J Neurosurg. 2025 Mar 7:1-9. doi: 10.3171/2024.10.JNS241060. Online ahead of print.

ABSTRACT

OBJECTIVE: Preoperative workup of trigeminal neuralgia (TN) consists of identification of neurovascular features on MRI. In this study, the authors apply and evaluate the performance of deep learning models for segmentation of the trigeminal nerve and surrounding vasculature to quantify anatomical features of the nerve and vessels.

METHODS: Six U-Net-based neural networks, each with a different encoder backbone, were trained to label constructive interference in steady-state MRI voxels as nerve, vasculature, or background. A retrospective dataset of 50 TN patients at the authors' institution who underwent preoperative high-resolution MRI in 2022 was utilized to train and test the models. Performance was measured by the Dice coefficient and intersection over union (IoU) metrics. Anatomical characteristics, such as surface area of neurovascular contact and distance to the contact point, were computed and compared between the predicted and ground truth segmentations.

RESULTS: Of the evaluated models, the best performing was U-Net with an SE-ResNet50 backbone (Dice score = 0.775 ± 0.015, IoU score = 0.681 ± 0.015). When the SE-ResNet50 backbone was used, the average surface area of neurovascular contact in the testing dataset was 6.90 mm2, which was not significantly different from the surface area calculated from manual segmentation (p = 0.83). The average calculated distance from the brainstem to the contact point was 4.34 mm, which was also not significantly different from manual segmentation (p = 0.29).

CONCLUSIONS: U-Net-based neural networks perform well for segmenting trigeminal nerve and vessels from preoperative MRI volumes. This technology enables the development of quantitative and objective metrics for radiographic evaluation of TN.

PMID:40053933 | DOI:10.3171/2024.10.JNS241060

Mol Pharm. 2025 Mar 7. doi: 10.1021/acs.molpharmaceut.4c01086. Online ahead of print.

ABSTRACT

The optimization of absorption, distribution, metabolism, and excretion (ADME) profiles of compounds is critical to the drug discovery process. As such, machine learning (ML) models for ADME are widely used for prioritizing the design and synthesis of compounds. The effectiveness of ML models for ADME depends on the availability of high-quality experimental data for a diverse set of compounds that is relevant to the emerging chemical space being explored by the drug discovery teams. To that end, ADME data sets from Genentech and Roche were combined to evaluate the impact of expanding the chemical space on the performance of ML models, a first experiment of its kind for large-scale, historical ADME data sets. The combined ADME data set consisted of over 1 million individual measurements distributed across 11 assay end points. We utilized a multitask (MT) neural network architecture that enables the modeling of multiple end points simultaneously and thereby exploits information transfer between interconnected ADME end points. Both single- and cross-site MT models were trained and compared against single-site, single-task baseline models. Given the differences in assay protocols across the two sites, the data for corresponding end points across sites were modeled as separate tasks. Models were evaluated against test sets representing varying degrees of extrapolation difficulty, including cluster-based, temporal, and external test sets. We found that cross-site MT models appeared to provide a greater generalization capacity compared to single-site models. The performance improvement of the cross-site MT models was more pronounced for the relatively "distant" external and temporal test sets, suggesting an expanded applicability domain. The data exchange exercise described here demonstrates the value of expanding the learning from ADME data from multiple sources without the need to aggregate such data when the experimental methods are disparate.

PMID:40053846 | DOI:10.1021/acs.molpharmaceut.4c01086

JMIR Med Inform. 2025 Mar 4;13:e68863. doi: 10.2196/68863.

ABSTRACT

BACKGROUND: Patients' oral expressions serve as valuable sources of clinical information to improve pharmacotherapy. Natural language processing (NLP) is a useful approach for analyzing unstructured text data, such as patient narratives. However, few studies have focused on using NLP for narratives in the Japanese language.

OBJECTIVE: We aimed to develop a high-performance NLP system for extracting clinical information from patient narratives by examining the performance progression with a gradual increase in the amount of training data.

METHODS: We used subjective texts from the pharmaceutical care records of Keio University Hospital from April 1, 2018, to March 31, 2019, comprising 12,004 records from 6559 cases. After preprocessing, we annotated diseases and symptoms within the texts. We then trained and evaluated a deep learning model (bidirectional encoder representations from transformers combined with a conditional random field [BERT-CRF]) through 10-fold cross-validation. The annotated data were divided into 10 subsets, and the amount of training data was progressively increased over 10 steps. We also analyzed the causes of errors. Finally, we applied the developed system to the analysis of case report texts to evaluate its usability for texts from other sources.

RESULTS: The F1-score of the system improved from 0.67 to 0.82 as the amount of training data increased from 1200 to 12,004 records. The F1-score reached 0.78 with 3600 records and was largely similar thereafter. As performance improved, errors from incorrect extractions decreased significantly, which resulted in an increase in precision. For case reports, the F1-score also increased from 0.34 to 0.41 as the training dataset expanded from 1200 to 12,004 records. Performance was lower for extracting symptoms from case report texts compared with pharmaceutical care records, suggesting that this system is more specialized for analyzing subjective data from pharmaceutical care records.

CONCLUSIONS: We successfully developed a high-performance system specialized in analyzing subjective data from pharmaceutical care records by training a large dataset, with near-complete saturation of system performance with about 3600 training records. This system will be useful for monitoring symptoms, offering benefits for both clinical practice and research.

PMID:40053805 | DOI:10.2196/68863

J Med Internet Res. 2025 Mar 7;27:e69068. doi: 10.2196/69068.

ABSTRACT

BACKGROUND: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnostic pathway, so various artificial intelligence (AI)-based methods have been developed to address this issue.

OBJECTIVE: We aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities.

METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines, we conducted a systematic review to synthesize current knowledge on this topic. Our search across 3 major databases (Scopus, PubMed, Association for Computing Machinery [ACM] Digital Library) yielded 1146 records, of which we included 152 (13.3%) studies in our analysis. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies version 2) was performed for the risk-of-bias assessment of all included studies.

RESULTS: Radiographic biomarkers (n=129, 84.9%) and deep learning (DL; n=122, 80.3%) approaches were predominantly used, with convolutional neural networks (CNNs) using Visual Geometry Group (VGG)-16 (n=37, 24.3%), ResNet-50 (n=33, 21.7%), and DenseNet-121 (n=19, 12.5%) architectures being the most common DL approach. The majority of studies focused on model development (n=143, 94.1%) and used a single modality approach (n=141, 92.8%). AI methods demonstrated good performance in all studies: mean accuracy=91.93% (SD 8.10%, 95% CI 90.52%-93.33%; median 93.59%, IQR 88.33%-98.32%), mean area under the curve (AUC)=93.48% (SD 7.51%, 95% CI 91.90%-95.06%; median 95.28%, IQR 91%-99%), mean sensitivity=92.77% (SD 7.48%, 95% CI 91.38%-94.15%; median 94.05% IQR 89%-98.87%), and mean specificity=92.39% (SD 9.4%, 95% CI 90.30%-94.49%; median 95.38%, IQR 89.42%-99.19%). AI performance across different biomarker types showed mean accuracies of 92.45% (SD 7.83%), 89.03% (SD 8.49%), and 84.21% (SD 0%); mean AUCs of 94.47% (SD 7.32%), 88.45% (SD 8.33%), and 88.61% (SD 5.9%); mean sensitivities of 93.8% (SD 6.27%), 88.41% (SD 10.24%), and 93% (SD 0%); and mean specificities of 94.2% (SD 6.63%), 85.89% (SD 14.66%), and 95% (SD 0%) for radiographic, molecular/biochemical, and physiological types, respectively. AI performance across various reference standards showed mean accuracies of 91.44% (SD 7.3%), 93.16% (SD 6.44%), and 88.98% (SD 9.77%); mean AUCs of 90.95% (SD 7.58%), 94.89% (SD 5.18%), and 92.61% (SD 6.01%); mean sensitivities of 91.76% (SD 7.02%), 93.73% (SD 6.67%), and 91.34% (SD 7.71%); and mean specificities of 86.56% (SD 12.8%), 93.69% (SD 8.45%), and 92.7% (SD 6.54%) for bacteriological, human reader, and combined reference standards, respectively. The transfer learning (TL) approach showed increasing popularity (n=89, 58.6%). Notably, only 1 (0.7%) study conducted domain-shift analysis for TB detection.

CONCLUSIONS: Findings from this review underscore the considerable promise of AI-based methods in the realm of TB detection. Future research endeavors should prioritize conducting domain-shift analyses to better simulate real-world scenarios in TB detection.

TRIAL REGISTRATION: PROSPERO CRD42023453611; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023453611.

PMID:40053773 | DOI:10.2196/69068

J Med Internet Res. 2025 Mar 5;27:e54543. doi: 10.2196/54543.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) ranks among the leading causes of global mortality, and COVID-19 has intensified its challenges. Beyond the evident physical effects, the long-term psychological effects of COVID-19 are not fully understood.

OBJECTIVE: This study aims to unveil the long-term psychological trends and patterns in populations with COPD throughout the COVID-19 pandemic and beyond via large-scale Twitter mining.

METHODS: A 2-stage deep learning framework was designed in this study. The first stage involved a data retrieval procedure to identify COPD and non-COPD users and to collect their daily tweets. In the second stage, a data mining procedure leveraged various deep learning algorithms to extract demographic characteristics, hashtags, topics, and sentiments from the collected tweets. Based on these data, multiple analytical methods, namely, odds ratio (OR), difference-in-difference, and emotion pattern methods, were used to examine the psychological effects.

RESULTS: A cohort of 15,347 COPD users was identified from the data that we collected in the Twitter database, comprising over 2.5 billion tweets, spanning from January 2020 to June 2023. The attentiveness toward COPD was significantly affected by gender, age, and occupation; it was lower in females (OR 0.91, 95% CI 0.87-0.94; P<.001) than in males, higher in adults aged 40 years and older (OR 7.23, 95% CI 6.95-7.52; P<.001) than in those younger than 40 years, and higher in individuals with lower socioeconomic status (OR 1.66, 95% CI 1.60-1.72; P<.001) than in those with higher socioeconomic status. Across the study duration, COPD users showed decreasing concerns for COVID-19 and increasing health-related concerns. After the middle phase of COVID-19 (July 2021), a distinct decrease in sentiments among COPD users contrasted sharply with the upward trend among non-COPD users. Notably, in the post-COVID era (June 2023), COPD users showed reduced levels of joy and trust and increased levels of fear compared to their levels of joy and trust in the middle phase of COVID-19. Moreover, males, older adults, and individuals with lower socioeconomic status showed heightened fear compared to their counterparts.

CONCLUSIONS: Our data analysis results suggest that populations with COPD experienced heightened mental stress in the post-COVID era. This underscores the importance of developing tailored interventions and support systems that account for diverse population characteristics.

PMID:40053739 | DOI:10.2196/54543

J Med Internet Res. 2025 Feb 28;27:e67576. doi: 10.2196/67576.

ABSTRACT

BACKGROUND: In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency department (ED)-based IHCA (EDCA) accounts for 10% to 20% of all IHCA cases. Early detection of EDCA is crucial, yet identifying subtle signs of cardiac deterioration is challenging. Traditional EDCA prediction methods primarily rely on structured vital signs or electrocardiogram (ECG) signals, which require additional preprocessing or specialized devices. This study introduces a novel approach using image-based 12-lead ECG data obtained at ED triage, leveraging the inherent richness of visual ECG patterns to enhance prediction and integration into clinical workflows.

OBJECTIVE: This study aims to address the challenge of early detection of EDCA by developing an innovative deep learning model, the ECG-Image-Aware Network (EIANet), which uses 12-lead ECG images for early prediction of EDCA. By focusing on readily available triage ECG images, this research seeks to create a practical and accessible solution that seamlessly integrates into real-world ED workflows.

METHODS: For adult patients with EDCA (cases), 12-lead ECG images at ED triage were obtained from 2 independent data sets: National Taiwan University Hospital (NTUH) and Far Eastern Memorial Hospital (FEMH). Control ECGs were randomly selected from adult ED patients without cardiac arrest during the same study period. In EIANet, ECG images were first converted to binary form, followed by noise reduction, connected component analysis, and morphological opening. A spatial attention module was incorporated into the ResNet50 architecture to enhance feature extraction, and a custom binary recall loss (BRLoss) was used to balance precision and recall, addressing slight data set imbalance. The model was developed and internally validated on the NTUH-ECG data set and was externally validated on an independent FEMH-ECG data set. The model performance was evaluated using the F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC).

RESULTS: There were 571 case ECGs and 826 control ECGs in the NTUH data set and 378 case ECGs and 713 control ECGs in the FEMH data set. The novel EIANet model achieved an F1-score of 0.805, AUROC of 0.896, and AUPRC of 0.842 on the NTUH-ECG data set with a 40% positive sample ratio. It achieved an F1-score of 0.650, AUROC of 0.803, and AUPRC of 0.678 on the FEMH-ECG data set with a 34.6% positive sample ratio. The feature map showed that the region of interest in the ECG was the ST segment.

CONCLUSIONS: EIANet demonstrates promising potential for accurately predicting EDCA using triage ECG images, offering an effective solution for early detection of high-risk cases in emergency settings. This approach may enhance the ability of health care professionals to make timely decisions, with the potential to improve patient outcomes by enabling earlier interventions for EDCA.

PMID:40053733 | DOI:10.2196/67576

J Chem Inf Model. 2025 Mar 7. doi: 10.1021/acs.jcim.4c02216. Online ahead of print.

ABSTRACT

Proteins, as the fundamental macromolecules of life, play critical roles in various biological processes. Recent advancements in intelligent protein function prediction methods leverage sequences, structures, and biomedical literature data. Among them, function prediction methods for protein sequences remain an enduring and popular research direction. Existing studies have failed to effectively utilize the multilevel attribute features reflected in protein sequences. This limitation hinders the enrichment of protein descriptions needed for high-precision prediction of protein functions. To address this, we propose DeepMVD, a novel deep learning model that enhances prediction accuracy by dynamically fusing multiview features. DeepMVD employs specialized modules to extract unique features from each view and utilizes an adaptive fusion mechanism for optimal integration. Evaluation of the CAFA4 data set shows that DeepMVD significantly outperforms existing state-of-the-art models in terms of BP, MF, and CC terminology, all obtaining the highest Fmax (0.523, 0.712, 0.740). Ablation studies confirm the model's robustness. Source code and data sets are available at http://swanhub.co/scl/DeepMVD.

PMID:40053671 | DOI:10.1021/acs.jcim.4c02216

IEEE J Biomed Health Inform. 2025 Mar 7;PP. doi: 10.1109/JBHI.2025.3548940. Online ahead of print.

ABSTRACT

MiRNA subcellular localizations (MSLs) are essential for uncovering and understanding miRNA functions in various biological processes. Several computational methods have been proposed for measuring MSL. However, existing methods only rely on manually crafted features based on sequence without considering RNA 3D structure information, and most methods often rely on single-model approaches, which fail to capture the full complexity of biological systems, further hindering predictive accuracy and performance. In this study, we introduce a deep learning-based approach, MMFmiRLocEL, which integrates multi-model fusion and ensemble learning for MSL identification. To the best of our knowledge, MMFmiRLocEL is the first method to combine sequence, structure, and function three information for MSL prediction. Specifically, it employs RNA 3D structure generated by the predicted structural model to construct a structure-based approach for MSL prediction. It also develops a sequence-based prediction method using sequence features and convolutional neural networks, while constructing a function-based prediction method using miRNA-disease association networks and deep residual neural networks. Furthermore, a multi-model fusion approach, employing weighted ensemble strategies, integrates sequence, structure, and function models to enhance the robustness and accuracy of MSL identification. Experimental results demonstrate that MMFmiRLocEL outperforms existing state-of-the-art methods, and then ablation analysis confirmed the significant contribution of the multi-model fusion mechanism to improve the prediction performance.

PMID:40053625 | DOI:10.1109/JBHI.2025.3548940

PLoS One. 2025 Mar 7;20(3):e0317240. doi: 10.1371/journal.pone.0317240. eCollection 2025.

ABSTRACT

BACKGROUND: Breast cancer (BC) continues to pose a substantial global health concern, necessitating continuous advancements in therapeutic approaches. Neoadjuvant chemotherapy (NAC) has gained prominence as a key therapeutic strategy, and there is growing interest in the predictive utility of Background Parenchymal Enhancement (BPE) in evaluating the response of breast tumors to NAC. However, the analysis of BPE as a predictive biomarker, along with the techniques used to model BPE changes for accurate and timely predictions of treatment response presents several obstacles. This systematic review aims to thoroughly investigate recent advancements in the analytical methodologies for BPE analysis, and to evaluate their reliability and effectiveness in predicting breast tumor response to NAC, ultimately contributing to the development of personalized and effective therapeutic strategies.

METHODS: A comprehensive and structured literature search was conducted across key electronic databases, including Cochrane Database of Systematic Reviews, Google Scholar, PubMed, and IEEE Xplore covering articles published up to May 10, 2024. The inclusion criteria targeted studies focusing on breast cancer cohorts treated with NAC, involving both pre-treatment and at least one post-treatment breast dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) scan, and analyzing BPE utility in predicting breast tumor response to NAC. Methodological quality assessment and data extraction were performed to synthesize findings and identify commonalities and differences among various BPE analytical approaches.

RESULTS: The search yielded a total of 882 records. After meticulous screening, 78 eligible records were identified, with 13 studies ultimately meeting the inclusion criteria for the systematic review. Analysis of the literature revealed a significant evolution in BPE analysis, from early studies focusing on single time-point BPE analysis to more recent studies adopting longitudinal BPE analysis. The review uncovered several gaps that compromise the accuracy and timeliness of existing longitudinal BPE analysis methods, such as missing data across multiple imaging time points, manual segmentation of the whole-breast region of interest, and over reliance on traditional statistical methods like logistic regression for modeling BPE and pathological complete response (pCR).

CONCLUSION: This review provides a thorough examination of current advancements in analytical approaches for BPE analysis in predicting breast tumor response to NAC. The shift towards longitudinal BPE analysis has highlighted significant gaps, suggesting the need for alternative analytical techniques, particularly in the realm of artificial intelligence (AI). Future longitudinal BPE research work should focus on standardization in longitudinal BPE measurement and analysis, through integration of deep learning-based approaches for automated tumor segmentation, and implementation of advanced AI technique that can better accommodate varied breast tumor responses, non-linear relationships and complex temporal dynamics in BPE datasets, while also handling missing data more effectively. Such integration could lead to more precise and timely predictions of breast tumor responses to NAC, thereby enhancing personalized and effective breast cancer treatment strategies.

PMID:40053513 | DOI:10.1371/journal.pone.0317240

Environ Sci Technol. 2025 Mar 7. doi: 10.1021/acs.est.4c14209. Online ahead of print.

ABSTRACT

Water, as a finite and vital resource, necessitates water quality monitoring to ensure its sustainable use. A key aspect of this process is the accurate measurement of critical parameters such as chemical oxygen demand (COD). However, current spectroscopic methods struggle with accurately and consistently measuring COD in large-scale, complex water environments due to an insufficient understanding of water spectra and limited generalizability. To address these limitations, we introduce the physicochemical-informed spectral Transformer (PIST) model, combined with ultraviolet-visible-shortwave-near-infrared (UV-vis-SWNIR) spectroscopy for water quality sensing. To the best of our knowledge, this is the first approach to combine Transformer with spectroscopy for water quality sensing. PIST integrates a physicochemical-informed block to incorporate existing physical and chemical information into the spectral encoding for domain adaptation, along with a feature embedding block for comprehensive spectral features extraction. We validated PIST using an actual surface water spectral data set with extensive geographic coverage including the Yangtze River and Poyang Lake. PIST demonstrated notable performance in COD sensing within complex water environments, achieving an impressive R2 value of 0.9008 and reducing root mean squared error (RMSE) by 45.20% and 29.38% compared to benchmark models such as support vector regression (SVR) and convolutional neural network (CNN). These results emphasize PIST's accuracy and generalizability, marking a significant advancement in multidisciplinary approaches that combine spectroscopy with deep learning for rapid water quality sensing.

PMID:40053333 | DOI:10.1021/acs.est.4c14209

Jpn J Radiol. 2025 Mar 7. doi: 10.1007/s11604-025-01759-9. Online ahead of print.

ABSTRACT

PURPOSE: This is a preliminary analysis of one of the secondary endpoints in the prospective study cohort. The aim of this study is to assess the image quality and diagnostic confidence for lung cancer of CT images generated by using cadmium-zinc-telluride (CZT)-based photon-counting-detector-CT (PCD-CT) and comparing these super-high-resolution (SHR) images with conventional normal-resolution (NR) CT images.

MATERIALS AND METHODS: Twenty-five patients (median age 75 years, interquartile range 66-78 years, 18 men and 7 women) with 29 lung nodules overall (including two patients with 4 and 2 nodules, respectively) were enrolled to undergo PCD-CT. Three types of images were reconstructed: a 512 × 512 matrix with adaptive iterative dose reduction 3D (AIDR 3D) as the NRAIDR3D image, a 1024 × 1024 matrix with AIDR 3D as the SHRAIDR3D image, and a 1024 × 1024 matrix with deep-learning reconstruction (DLR) as the SHRDLR image. For qualitative analysis, two radiologists evaluated the matched reconstructed series twice (NRAIDR3D vs. SHRAIDR3D and SHRAIDR3D vs. SHRDLR) and scored the presence of imaging findings, such as spiculation, lobulation, appearance of ground-glass opacity or air bronchiologram, image quality, and diagnostic confidence, using a 5-point Likert scale. For quantitative analysis, contrast-to-noise ratios (CNRs) of the three images were compared.

RESULTS: In the qualitative analysis, compared to NRAIDR3D, SHRAIDR3D yielded higher image quality and diagnostic confidence, except for image noise (all P < 0.01). In comparison with SHRAIDR3D, SHRDLR yielded higher image quality and diagnostic confidence (all P < 0.01). In the quantitative analysis, CNRs in the modified NRAIDR3D and SHRDLR groups were higher than those in the SHRAIDR3D group (P = 0.003, <0.001, respectively).

CONCLUSION: In PCD-CT, SHRDLR images provided the highest image quality and diagnostic confidence for lung tumor evaluation, followed by SHRAIDR3D and NRAIDR3D images. DLR demonstrated superior noise reduction compared to other reconstruction methods.

PMID:40053285 | DOI:10.1007/s11604-025-01759-9

Crit Rev Food Sci Nutr. 2025 Mar 7:1-19. doi: 10.1080/10408398.2025.2474183. Online ahead of print.

ABSTRACT

Tea is one of the most popular drinks due to its distinct flavor and numerous health benefits. The quality of tea is closely related to production processing. Human sensory evaluation is the conventional method for quality monitoring in tea processing. However, this method is subjective and susceptible to environmental influences. Therefore, visible/near-infrared spectroscopy (Vis/NIRS) and hyperspectral imaging (HSI) techniques offer great potential due to their rapid detection speed, nondestructive, low cost, and simple operations. Artificial intelligence (AI) is one of the most promising methodological approaches for spectral analysis and decision-making of automated production. Vis/NIRS and HSI techniques assisted by AI further promote the progress of quality monitoring in tea processing. This paper reviewed the updated applications of Vis/NIRS and HSI techniques assisted by AI for quality monitoring in tea processing from 2019 to 2025. In particular, the tea production process, theories of Vis/NIRS and HSI techniques, and AI algorithms in spectral analysis are briefly introduced. Furthermore, the recent applications of Vis/NIRS and HSI techniques assisted by AI in tea processing quality monitoring are summarized and discussed. Finally, the challenges and future trends of Vis/NIRS and HSI techniques associated with their practical application in the tea industry are presented.

PMID:40053139 | DOI:10.1080/10408398.2025.2474183

Abdom Radiol (NY). 2025 Mar 7. doi: 10.1007/s00261-025-04833-y. Online ahead of print.

ABSTRACT

OBJECTIVE: The objective of this study is to investigate the value of radiomics features and deep learning features based on positron emission tomography/computed tomography (PET/CT) in predicting perineural invasion (PNI) in rectal cancer.

METHODS: We retrospectively collected 120 rectal cancer (56 PNI-positive patients 64 PNI-negative patients) patients with preoperative 18F-FDG PET/CT examination and randomly divided them into training and validation sets at a 7:3 ratio. We also collected 31 rectal cancer patients from two other hospitals as an independent external validation set. χ2 test and binary logistic regression were used to analyze PET metabolic parameters. PET/CT images were utilized to extract radiomics features and deep learning features. The Mann-Whitney U test and LASSO were employed to select valuable features. Metabolic parameter, radiomics, deep learning and combined models were constructed. ROC curves were generated to evaluate the performance of models.

RESULTS: The results indicate that metabolic tumor volume (MTV) is correlated with PNI (P = 0.001). In the training set and validation set, the AUC values of the metabolic parameter model were 0.673 (95%CI: 0.572-0.773), 0.748 (95%CI: 0.599-0.896). We selected 16 radiomics features and 17 deep learning features as valuable factors for predicting PNI. The AUC values of radiomics model and deep learning model were 0.768 (95%CI: 0.667-0.868) and 0.860 (95%CI: 0.780-0.940) in the training set. And the AUC values in the validation set were 0.803 (95%CI: 0.656-0.950) and 0.854 (95% CI 0.721-0.987). Finally, the combined model exhibited AUCs of 0.893 (95%CI: 0.825-0.961) in the training set and 0.883 (95%CI: 0.775-0.990) in the validation set. In the external validation set, the combined model achieved an AUC of 0.829 (95% CI: 0.674-0.984), outperforming each individual model. The decision curve analysis of these models indicated that using the combined model to guide treatment provided a substantial net benefit.

CONCLUSIONS: This combined model established by integrating PET metabolic parameters, radiomics features, and deep learning features can accurately predict the PNI in rectal cancer.

PMID:40053051 | DOI:10.1007/s00261-025-04833-y

Transl Vis Sci Technol. 2025 Mar 3;14(3):6. doi: 10.1167/tvst.14.3.6.

ABSTRACT

Optical coherence tomography angiography (OCTA) is a signal processing and scan acquisition approach that enables OCT devices to clearly identify vascular tissue down to the capillary scale. As originally proposed, OCTA included several important limitations, including small fields of view relative to allied imaging modalities and the presence of confounding artifacts. New approaches, including both hardware and software, are solving these problems and can now produce high-quality angiograms from tissue throughout the retina and choroid. Image analysis tools have also improved, enabling OCTA data to be quantified at high precision and used to diagnose disease using deep learning models. This review highlights these advances and trends in OCTA technology, focusing on work produced since 2020.

PMID:40052848 | DOI:10.1167/tvst.14.3.6

J Proteome Res. 2025 Mar 7. doi: 10.1021/acs.jproteome.4c00972. Online ahead of print.

ABSTRACT

Mass spectrometry (MS)-based proteomics relies heavily on MS/MS (MS2) data, which do not fully exploit the available MS1 information. Traditional peptide identity propagation (PIP) methods, such as match-between-runs (MBR), are limited to similar runs, particularly with the same liquid chromatography (LC) gradients, thus potentially underutilizing available proteomics libraries. We introduce SWAPS, a novel and modular MS1-centric framework incorporating advances in peptide property prediction, extensive proteomics libraries, and deep-learning-based postprocessing to enable and explore PIP across more diverse experimental conditions and LC gradients. SWAPS substantially enhances precursor identification, especially in shorter gradients. On the example of 30, 15, and 7.5 min gradients, SWAPS achieves increases of 46.3, 86.2, and 112.1% on precursor level over MaxQuant's MS2-based identifications. Despite the inherent challenges in controlling false discovery rates (FDR) with MS1-based methods, SWAPS demonstrates strong efficacy in deconvoluting MS1 signals, offering powerful discrimination and deeper sequence exploration, while maintaining quantitative accuracy. By building on and applying peptide property predictions in practical contexts, SWAPS reveals that current models, while advanced, are still not fully comparable to experimental measurements, sparking the need for further research. Additionally, its modular design allows seamless integration of future improvements, positioning SWAPS as a forward-looking tool in proteomics.

PMID:40052690 | DOI:10.1021/acs.jproteome.4c00972

Eur Heart J Cardiovasc Imaging. 2025 Mar 7:jeaf081. doi: 10.1093/ehjci/jeaf081. Online ahead of print.

ABSTRACT

AIMS: Understanding determinants of thoracic aortic morphology is crucial for precise diagnostics and therapeutic approaches. This study aimed to automatically characterize ascending aortic morphology based on 3D non-contrast-enhanced magnetic resonance angiography (NC-MRA) data from the epidemiological cross-sectional German National Cohort (NAKO) and to investigate possible determinants of mid-ascending aortic diameter (mid-AAoD).

METHODS AND RESULTS: Deep learning (DL) automatically segmented the thoracic aorta and ascending aortic length, volume, and diameter was extracted from 25,073 NC-MRAs. Statistical analyses investigated relationships between mid-AAoD and demographic factors, hypertension, diabetes, alcohol, and tobacco consumption. Males exhibited significantly larger mid-AAoD than females (M:35.5±4.8mm, F:33.3±4.5mm). Age and body surface area (BSA) were positively correlated with mid-AAoD (age: male: r²=0.20, p<0.001, female: r²=0.16, p<0.001; BSA: male: r²=0.08, p<0.001, female: r²=0.05, p<0.001). Hypertensive and diabetic subjects showed higher mid-AAoD (ΔHypertension = 2.9 ± 0.5mm; ΔDiabetes = 1.5 ± 0.6mm). Hypertension was linked to higher mid-AAoD regardless of age and BSA, while diabetes and mid-AAoD were uncorrelated across age-stratified subgroups. Daily alcohol consumption (male: 37.4±5.1mm, female: 35.0±4.8mm) and smoking history exceeding 16.5 pack-years (male: 36.6±5.0mm, female: 33.9±4.3mm) exhibited highest mid-AAoD. Causal analysis (Peter-Clark algorithm) suggested that age, BSA, hypertension, and alcohol consumption are possibly causally related to mid-AAoD, while diabetes and smoking are likely spuriously correlated.

CONCLUSIONS: This study demonstrates the potential of DL and causal analysis for understanding ascending aortic morphology. By disentangling observed correlations using causal analysis, this approach identifies possible causal determinants, such as age, BSA, hypertension, and alcohol consumption. These findings can inform targeted diagnostics and preventive strategies, supporting clinical decision-making for cardiovascular health.

PMID:40052574 | DOI:10.1093/ehjci/jeaf081

J Food Sci. 2025 Mar;90(3):e70116. doi: 10.1111/1750-3841.70116.

ABSTRACT

Chronic diseases such as obesity and hypertension due to malnutrition can be prevented by following the appropriate diet, correct diet intake with correct measuring portion size, and developing healthy eating habits. Having a system that can automatically measure food consumption is important to determine whether individual nutritional needs are being met in order to accurately diagnose and solve nutritional problems, act quickly, and minimize the risk of malnutrition due to the cross-cultural diversity of foods. In this study, a deep learning system has been developed and implemented for automatically grouping and classifying foods. Dishes from Turkish cuisine were chosen as a sample for application and testing. The deep learning method used in this system is convolutional neural network (CNN) models based on image recognition. This study developed and implemented a deep learning system using CNNs to classify food groups and estimate portion sizes of Turkish cuisine dishes, achieving accuracy rates of up to 80% for food group classification and 80.47% for portion estimation with the inclusion of data augmentation.

PMID:40052549 | DOI:10.1111/1750-3841.70116

Brief Bioinform. 2025 Mar 4;26(2):bbaf087. doi: 10.1093/bib/bbaf087.

ABSTRACT

Cancer neoantigens are peptides that originate from alterations in the genome, transcriptome, or proteome. These peptides can elicit cancer-specific T-cell recognition, making them potential candidates for cancer vaccines. The rapid advancement of proteomics technology holds tremendous potential for identifying these neoantigens. Here, we provided an up-to-date survey about database-based search methods and de novo peptide sequencing approaches in proteomics, and we also compared these methods to recommend reliable analytical tools for neoantigen identification. Unlike previous surveys on mass spectrometry-based neoantigen discovery, this survey summarizes the key advancements in de novo peptide sequencing approaches that utilize artificial intelligence. From a comparative study on a dataset of the HepG2 cell line and nine mixed hepatocellular carcinoma proteomics samples, we demonstrated the potential of proteomics for the identification of cancer neoantigens and conducted comparisons of the existing methods to illustrate their limits. Understanding these limits, we suggested a novel workflow for neoantigen discovery as perspectives.

PMID:40052441 | DOI:10.1093/bib/bbaf087

Comput Methods Biomech Biomed Engin. 2025 Mar 7:1-14. doi: 10.1080/10255842.2025.2470809. Online ahead of print.

ABSTRACT

Acute kidney injury (AKI) is a prevalent clinical syndrome that causes over one-fifth of hospitalized patients worldwide to suffer from AKI. We proposed the GCAT, which aims to identify high-risk AKI patients in the hospital settings using the MIMIC-III dataset. Firstly, it fully explores the similarity of attribute features among a large number of patients and calculates the attribute similarity values between patients to generate a node similarity matrix. Then, it selects nodes with high similarity to construct a patient feature similarity network (PFSN). Experiments demonstrate that the GCAT achieves an accuracy of 88.57%, its effectiveness is superior to state-of-the-art methods.

PMID:40052403 | DOI:10.1080/10255842.2025.2470809

Glob Chang Biol. 2025 Mar;31(3):e70118. doi: 10.1111/gcb.70118.

ABSTRACT

Understanding the dynamics of soil respiration (Rs) and its environmental drivers is crucial for accurately modeling terrestrial carbon fluxes. However, current methodologies often lead to divergent estimates and rely on annual predictions that may overlook critical interactions occurring at seasonal scales. A critical knowledge gap lies in understanding how temporal resolution affects both Rs predictions and their environmental drivers. Here, we employ deep learning models to predict global Rs at monthly (MRM) and annual (ARM) scales from 1982 to 2018. We then consider three main drivers potentially affecting Rs, including temperature, precipitation, and a vegetation proxy (leaf area index; LAI). Our models demonstrate strong predictive capabilities with global Rs estimation of 79.4 ± 5.7 Pg C year-1 for the MRM and 78.3 ± 7.5 Pg C year-1 for ARM (mean ± SD). While the difference in global estimations between both models is small, there are notable disparities in the spatial contribution of dominant drivers. The MRM highlights an influence of both temperature and LAI, while the ARM emphasizes a dominant role of precipitation. These findings underscore the critical role of temporal resolution in capturing seasonal variations and identifying key Rs-environment relationships that annual models may obscure. High temporal resolution Rs predictions, such as those provided by the MRM, are essential for capturing nuanced seasonal interactions between Rs and its drivers, refining carbon flux models, detecting critical seasonal thresholds, and enhancing the reliability of future Earth system predictions. This work highlights the need for further research into monthly and seasonal Rs variations, as well as higher timescale resolutions, to advance our understanding of ecosystem carbon dynamics in a rapidly changing climate.

PMID:40052202 | DOI:10.1111/gcb.70118