J Imaging Inform Med. 2025 Jan 27. doi: 10.1007/s10278-025-01412-x. Online ahead of print.

ABSTRACT

Rib pathology is uniquely difficult and time-consuming for radiologists to diagnose. AI can reduce radiologist workload and serve as a tool to improve accurate diagnosis. To date, no reviews have been performed synthesizing identification of rib fracture data on AI and its diagnostic performance on X-ray and CT scans of rib fractures and its comparison to physicians. The objectives of this study are to analyze the performance of artificial intelligence in diagnosing rib fracture on X-ray and computed tomography (CT) scan using multiple clinical studies and to compare it to that of physicians findings of rib fracture. A literature search was conducted on PubMed and Embase for articles regarding the use of artificial intelligence for the detection of rib fractures up until July 2024. AI model, number of cases, sensitivity, and comparison to physicians data was collected. A total of 29 studies, comprising 125,364 cases, were included in this review. The pooled sensitivity of AI models was 0.853. Nineteen of these studies compared their results to radiologists, orthopedic surgeons, or anesthesiologists, totalling 61 physicians. Of these 19 studies, the radiologists had a pooled sensitivity of 0.750. The sensitivity of AI in these studies by comparison was 0.840. The results suggest that artificial intelligence has a promising role in detecting rib fractures on X-ray and CT scans. In our interpretation, the performance of artificial intelligence is similar to, or better than, that of physicians, alluding to its encouraging potential in a clinical setting as it may reduce physician workload, improve reading efficiency, and lead to better patient outcomes.

PMID:39871041 | DOI:10.1007/s10278-025-01412-x

J Imaging Inform Med. 2025 Jan 27. doi: 10.1007/s10278-025-01416-7. Online ahead of print.

ABSTRACT

This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated. These diseases must be identified in the early stages to prevent eye damage progression. This paper focuses on the accurate identification and analysis of disparate eye diseases, including glaucoma, diabetic retinopathy, and cataracts, using ophthalmoscopy images. Deep learning (DL) has been widely used in image recognition for the early detection and treatment of eye diseases. In this study, ResNet50, DenseNet121, Inception-ResNetV2, and six variations of ViT are employed, and their performance in diagnosing diseases such as glaucoma, cataracts, and diabetic retinopathy is evaluated. In particular, the article uses the vision transformer model as an automated method to diagnose retinal eye diseases, highlighting the accuracy of pre-trained deep transfer learning (DTL) structures. The updated ViT#5 model with the augmented-regularized pre-trained model (AugReg ViT-L/16_224) and learning rate of 0.00002 outperforms the state-of-the-art techniques, obtaining a data-based accuracy score of 98.1% on a publicly accessible retinal ophthalmoscopy image dataset, which includes 4217 images. In most categories, the model outperforms other convolutional-based and ViT models in terms of accuracy, precision, recall, and F1 score. This research contributes significantly to medical image analysis, demonstrating the potential of AI in enhancing the precision of eye disease diagnoses and advocating for the integration of artificial intelligence in medical diagnostics.

PMID:39871038 | DOI:10.1007/s10278-025-01416-7

Eur J Nucl Med Mol Imaging. 2025 Jan 28. doi: 10.1007/s00259-025-07099-0. Online ahead of print.

ABSTRACT

PURPOSE: The study explores the role of multimodal imaging techniques, such as [18F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.

METHODS: This study conducted a retrospective analysis of 341 prostate cancer patients enrolled between 2019 and 2023, with data collected from five imaging modalities: [18F]F-PSMA-1007 PET, CT, Diffusion Weighted Imaging (DWI), T2 Weighted Imaging (T2WI), and Apparent Diffusion Coefficient (ADC). The study compared the performance of five single-modality data sets, PET/CT dual-modality fusion data, mpMRI tri-modality fusion data, and five-modality fusion data within deep learning networks, analyzing how different modalities impact the accuracy of ISUP grading prediction. To address the issue of limited data, a few-shot deep learning network was employed, enabling training and cross-validation with only a small set of labeled samples. Additionally, the results were compared with those from preoperative biopsies and clinical prediction models to further assess the reliability of the experimental findings.

RESULTS: The experimental results demonstrate that the multimodal model (combining [18F]F-PSMA-1007 PET/CT and multiparametric MRI) significantly outperforms other models in predicting ISUP grading of prostate cancer. Meanwhile, both the PET/CT dual-modality and mpMRI tri-modality models outperform the single-modality model, with comparable performance between the two multimodal models. Furthermore, the experimental data confirm that the few-shot learning network introduced in this study provides reliable predictions, even with limited data.

CONCLUSION: This study highlights the potential of applying multimodal imaging techniques (such as PET/CT and mpMRI) in predicting ISUP grading of prostate cancer. The findings suggest that this integrated approach can enhance the accuracy of prostate cancer diagnosis and contribute to more personalized treatment planning. Furthermore, incorporating few-shot learning into the model development process allows for more robust predictions despite limited data, making this approach highly valuable in clinical settings with sparse data.

PMID:39871017 | DOI:10.1007/s00259-025-07099-0

Med Phys. 2025 Jan 27. doi: 10.1002/mp.17628. Online ahead of print.

ABSTRACT

BACKGROUND: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.

PURPOSE: This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT.

METHODS AND MATERIALS: A MC dose prediction DL model called CAM-CHD U-Net for HIT was introduced, based on the GATE/Geant4 MC simulation platform. The proposed model improved upon the original CHD U-Net by adding a Channel Attention Mechanism (CAM). Two experiments were conducted, one with CHD U-Net (Experiment 1) and another with CAM-CHD U-Net (Experiment 2), and involved data from 120 head and neck cancer patients. Using patient CT images, three-dimensional energy matrices, and ray-masks as inputs, the model completed the entire MC dose prediction process within a few seconds.

RESULTS: In Experiment 2, within the Planned Target Volume (PTV) region, the average gamma passing rate (3%/3 mm) between the predicted dose and true MC dose reached 99.31%, and 96.48% across all body voxels. Experiment 2 demonstrated a 46.15% reduction in the mean absolute difference in D 5 ${D_5}$ in organs at risk compared to Experiment 1.

CONCLUSIONS: By extracting relevant parameters of radiotherapy plans, the CAM-CHD U-Net model can directly and accurately predict independent MC dose, and has a high gamma passing rate with the ground truth dose (the dose obtained after a complete MC simulation). Our workflow enables the implementation of heavy ion OART, and the predicted MCDose can be used for rapid QA of HIT.

PMID:39871016 | DOI:10.1002/mp.17628

Nat Methods. 2025 Jan 27. doi: 10.1038/s41592-024-02583-1. Online ahead of print.

ABSTRACT

Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize across experimental protocols and map neuronal activity at the laminar and subpopulation-specific levels, beyond atlas-defined regions. Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity. Validation against state-of-the-art segmentation and detection methods on unseen datasets demonstrated ACE's high generalizability and performance. Applying ACE in two distinct neurobiological contexts, we discovered subregional effects missed by existing atlas-based analyses and showcase ACE's ability to reveal localized or laminar neuronal activity brain-wide. Our open-source pipeline enables whole-brain mapping of neuronal ensembles at a high level of precision across a wide range of neuroscientific applications.

PMID:39870865 | DOI:10.1038/s41592-024-02583-1

Acta Pharmacol Sin. 2025 Jan 27. doi: 10.1038/s41401-024-01444-z. Online ahead of print.

ABSTRACT

Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods for protein structure prediction, the structural coverage of human proteome has increased substantially. This progress inspired the development of the first genome-wide small molecule targets scanning method. Our method aims to localize drug targets and detect potential off-target effects early in the drug discovery process, thereby improving the success rate of drug development. We have constructed a high-quality database of protein structures with annotated potential binding sites, covering 82% of the protein-coding genome. On the basis of this database, to enhance our search capabilities, we have integrated computational techniques, including both artificial intelligence-based and biophysical model-based methods. This integration led to the development of a target identification method called Multi-Algorithm Integrated Target Fisher (MAI-TargetFisher). MAI-TargetFisher leverages the complementary strengths of various methods while minimizing their weaknesses, enabling precise database navigation to generate a reliably ranked set of candidate targets for an active query molecule. Importantly, our work is the first comprehensive scan of protein surfaces across the entire human genome, aimed at evaluating potential small molecule binding sites on each protein. Through a series of evaluations on benchmark and a target identification task, the results demonstrate the high hit rates and good reliability of our method under the validation of wet experiments. We have also made available a freely accessible web server at https://bailab.siais.shanghaitech.edu.cn/mai-targetfisher for non-commercial use.

PMID:39870848 | DOI:10.1038/s41401-024-01444-z

Clin Transl Med. 2025 Feb;15(2):e70201. doi: 10.1002/ctm2.70201.

NO ABSTRACT

PMID:39871108 | DOI:10.1002/ctm2.70201

Nat Methods. 2025 Jan 27. doi: 10.1038/s41592-024-02571-5. Online ahead of print.

NO ABSTRACT

PMID:39870863 | DOI:10.1038/s41592-024-02571-5

Nat Commun. 2025 Jan 27;16(1):1085. doi: 10.1038/s41467-025-56164-7.

NO ABSTRACT

PMID:39870666 | DOI:10.1038/s41467-025-56164-7

J Neurosci. 2025 Jan 27:e0053212025. doi: 10.1523/JNEUROSCI.0053-21.2025. Online ahead of print.

ABSTRACT

Neurons in the cerebral cortex and hippocampus discharge synchronously in brain state-dependent manner to transfer information. Published studies have highlighted the temporal coordination of neuronal activities between the hippocampus and a neocortical area, however, how the spatial extent of neocortical activity relates to hippocampal activity remains partially unknown. We imaged mesoscopic neocortical activity while recording hippocampal local field potentials in anesthetized and unanesthetized GCaMP-expressing transgenic mice. We found that neocortical activity elevates around hippocampal sharp wave ripples (SWR). SWR-associated neocortical activities occurred predominantly in vision-related regions including visual, retrosplenial and frontal cortex. While pre-SWR neocortical activities were frequently observed in awake and natural sleeping states, post-SWR neocortical activity decreased significantly in the latter. Urethane anesthetized mice also exhibited SWR-correlated calcium elevation, but in longer time scale than observed in natural sleeping mice. During hippocampal theta oscillation states, phase-locked oscillations of calcium activity were observed throughout the entire neocortical areas. In addition, possible environmental effects on neocortico-hippocampal dynamics were assessed in this study by comparing mice reared in ISO (isolated condition) and ENR (enriched environment). In both SWR and theta oscillations, mice reared in ISO exhibited clearer brain state-dependent dynamics than those reared in ENR. Our data demonstrate that the neocortex and hippocampus exhibit heterogeneous activity patterns that characterize brain states, and postnatal experience plays a significant role in modulating these patterns.Significant Statement The hippocampus is a center for memory formation. However, the memory formed in the hippocampus is not stored forever, but gradually transferred into the cerebral cortex synchronized activities between the neocortex and hippocampus has been hypothesized (for hippocampus-independent memory see (Sutherland and Rudy, 1989)). However, spatio-temporal dynamics between hippocampus and whole neocortical areas remains partially unexplored. We measured cortical calcium activities with hippocampal electroencephalogram (EEG) simultaneously and found that the activities of widespread neocortical areas are temporally associated with hippocampal EEG. The neocortico-hippocampal dynamics is primarily regulated by animal awake/sleep state. Even if similar EEG patters were observed, temporal dynamics between the neocortex and hippocampus exhibit distinct patterns between awake and sleep period. In addition, animals' postnatal experience modulates the dynamics.

PMID:39870530 | DOI:10.1523/JNEUROSCI.0053-21.2025

Am J Cardiol. 2025 Jan 25:S0002-9149(25)00039-6. doi: 10.1016/j.amjcard.2025.01.015. Online ahead of print.

ABSTRACT

Advances in personalized medicine and Systems Biology have introduced probabilistic models and error discovery to cardiovascular care, aiding disease prevention and procedural planning. However, clinical application faces cultural, technical, and methodological hurdles. Patient autonomy remains essential, with shared decision-making (SDM) gaining importance in managing complex cardiovascular treatment options. Effective SDM relies on collaboration between providers and patients, guided by P5 Medicine principles, which combine psycho-cognitive considerations with predictive, personalized, preventive, and participatory care. Here we propose a three-step methodological proposal for implementing SDM and enhancing consent acquisition in cardiovascular care. The approach emphasizes personalized patient engagement and the need for clear, comprehensive consent processes. It identifies and addresses significant gaps in current practices, including the complexity of consent language, information dispersion, and the specific needs of vulnerable populations. Issues of Medical Responsibility and/or Liability may raise in the case of absence of consent acquisition or invalid consent due to insufficient/incorrect information. The International Guidelines on Medico-Legal Methods of Ascertainment and Evaluation Criteria are reported. In conclusion, the paper proposes practical solutions, including the use of artificial intelligence (AI) to enhance decision-making and patient counseling, and strategies to ensure that consent processes are both thorough and legally sound and respectful to the individual's autonomy.

PMID:39870321 | DOI:10.1016/j.amjcard.2025.01.015

Brain Behav Immun. 2025 Jan 25:S0889-1591(25)00025-X. doi: 10.1016/j.bbi.2025.01.016. Online ahead of print.

ABSTRACT

This study investigated the neural correlates of perceiving visual contagion cues characteristic of respiratory infections through functional magnetic resonance imaging (fMRI). Sixty-two participants (32f/ 30 m; ∼25 years on average) watched short videos depicting either contagious or non-contagious everyday situations, while their brain activation was continuously measured. We further measured the release of secretory immunoglobulin A (sIgA) in saliva to examine the first-line defensive response of the mucosal immune system. Perceiving sneezing and sick individuals compared to non-contagious individuals triggered increased activation in the anterior insula and other regions of the neuroimmune axis, that have been implicated in the somatosensory representation of the respiratory tract, and further led to increased release of sIgA. In line with predictions, this contagion cue-related activation of the insula was positively correlated with both perceived contagiousness and disgust evoked by the videos, as well as with the mucosal sIgA response. In contrast, the amygdala exhibited heightened activation to all videos featuring humans, regardless of explicit signs of contagion, indicating a nonspecific alertness to human presence. Nevertheless, amygdala activation was also correlated with the disgust ratings of each video. Collectively, these findings outline a neuroimmune mechanism for the processing of respiratory contagion cues. While the insula coordinates central and peripheral immune activation to match the perceived contagion threat, supposedly by triggering both increased sIgA release and contagion-related cognitions, the amygdala may rather act as an alerting system for social situations with a heightened transmission risk. This proactive neuroimmune response may help humans to manage contagion risks, that are difficult to avoid, by activating physiological and cognitive countermeasures in reaction to typical symptoms of respiratory infection, which prepares the organism for subsequent pathogen exposure.

PMID:39870198 | DOI:10.1016/j.bbi.2025.01.016

Drug Ther Bull. 2025 Jan 27;63(2):18. doi: 10.1136/dtb.2025.000004.

NO ABSTRACT

PMID:39870392 | DOI:10.1136/dtb.2025.000004

Drug Ther Bull. 2025 Jan 27:dtb-2025-000002. doi: 10.1136/dtb.2025.000002. Online ahead of print.

NO ABSTRACT

PMID:39870391 | DOI:10.1136/dtb.2025.000002

Blood. 2025 Jan 27:blood.2024027171. doi: 10.1182/blood.2024027171. Online ahead of print.

ABSTRACT

T-cell prolymphocytic leukemia (T-PLL) is an aggressive lymphoid malignancy with limited treatment options. To discover new treatment targets for T-PLL, we performed high-throughput drug sensitivity screening on 30 primary patient samples ex-vivo. After screening over 2'800 unique compounds, we found T-PLL to be more resistant to most drug classes, including chemotherapeutics, compared to other blood cancers. Furthermore, we discovered previously not reported vulnerabilities of T-PLL. T-PLL cells exhibited a particular sensitivity to drugs targeting autophagy (thapsigargin, bafilomycin A1), nuclear export (selinexor), and inhibitor of apoptosis proteins (IAPs) (birinapant), sensitivities that were also shared by other T-cell malignancies. Through bulk and single-cell RNA-Sequencing we found these compounds to activate the toll-like-receptor (TLR) (bafilomycin A1), p53 (selinexor), and TNF-ɑ/NFκB signaling pathways (birinapant) in T-PLL cells. Focussing on birinapant for its potential in drug repurposing, we uncovered that IAP inhibitor-induced cell death was primarily necroptotic and dependent on TNF-ɑ. Through spectral flow cytometry we confirmed the absence of cleaved caspase-3 in IAP inhibitor treated T-PLL cells and show that IAP inhibition reduces the proliferation of T-PLL cells stimulated ex-vivo, while showing only a limited effect on non-malignant T-cells. In summary, our study maps the drug sensitivity of T-PLL across a broad range of targets and identifies new therapeutic approaches for T-PLL by targeting IAPs, XPO1 and autophagy, highlighting potential candidates for drug repurposing and novel treatment strategies.

PMID:39869826 | DOI:10.1182/blood.2024027171

Acta Orthop Belg. 2024 Dec;90(4):603-611. doi: 10.52628/90.4.12600.

ABSTRACT

Patients with anterior cruciate ligament reconstruction frequently present asymmetries in the sagittal plane dynamics when performing single leg jumps but their assessment is inaccessible to health-care professionals as it requires a complex and expensive system. With the development of deep learning methods for human pose detection, kinematics can be quantified based on a video and this study aimed to investigate whether a relatively simple 2D multibody model could predict relevant dynamic biomarkers based on the kinematics using inverse dynamics. Six participants performed ten vertical and forward single leg hops while the kinematics and the ground reaction force "GRF" were captured using an optoelectronic system coupled with a force platform. The participants are modelled by a seven rigid bodies system and the sagittal plane kinematics was used as model input. Model outputs were compared to values measured by the force platform using intraclass correlation coefficients for seven outcomes: the peak vertical and antero-posterior GRFs and the impulses during the propulsion and landing phases and the loading ratio. The model reliability is either good or excellent for all outcomes (0,845 ≤ ICC ≤ 0.987). The study results are promising for deploying the developed model following a kinematics analysis based on a video. This could enable clinicians to assess their patients' jumps more effectively using video recordings made with widely available smartphones, even outside the laboratory.

PMID:39869863 | DOI:10.52628/90.4.12600

PLoS One. 2025 Jan 27;20(1):e0317376. doi: 10.1371/journal.pone.0317376. eCollection 2025.

ABSTRACT

With the increasing number of patients with Alzheimer's Disease (AD), the demand for early diagnosis and intervention is becoming increasingly urgent. The traditional detection methods for Alzheimer's disease mainly rely on clinical symptoms, biomarkers, and imaging examinations. However, these methods have limitations in the early detection of Alzheimer's disease, such as strong subjectivity in diagnostic criteria, high detection costs, and high misdiagnosis rates. To address these issues, this study proposes a deep learning model to detect Alzheimer's disease; it is called Enhanced Residual Attention Network (ERAN) that can classify medical images. By combining residual learning, attention mechanism, and soft thresholding, the feature representation ability and classification accuracy of the model have been improved. The accuracy of the model in detecting Alzheimer's disease has reached 99.36%, with a loss rate of only 0.0264. The experimental results indicate that the Enhanced Residual Attention Network has achieved excellent performance on the Alzheimer's disease test dataset, providing strong support for the early diagnosis and treatment of Alzheimer's disease.

PMID:39869613 | DOI:10.1371/journal.pone.0317376

PLoS One. 2025 Jan 27;20(1):e0316536. doi: 10.1371/journal.pone.0316536. eCollection 2025.

ABSTRACT

Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures. This paper addresses this gap by proposing a novel IDS that utilizes hybrid feature selection and deep learning classifiers to detect FDIAs in smart grids. The main objective is to enhance the accuracy and robustness of IDS in smart grids. The proposed methodology combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for hybrid feature selection, ensuring the selection of the most relevant features for detecting FDIAs. Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data's spatial and temporal features. The dataset used for evaluation, the Industrial Control System (ICS) Cyber Attack Dataset (Power System Dataset) consists of various FDIA scenarios simulated in a smart grid environment. Experimental results demonstrate that the proposed IDS framework significantly outperforms traditional methods. The hybrid feature selection effectively reduces the dimensionality of the dataset, improving computational efficiency and detection performance. The hybrid deep learning classifier performs better in key metrics, including accuracy, recall, precision, and F-measure. Precisely, the proposed approach attains higher accuracy by accurately identifying true positives and minimizing false negatives, ensuring the reliable operation of smart grids. Recall is enhanced by capturing critical features relevant to all attack types, while precision is improved by reducing false positives, leading to fewer unnecessary interventions. The F-measure balances recall and precision, indicating a robust and reliable detection system. This study presents a practical dual-hybrid IDS framework for detecting FDIAs in smart grids, addressing the limitations of existing IDS techniques. Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.

PMID:39869576 | DOI:10.1371/journal.pone.0316536

PLoS One. 2025 Jan 27;20(1):e0317999. doi: 10.1371/journal.pone.0317999. eCollection 2025.

ABSTRACT

As people's material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental improvement and contributes to the sustainable development of production and the economy. However, existing deep learning-based garbage image classification models generally suffer from low classification accuracy, insufficient robustness, and slow detection speed due to the large number of model parameters. To this end, a new garbage image classification model is proposed, with the ResNet-50 network as the core architecture. Specifically, first, a redundancy-weighted feature fusion module is proposed, enabling the model to fully leverage valuable feature information, thereby improving its performance. At the same time, the module filters out redundant information from multi-scale features, reducing the number of model parameters. Second, the standard 3×3 convolutions in ResNet-50 are replaced with depth-separable convolutions, significantly improving the model's computational efficiency while preserving the feature extraction capability of the original convolutional structure. Finally, to address the issue of class imbalance, a weighting factor is added to the Focal Loss, aiming to mitigate the negative impact of class imbalance on model performance and enhance the model's robustness. Experimental results on the TrashNet dataset show that the proposed model effectively reduces the number of parameters, improves detection speed, and achieves an accuracy of 94.13%, surpassing the vast majority of existing deep learning-based waste image classification models, demonstrating its solid practical value.

PMID:39869568 | DOI:10.1371/journal.pone.0317999

PLoS One. 2025 Jan 27;20(1):e0315893. doi: 10.1371/journal.pone.0315893. eCollection 2025.

ABSTRACT

BACKGROUND: Ras-GTPase-activating protein (GAP)-binding protein 1 (G3BP1) emerges as a pivotal oncogenic gene across various malignancies, notably including nasopharyngeal carcinoma (NPC). The use of automated image analysis tools for immunohistochemical (IHC) staining of particular proteins is highly beneficial, as it could reduce the burden on pathologists. Interestingly, there have been no prior studies that have examined G3BP1 IHC staining using digital pathology.

METHODS: Whole-slide images (WSIs) were meticulously collected and annotated by experienced pathologists. A model was intricately designed and rigorously tested to yield the quantitative data regarding staining intensity and extent. The collective output data was subjected multiplicative analysis, exploring its correlation with the prognosis.

RESULTS: The G3BP1 molecular marker scoring model was successfully established utilizing deep learning methodologies, with a calculated threshold staining scores of 1.5. Notably, patients with NPC exhibiting higher expression levels of G3BP1 proteins displayed significantly lower for overall survival rates (OS). Multivariate analysis further validated that positive expression of G3BP1 stood as an independent poorer prognostic factors, indicating a poorer prognosis for NPC patients.

CONCLUSION: Computational pathology emerges as a transformative tool capable of substantially reducing the burden on pathologists while concurrently enhancing and diagnostic sensitivity and specificity. The positive expression of G3BP1 protein serves as valuable, independent biomarker, offering predictive insights into a poor prognosis for patients with NPC.

PMID:39869565 | DOI:10.1371/journal.pone.0315893