicity (n = 10); otolaryngology-related quality of life (n = 5); nasal and paranasal sinuses (n = 11); sleep disorders (n = 3); paranasal sinuses imaging (n = 5); other (n = 4)).

CONCLUSION: The most common otolaryngological manifestation of children with CF is chronic rhinosinusitis, but CF can have other otolaryngological-related pathologies such as hearing loss, middle ear problems, sleep apnea syndrome, and decreased smell and/or taste functions. We found a considerable gap in the literature if we would draw evidence-based conclusions on diagnosis and management of otolaryngological manifestations in children with CF.

PMID:39879870 | DOI:10.1016/j.ijporl.2025.112238

Redox Biol. 2025 Jan 24;80:103513. doi: 10.1016/j.redox.2025.103513. Online ahead of print.

ABSTRACT

Repeated use of nitroglycerin results in a loss of its vasodilatory efficacy which limits its clinical use for the treatment of angina pectoris. This tolerance phenomenon is a defining characteristic of all compounds classified as nitrodilators, which includes NTG as well as S-nitrosothiols and dinitrosyl iron complexes. These compounds vasodilate via activation of soluble guanylate cyclase, although they do not release requisite amounts of free nitric oxide (NO) and some do not even cross the plasma membrane. Here we demonstrate that nitrodilators cause vasodilation via mobilization of NO moiety from a nitrodilator-activated NO store (NANOS) pre-formed in the vascular smooth muscle cell, similar to the mechanism by which UV light is also known to cause vasodilation and tolerance. Intraperitoneal nitrite prevented NTG tolerance in coronary arteries of rats that received NTG transdermal patches for 4 days, and potentiated NTG- and GSNO- mediated mesenteric vasodilation in intact rats. Consistent with the incorporation of nitrite into the depletable NANOS, incubation of arteries with 15N-nitrite resulted in the accumulation of high molecular weight 15N-NO-containing compounds in arteries, and subsequent exposure to NTG, GSNO, or UV light resulted in efflux of 15N-NO species. In addition, H2O2 and metal/metalloproteins synergistically facilitated NO release from nitrite, while the oxidative stress associated with inflammation and nitrite synergistically potentiated the nitrodilator-mediated vasodilation. In conclusion, NTG mediates vasodilation via activation of a depletable intracellular store of NO that can be replenished by nitrite, thereby preventing tolerance.

PMID:39879735 | DOI:10.1016/j.redox.2025.103513

J Cheminform. 2025 Jan 29;17(1):12. doi: 10.1186/s13321-024-00945-7.

ABSTRACT

G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening. To address these issues, we introduce AiGPro, a novel multitask model designed to predict small molecule agonists (EC50) and antagonists (IC50) across the 231 human GPCRs, making it a first-in-class solution for large-scale GPCR profiling. Leveraging multi-scale context aggregation and bidirectional multi-head cross-attention mechanisms, our approach demonstrates that ensemble models may not be necessary for predicting complex GPCR states and small molecule interactions. Through extensive validation using stratified tenfold cross-validation, AiGPro achieves robust performance with Pearson's correlation coefficient of 0.91, indicating broad generalizability. This breakthrough sets a new standard in the GPCR studies, outperforming previous studies. Moreover, our first-in-class multi-tasking model can predict agonist and antagonist activities across a wide range of GPCRs, offering a comprehensive perspective on ligand bioactivity within this diverse superfamily. To facilitate easy accessibility, we have deployed a web-based platform for model access at https://aicadd.ssu.ac.kr/AiGPro . Scientific Contribution We introduce a deep learning-based multi-task model to generalize the agonist and antagonist bioactivity prediction for GPCRs accurately. The model is implemented on a user-friendly web server to facilitate rapid screening of small-molecule libraries, expediting GPCR-targeted drug discovery. Covering a diverse set of 231 GPCR targets, the platform delivers a robust, scalable solution for advancing GPCR-focused therapeutic development. The proposed framework incorporates an innovative dual-label prediction strategy, enabling the simultaneous classification of molecules as agonists, antagonists, or both. Each prediction is further accompanied by a confidence score, offering a quantitative measure of activity likelihood. This advancement moves beyond conventional models focusing solely on binding affinity, providing a more comprehensive understanding of ligand-receptor interactions. At the core of our model lies the Bi-Directional Multi-Head Cross-Attention (BMCA) module, a novel architecture that captures forward and backward contextual embeddings of protein and ligand features. By leveraging BMCA, the model effectively integrates structural and sequence-level information, ensuring a precise representation of molecular interactions. Results show that this approach is highly accurate in binding affinity predictions and consistent across diverse GPCR families. By unifying agonist and antagonist bioactivity prediction into a single model architecture, we bridge a critical gap in GPCR modeling. This enhances prediction accuracy and accelerates virtual screening workflows, offering a valuable and innovative solution for advancing GPCR-targeted drug discovery.

PMID:39881398 | DOI:10.1186/s13321-024-00945-7

BMC Oral Health. 2025 Jan 29;25(1):153. doi: 10.1186/s12903-025-05431-6.

ABSTRACT

BACKGROUND: The severity of furcation involvement (FI) directly affected tooth prognosis and influenced treatment approaches. However, assessing, diagnosing, and treating molars with FI was complicated by anatomical and morphological variations. Cone-beam computed tomography (CBCT) enhanced diagnostic accuracy for detecting FI and measuring furcation defects. Despite its advantages, the high cost and radiation dose associated with CBCT equipment limited its widespread use. The aim of this study was to evaluate the performance of the Vision Transformer (ViT) in comparison with several commonly used traditional deep learning (DL) models for classifying molars with or without FI on panoramic radiographs.

METHODS: A total of 1,568 tooth images obtained from 506 panoramic radiographs were used to construct the database and evaluate the models. This study developed and assessed a ViT model for classifying FI from panoramic radiographs, and compared its performance with traditional models, including Multi-Layer Perceptron (MLP), Visual Geometry Group (VGG)Net, and GoogLeNet.

RESULTS: Among the evaluated models, the ViT model outperformed all others, achieving the highest precision (0.98), recall (0.92), and F1 score (0.95), along with the lowest cross-entropy loss (0.27) and the highest accuracy (92%). ViT also recorded the highest area under the curve (AUC) (98%), outperforming the other models with statistically significant differences (p < 0.05), confirming its enhanced classification capability. The gradient-weighted class activation mapping (Grad-CAM) analysis on the ViT model revealed the key areas of the images that the model focused on during predictions.

CONCLUSION: DL algorithms can automatically classify FI using readily accessible panoramic images. These findings demonstrate that ViT outperforms the tested traditional models, highlighting the potential of transformer-based approaches to significantly advance image classification. This approach is also expected to reduce both the radiation dose and the financial burden on patients while simultaneously improving diagnostic precision.

PMID:39881302 | DOI:10.1186/s12903-025-05431-6

BMC Med Educ. 2025 Jan 29;25(1):143. doi: 10.1186/s12909-025-06716-8.

ABSTRACT

STUDY OBJECTIVE: Student-centered learning and unconventional teaching modalities are gaining popularity in medical education. One notable approach involves engaging students in producing creative projects to complement the learning of preclinical topics. A systematic review was conducted to characterize the impact of creative project-based learning on metacognition and knowledge gains in medical students.

METHODS: A systematic search was conducted using MEDLINE and Embase via Ovid, PubMed, CINAHL, Web of Science, Cochrane CENTRAL, and Scopus from January 1st, 1995, to July 6th, 2023. Studies using quantitative, qualitative, or mixed-methods approaches that explored the impact of creative project-based lessons on medical students' educational outcomes were included. Two investigators independently screened the titles and abstracts and extracted data from included articles. A narrative synthesis was conducted to summarize study designs and outcome measures. Content analysis was conducted to generate codes and themes. Study quality was assessed using the Mixed Methods Appraisal Tool in view of the range of study types employed.

RESULTS: The review included 17 studies published between 2010 to 2022. These studies implemented various creative project interventions such as handicraft models, drawings, and concept maps covering multiple topics, including anatomy, histopathology, and fundamental sciences. The identified themes of Enhanced Learning, Collaborative Learning, and Deep Learning led to further themes of Student Engagement, Student Disengagement, and Faculty Engagement. Collaborative learning involves students working in teams and benefitting from effective mentorship. Creative projects facilitated deep learning objectives via interdisciplinary learning and promoted new ways of perceiving concepts. Learning was enhanced through increased interactivity, high conceptual fidelity and improved knowledge retention.

CONCLUSION: Creative projects undertaken by medical students exhibit attributes that facilitate the acquisition of collaborative and deep learning objectives through self-directed learning, cognitive load modulation, and metacognitive behaviours. Faculty mentorship and group learning amongst peers facilitate these processes, although challenges such as high task demands, cognitive and emotional intensiveness, and mismatch with students; professional identities remain. Overall, students and faculty received these interventions well, thus, warranting further exploration for uses in medical curricula.

TRIAL REGISTRATION: Not applicable as this study is a systematic review.

PMID:39881268 | DOI:10.1186/s12909-025-06716-8

BMC Bioinformatics. 2025 Jan 29;26(1):33. doi: 10.1186/s12859-025-06047-x.

ABSTRACT

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.

RESULTS: We propose the SMD deep learning model, which integrates nonlinear dimensionality reduction techniques with a porous dilated attention gate component. Built upon a convolutional autoencoder and informed by the negative binomial distribution, the SMD model efficiently captures essential cell clustering features and dynamically adjusts feature weights. Comprehensive evaluation on both public datasets and proprietary osteosarcoma data highlights the SMD model's efficacy in achieving precise classifications for single-cell data clustering, showcasing its potential for advanced transcriptomic analysis.

CONCLUSION: This study underscores the potential of deep learning-specifically the SMD model-in advancing single-cell RNA sequencing data analysis. By integrating innovative computational techniques, the SMD model provides a powerful framework for unraveling cellular complexities, enhancing our understanding of biological processes, and elucidating disease mechanisms. The code is available from https://github.com/xiaoxuc/scSMD .

PMID:39881248 | DOI:10.1186/s12859-025-06047-x

BMC Cancer. 2025 Jan 29;25(1):173. doi: 10.1186/s12885-025-13582-6.

ABSTRACT

BACKGROUND: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.

MATERIALS AND METHODS: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas. Renal tumor masks manually drawn on contrast-enhanced CT images were used to develop a 3D U-Net-based deep learning model for fully automated tumor segmentation. After segmentation, the entire classification pipeline, including feature extraction and subtype classification, was conducted without any manual intervention. Both conventional radiological features (Hounsfield units, HUs) and radiomic features extracted from areas predicted by the deep learning models were used to develop an algorithm for classifying renal tumor subtypes via a random forest classifier. The performance of the segmentation model was evaluated using the Dice similarity coefficient, while the classification model was assessed based on accuracy, sensitivity, and specificity.

RESULTS: For tumors larger than 4 cm, the Dice similarity coefficient (DSC) for automated segmentation was 0.83, while for tumors smaller than 4 cm, the DSC was 0.65. The classification accuracy (ACC) for distinguishing RCC subtypes was 0.77 for tumors larger than 4 cm and 0.68 for tumors smaller than 4 cm. Additionally, the accuracy for benign versus malignant classification was 0.85.

CONCLUSIONS: Our automatic segmentation and classifier model showed promising results for renal tumor segmentation and classification.

PMID:39881216 | DOI:10.1186/s12885-025-13582-6

Sci Rep. 2025 Jan 30;15(1):3734. doi: 10.1038/s41598-025-88210-1.

ABSTRACT

Infertility has emerged as a significant global health concern. Assisted reproductive technology (ART) assists numerous infertile couples in conceiving, yet some experience repeated, unsuccessful cycles. This study aims to identify the pivotal clinical factors influencing the success of fresh embryo transfer of in vitro fertilization (IVF). We introduce a novel Non-negative Matrix Factorization (NMF)-based Ensemble algorithm (NMFE). By combining feature matrices from NMF, accelerated multiplicative updates for non-negative matrix factorization (AMU-NMF), and the generalized deep learning clustering (GDLC) algorithm. NMFE exhibits superior accuracy and reliability in analyzing the in vitro fertilization and embryo transfer (IVF-ET) dataset. The dataset comprises 2238 cycles and 85 independent clinical features, categorized into 13 categories based on feature correlation. Subsequently, the NMFE model was trained and reached convergence. Then the features of 13 categories were sequentially masked to analyze their individual effects on IVF-ET live births. The NMFE analysis highlights the significant influence of therapeutic interventions, Embryo transfer outcomes, and ovarian response assessment on live births of IVF-ET. Therapeutic interventions, including ovarian stimulation protocols, ovulation stimulation drugs, and pre-and intra-stimulation cycle acupuncture play prominent roles. However, their impacts on the IVF-ET model are reduced, suggesting a potential synergistic effect when combined. Conversely, factors like basic information, diagnosis, and obstetric history have a lesser influence. The NMFE algorithm demonstrates promising potential in assessing the influence of clinical features on live births in IVF fresh embryo transfer.

PMID:39881210 | DOI:10.1038/s41598-025-88210-1

Sci Rep. 2025 Jan 29;15(1):3648. doi: 10.1038/s41598-024-77602-4.

ABSTRACT

The opioid crisis has disproportionately affected U.S. veterans, leading the Veterans Health Administration to implement opioid prescribing guidelines. Veterans who receive care from both VA and non-VA providers-known as dual-system users-have an increased risk of Opioid Use Disorder (OUD). The interaction between dual-system use and demographic and clinical factors, however, has not been previously explored. We conducted a retrospective study of 856,299 patient instances from the Washington DC and Baltimore VA Medical Centers (2012-2019), using a deep neural network (DNN) and explainable Artificial Intelligence to examine the impact of dual-system use on OUD and how demographic and clinical factors interact with it. Of the cohort, 146,688(17%) had OUD, determined through Natural Language Processing of clinical notes and ICD-9/10 diagnoses. The DNN model, with a 78% area under the curve, confirmed that dual-system use is a risk factor for OUD, along with prior opioid use or other substance use. Interestingly, a history of other drug use interacted negatively with dual-system use regarding OUD risk. In contrast, older age was associated with a lower risk of OUD but interacted positively with dual-system use. These findings suggest that within the dual-system users, patients with certain risk profiles warrant special attention.

PMID:39881142 | DOI:10.1038/s41598-024-77602-4

Insights Imaging. 2025 Jan 29;16(1):27. doi: 10.1186/s13244-025-01904-y.

ABSTRACT

OBJECTIVES: To develop and validate radiomics and deep learning models based on contrast-enhanced MRI (CE-MRI) for differentiating dual-phenotype hepatocellular carcinoma (DPHCC) from HCC and intrahepatic cholangiocarcinoma (ICC).

METHODS: Our study consisted of 381 patients from four centers with 138 HCCs, 122 DPHCCs, and 121 ICCs (244 for training and 62 for internal tests, centers 1 and 2; 75 for external tests, centers 3 and 4). Radiomics, deep transfer learning (DTL), and fusion models based on CE-MRI were established for differential diagnosis, respectively, and their diagnostic performances were compared using the confusion matrix and area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS: The radiomics model demonstrated competent diagnostic performance, with a macro-AUC exceeding 0.9, and both accuracy and F1-score above 0.75 in the internal and external validation sets. Notably, the vgg19-combined model outperformed the radiomics and other DTL models. The fusion model based on vgg19 further improved diagnostic performance, achieving a macro-AUC of 0.990 (95% CI: 0.965-1.000), an accuracy of 0.935, and an F1-score of 0.937 in the internal test set. In the external test set, it similarly performed well, with a macro-AUC of 0.988 (95% CI: 0.964-1.000), accuracy of 0.875, and an F1-score of 0.885.

CONCLUSIONS: Both the radiomics and the DTL models were able to differentiate DPHCC from HCC and ICC before surgery. The fusion models showed better diagnostic accuracy, which has important value in clinical application.

CRITICAL RELEVANCE STATEMENT: MRI-based deep learning radiomics were able to differentiate DPHCC from HCC and ICC preoperatively, aiding clinicians in the identification and targeted treatment of these malignant hepatic tumors.

KEY POINTS: Fusion models may yield an incremental value over radiomics models in differential diagnosis. Radiomics and deep learning effectively differentiate the three types of malignant hepatic tumors. The fusion models may enhance clinical decision-making for malignant hepatic tumors.

PMID:39881111 | DOI:10.1186/s13244-025-01904-y

Insights Imaging. 2025 Jan 29;16(1):29. doi: 10.1186/s13244-025-01902-0.

ABSTRACT

OBJECTIVES: To determine whether deep learning-based reconstructions of zero-echo-time (ZTE-DL) sequences enhance image quality and bone visualization in cervical spine MRI compared to traditional zero-echo-time (ZTE) techniques, and to assess the added value of ZTE-DL sequences alongside standard cervical spine MRI for comprehensive pathology evaluation.

METHODS: In this retrospective study, 52 patients underwent cervical spine MRI using ZTE, ZTE-DL, and T2-weighted 3D sequences on a 1.5-Tesla scanner. ZTE-DL sequences were reconstructed from raw data using the AirReconDL algorithm. Three blinded readers independently evaluated image quality, artifacts, and bone delineation on a 5-point Likert scale. Cervical structures and pathologies, including soft tissue and bone components in spinal canal and neural foraminal stenosis, were analyzed. Image quality was quantitatively assessed by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).

RESULTS: Mean image quality scores were 2.0 ± 0.7 for ZTE and 3.2 ± 0.6 for ZTE-DL, with ZTE-DL exhibiting fewer artifacts and superior bone delineation. Significant differences were observed between T2-weighted and ZTE-DL sequences for evaluating intervertebral space, anterior osteophytes, spinal canal, and neural foraminal stenosis (p < 0.05), with ZTE-DL providing more accurate assessments. ZTE-DL also showed improved evaluation of the osseous components of neural foraminal stenosis compared to ZTE (p < 0.05).

CONCLUSIONS: ZTE-DL sequences offer superior image quality and bone visualization compared to ZTE sequences and enhance standard cervical spine MRI in assessing bone involvement in spinal canal and neural foraminal stenosis.

CRITICAL RELEVANCE STATEMENT: Deep learning-based reconstructions improve zero-echo-time sequences in cervical spine MRI by enhancing image quality and bone visualization. This advancement offers additional insights for assessing bone involvement in spinal canal and neural foraminal stenosis, advancing clinical radiology practice.

KEY POINTS: Conventional MRI encounters challenges with osseous structures due to low signal-to-noise ratio. Zero-echo-time (ZET) sequences offer CT-like images of the C-spine but with lower quality. Deep learning reconstructions improve image quality of zero-echo-time sequences. ZTE sequences with deep learning reconstructions refine cervical spine osseous pathology assessment. These sequences aid assessment of bone involvement in spinal and foraminal stenosis.

PMID:39881081 | DOI:10.1186/s13244-025-01902-0

Eur Radiol Exp. 2025 Jan 29;9(1):12. doi: 10.1186/s41747-025-00552-7.

ABSTRACT

BACKGROUND: Body composition scores allow for quantifying the volume and physical properties of specific tissues. However, their manual calculation is time-consuming and prone to human error. This study aims to develop and validate CompositIA, an automated, open-source pipeline for quantifying body composition scores from thoraco-abdominal computed tomography (CT) scans.

METHODS: A retrospective dataset of 205 contrast-enhanced thoraco-abdominal CT examinations was used for training, while 54 scans from a publicly available dataset were used for independent testing. Two radiology residents performed manual segmentation, identifying the centers of the L1 and L3 vertebrae and segmenting the corresponding axial slices. MultiResUNet was used to identify CT slices intersecting the L1 and L3 vertebrae, and its performance was evaluated using the mean absolute error (MAE). Two U-nets were used to segment the axial slices, with performance evaluated through the volumetric Dice similarity coefficient (vDSC). CompositIA's performance in quantifying body composition indices was assessed using mean percentage relative error (PRE), regression, and Bland-Altman analyses.

RESULTS: On the independent dataset, CompositIA achieved a MAE of about 5 mm in detecting slices intersecting the L1 and L3 vertebrae, with a MAE < 10 mm in at least 85% of cases and a vDSC greater than 0.85 in segmenting axial slices. Regression and Bland-Altman analyses demonstrated a strong linear relationship and good agreement between automated and manual scores (p values < 0.001 for all indices), with mean PREs ranging from 5.13% to 15.18%.

CONCLUSION: CompositIA facilitated the automated quantification of body composition scores, achieving high precision in independent testing.

RELEVANCE STATEMENT: CompositIA is an automated, open-source pipeline for quantifying body composition indices from CT scans, simplifying clinical assessments, and expanding their applicability.

KEY POINTS: Manual body composition assessment from CTs is time-consuming and prone to errors. CompositIA was trained on 205 CT scans and tested on 54 scans. CompositIA demonstrated mean percentage relative errors under 15% compared to manual indices. CompositIA simplifies body composition assessment through an artificial intelligence-driven and open-source pipeline.

PMID:39881078 | DOI:10.1186/s41747-025-00552-7