ImportanceUnderstanding the relevance of covert cerebrovascular disease (CCD) for later health will allow clinicians to more effectively monitor and target interventions. ObjectiveTo examine the association between clinically reported CCD, measured using natural language processing (NLP), and subsequent disease risk. Design, Setting and ParticipantsWe conducted a retrospective e-cohort study using linked health record data. From all people with clinical brain imaging in Scotland from 2010 to 2018, we selected people with no prior hospitalisation for neurological disease. The data were analysed from March 2024 to June 2025. ExposureFour phenotypes were identified with NLP of imaging reports: white matter hypoattenuation or hyperintensities (WMH), lacunes, cortical infarcts and cerebral atrophy. Main outcomes and measuresHazard ratios (aHR) for stroke, dementia, and Parkinsons disease (conditions previously associated with CCD), epilepsy (a brain-based control condition) and colorectal cancer (a non-brain control condition), adjusted for age, sex, deprivation, region, scan modality, and pre-scan healthcare, were calculated for each phenotype. ResultsFrom 395,273 people with brain imaging and no history of neurological disease, 145,978 (37%) had [≥]1 phenotype. For each phenotype, the aHR of any stroke was: WMH 1.4 (95%CI: 1.3-1.4), lacunes 1.6 (1.5-1.6), cortical infarct 1.7 (1.6-1.8), and cerebral atrophy 1.1 (1.0-1.1). The aHR of any dementia was: WMH, 1.3 (1.3-1.3), lacunes, 1.0 (0.9-1.0), cortical infarct 1.1 (1.0-1.1) and cerebral atrophy 1.7 (1.7-1.7). The aHR of Parkinsons disease was, in people with a report of: WMH 1.1 (1.0-1.2), lacunes 1.1 (0.9-1.2), cortical infarct 0.7 (0.6-0.9) and cerebral atrophy 1.4 (1.3-1.5). The aHRs between CCD phenotypes and epilepsy and colorectal cancer overlapped the null. Conclusions and RelevanceNLP identified CCD and atrophy phenotypes from routine clinical image reports, and these had important associations with future stroke, dementia and Parkinsons disease. Prevention of neurological disease in people with CCD should be a priority for healthcare providers and policymakers. Key PointsO_ST_ABSQuestionC_ST_ABSAre measures of Covert Cerebrovascular Disease (CCD) associated with the risk of subsequent disease (stroke, dementia, Parkinsons disease, epilepsy, and colorectal cancer)? FindingsThis study used a validated NLP algorithm to identify CCD (white matter hypoattenuation/hyperintensities, lacunes, cortical infarcts) and cerebral atrophy from both MRI and computed tomography (CT) imaging reports generated during routine healthcare in >395K people in Scotland. In adjusted models, we demonstrate higher risk of dementia (particularly Alzheimers disease) in people with atrophy, and higher risk of stroke in people with cortical infarcts. However, associations with an age-associated control outcome (colorectal cancer) were neutral, supporting a causal relationship. It also highlights differential associations between cerebral atrophy and dementia and cortical infarcts and stroke risk. MeaningCCD or atrophy on brain imaging reports in routine clinical practice is associated with a higher risk of stroke or dementia. Evidence is needed to support treatment strategies to reduce this risk. NLP can identify these important, otherwise uncoded, disease phenotypes, allowing research at scale into imaging-based biomarkers of dementia and stroke.

The long-term prognostic significance of the coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) for non-obstructive coronary artery disease (CAD) is uncertain. We aimed to investigate the additional prognostic value of CT-FFR beyond CCTA-defined atherosclerotic burden for long-term outcomes. Consecutive patients with suspected stable CAD were candidates for this retrospective cohort study. Deep-learning-based vessel-specific CT-FFR was calculated. All patients enrolled were followed for at least 5 years. The primary outcome was major adverse cardiovascular events (MACE). Predictive abilities for MACE were compared among three models (model 1, constructed using clinical variables; model 2, model 1 + CCTA-derived atherosclerotic burden (Leiden risk score and segment involvement score); and model 3, model 2 + CT-FFR). A total of 1944 patients (median age, 59 (53-65) years; 53.0% men) were included. During a median follow-up time of 73.4 (71.2-79.7) months, 64 patients (3.3%) experienced MACE. In multivariate-adjusted Cox models, CT-FFR ≤ 0.80 (HR: 7.18; 95% CI: 4.25-12.12; p < 0.001) was a robust and independent predictor for MACE. The discriminant ability was higher in model 2 than in model 1 (C-index, 0.76 vs. 0.68; p = 0.001) and was further promoted by adding CT-FFR to model 3 (C-index, 0.83 vs. 0.76; p < 0.001). Integrated discrimination improvement (IDI) was 0.033 (p = 0.022) for model 2 beyond model 1. Of note, compared with model 2, model 3 also exhibited improved discrimination (IDI = 0.056; p < 0.001). In patients with non-obstructive CAD, CT-FFR provides robust and incremental prognostic information for predicting long-term outcomes. The combined model including CT-FFR and CCTA-defined atherosclerotic burden exhibits improved prediction abilities, which is helpful for risk stratification. Question Prognostic significance of the CT-fractional flow reserve (FFR) in non-obstructive coronary artery disease for long-term outcomes merits further investigation. Findings Our data strongly emphasized the independent and additional predictive value of CT-FFR beyond coronary CTA-defined atherosclerotic burden and clinical risk factors. Clinical relevance The new combined predictive model incorporating CT-FFR can be satisfactorily used for risk stratification of patients with non-obstructive coronary artery disease by identifying those who are truly suitable for subsequent high-intensity preventative therapies and extensive follow-up for prognostic reasons.

Coronary computed tomography angiography (CCTA) has emerged as the first-line noninvasive imaging test for patients at high risk of coronary artery disease (CAD). When combined with machine learning (ML), it provides more valid evidence in diagnosing major adverse cardiovascular events (MACEs). Radiomics provides informative multidimensional features that can help identify high-risk populations and can improve the diagnostic performance of CCTA. However, its role in predicting MACEs remains highly debated. We evaluated the diagnostic value of ML models constructed using radiomic features extracted from CCTA in predicting MACEs, and compared the performance of different learning algorithms and models, thereby providing clinical recommendations for the diagnosis, treatment, and prognosis of MACEs. We comprehensively searched 5 online databases, Cochrane Library, Web of Science, Elsevier, CNKI, and PubMed, up to September 10, 2024, for original studies that used ML models among patients who underwent CCTA to predict MACEs and reported clinical outcomes and endpoints related to it. Risk of bias in the ML models was assessed by the Prediction Model Risk of Bias Assessment Tool, while the radiomics quality score (RQS) was used to evaluate the methodological quality of the radiomics prediction model development and validation. We also followed the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) guidelines to ensure transparency of ML models included. Meta-analysis was performed using Meta-DiSc software (version 1.4), which included the I² score and Cochran Q test, along with StataMP 17 (StataCorp) to assess heterogeneity and publication bias. Due to the high heterogeneity observed, subgroup analysis was conducted based on different model groups. Ten studies were included in the analysis, 5 (50%) of which differentiated between training and testing groups, where the training set collected 17 kinds of models and the testing set gathered 26 models. The pooled area under the receiver operating characteristic (AUROC) curve for ML models predicting MACEs was 0.7879 in the training set and 0.7981 in the testing set. Logistic regression (LR), the most commonly used algorithm, achieved an AUROC of 0.8229 in the testing group and 0.7983 in the training group. Non-LR models yielded AUROCs of 0.7390 in the testing set and 0.7648 in the training set, while the random forest (RF) models reached an AUROC of 0.8444 in the training group. Study limitations included a limited number of studies, high heterogeneity, and the types of included studies. The performance of ML models for predicting MACEs was found to be superior to that of general models based on basic feature extraction and integration from CCTA. Specifically, LR-based ML diagnostic models demonstrated significant clinical potential, particularly when combined with clinical features, and are worth further validation through more clinical trials. PROSPERO CRD42024596364; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024596364.

Breast cancer remains a leading cause of cancer-related mortality worldwide, making early detection and accurate treatment response monitoring critical priorities. We present BreastDCEDL, a curated, deep learning-ready dataset comprising pre-treatment 3D Dynamic Contrast-Enhanced MRI (DCE-MRI) scans from 2,070 breast cancer patients drawn from the I-SPY1, I-SPY2, and Duke cohorts, all sourced from The Cancer Imaging Archive. The raw DICOM imaging data were rigorously converted into standardized 3D NIfTI volumes with preserved signal integrity, accompanied by unified tumor annotations and harmonized clinical metadata including pathologic complete response (pCR), hormone receptor (HR), and HER2 status. Although DCE-MRI provides essential diagnostic information and deep learning offers tremendous potential for analyzing such complex data, progress has been limited by lack of accessible, public, multicenter datasets. BreastDCEDL addresses this gap by enabling development of advanced models, including state-of-the-art transformer architectures that require substantial training data. To demonstrate its capacity for robust modeling, we developed the first transformer-based model for breast DCE-MRI, leveraging Vision Transformer (ViT) architecture trained on RGB-fused images from three contrast phases (pre-contrast, early post-contrast, and late post-contrast). Our ViT model achieved state-of-the-art pCR prediction performance in HR+/HER2- patients (AUC 0.94, accuracy 0.93). BreastDCEDL includes predefined benchmark splits, offering a framework for reproducible research and enabling clinically meaningful modeling in breast cancer imaging.

To synthesize current evidence on prognostic factors, tools, and strategies influencing functional outcomes in patients with traumatic brain injury (TBI), with a focus on the acute and postacute phases of care. Key early predictors such as Glasgow Coma Scale (GCS) scores, pupillary reactivity, and computed tomography (CT) imaging findings remain fundamental in guiding clinical decision-making. Prognostic models like IMPACT and CRASH enhance early risk stratification, while outcome measures such as the Glasgow Outcome Scale-Extended (GOS-E) provide structured long-term assessments. Despite their utility, heterogeneity in assessment approaches and treatment protocols continues to limit consistency in outcome predictions. Recent advancements highlight the value of fluid biomarkers like neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP), which offer promising avenues for improved accuracy. Additionally, artificial intelligence models are emerging as powerful tools to integrate complex datasets and refine individualized outcome forecasting. Neurological prognostication after TBI is evolving through the integration of clinical, radiological, molecular, and computational data. Although standardized models and scales remain foundational, emerging technologies and therapies - such as biomarkers, machine learning, and neurostimulants - represent a shift toward more personalized and actionable strategies to optimize recovery and long-term function.

This study aimed to develop and evaluate models for classifying the severity of neurological impairment in acute ischemic stroke (AIS) patients using multimodal MRI data. A retrospective cohort of 1227 AIS patients was collected and categorized into mild (NIHSS<5) and moderate-to-severe (NIHSS≥5) stroke groups based on NIHSS scores. Eight baseline models were constructed for performance comparison, including a clinical model, radiomics models using DWI or multiple MRI sequences, and deep learning (DL) models with varying fusion strategies (early fusion, later fusion, full cross-fusion, and DWI-centered cross-fusion). All DL models were based on the Vision Transformer (ViT) framework. Model performance was evaluated using metrics such as AUC and ACC, and robustness was assessed through subgroup analyses and visualization using Grad-CAM. Among the eight models, the DL model using DWI as the primary sequence with cross-fusion of other MRI sequences (Model 8) achieved the best performance. In the test cohort, Model 8 demonstrated an AUC of 0.914, ACC of 0.830, and high specificity (0.818) and sensitivity (0.853). Subgroup analysis shows that model 8 is robust in most subgroups with no significant prediction difference (p > 0.05), and the AUC value consistently exceeds 0.900. A significant predictive difference was observed in the BMI group (p < 0.001). The results of external validation showed that the AUC values of the model 8 in center 2 and center 3 reached 0.910 and 0.912, respectively. Visualization using Grad-CAM emphasized the infarct core as the most critical region contributing to predictions, with consistent feature attention across DWI, T1WI, T2WI, and FLAIR sequences, further validating the interpretability of the model. A ViT-based DL model with cross-modal fusion strategies provides a non-invasive and efficient tool for classifying AIS severity. Its robust performance across subgroups and interpretability make it a promising tool for personalized management and decision-making in clinical practice.

Atherosclerosis involves not only the narrowing of blood vessels and plaque accumulation but also changes in plaque composition and stability, all of which are critical for disease progression. Conventional imaging techniques such as magnetic resonance angiography (MRA) and digital subtraction angiography (DSA) primarily assess luminal narrowing and plaque size, but have limited capability in identifying plaque instability and inflammation within the vascular muscle wall. This study aimed to develop and evaluate a novel imaging approach using ligand-modified nanomagnetic contrast (lmNMC) nanoprobes in combination with molecular magnetic resonance imaging (mMRI) to visualize and quantify vascular inflammation and plaque characteristics in a rabbit model of atherosclerosis. A rabbit model of atherosclerosis was established and underwent mMRI before and after administration of lmNMC nanoprobes. Radiomic features were extracted from segmented images using discrete wavelet transform (DWT) to assess spatial frequency changes and gray-level co-occurrence matrix (GLCM) analysis to evaluate textural properties. Further radiomic analysis was performed using neural network-based regression and clustering, including the application of self-organizing maps (SOMs) to validate the consistency of radiomic pattern between training and testing data. Radiomic analysis revealed significant changes in spatial frequency between pre- and post-contrast images in both the horizontal and vertical directions. GLCM analysis showed an increase in contrast from 0.08463 to 0.1021 and a slight decrease in homogeneity from 0.9593 to 0.9540. Energy values declined from 0.2256 to 0.2019, while correlation increased marginally from 0.9659 to 0.9708. Neural network regression demonstrated strong convergence between target and output coordinates. Additionally, SOM clustering revealed consistent weight locations and neighbor distances across datasets, supporting the reliability of the radiomic validation. The integration of lmNMC nanoprobes with mMRI enables detailed visualization of atherosclerotic plaques and surrounding vascular inflammation in a preclinical model. This method shows promise for enhancing the characterization of unstable plaques and may facilitate early detection of high-risk atherosclerotic lesions, potentially improving diagnostic and therapeutic strategies.

We explore biases present in publicly available fetal ultrasound (US) imaging datasets, currently at the disposal of researchers to train deep learning (DL) algorithms for prenatal diagnostics. As DL increasingly permeates the field of medical imaging, the urgency to critically evaluate the fairness of benchmark public datasets used to train them grows. Our thorough investigation reveals a multifaceted bias problem, encompassing issues such as lack of demographic representativeness, limited diversity in clinical conditions depicted, and variability in US technology used across datasets. We argue that these biases may significantly influence DL model performance, which may lead to inequities in healthcare outcomes. To address these challenges, we recommend a multilayered approach. This includes promoting practices that ensure data inclusivity, such as diversifying data sources and populations, and refining model strategies to better account for population variances. These steps will enhance the trustworthiness of DL algorithms in fetal US analysis.

We developed an automated photoacoustic and ultrasound breast tomography system that images the patient in the standing pose. The system, named OneTouch-PAT, utilized linear transducer arrays with optical-acoustic combiners for effective dual-modal imaging. During scanning, subjects only need to gently attach their breasts to the imaging window, and co-registered three-dimensional ultrasonic and photoacoustic images of the breast can be obtained within one minute. Our system has a large field of view of 17 cm by 15 cm and achieves an imaging depth of 3 cm with sub-millimeter resolution. A three-dimensional deep-learning network was also developed to further improve the image quality by improving the 3D resolution, enhancing vasculature, eliminating skin signals, and reducing noise. The performance of the system was tested on four healthy subjects and 61 patients with breast cancer. Our results indicate that the ultrasound structural information can be combined with the photoacoustic vascular information for better tissue characterization. Representative cases from different molecular subtypes have indicated different photoacoustic and ultrasound features that could potentially be used for imaging-based cancer classification. Statistical analysis among all patients indicates that the regional photoacoustic intensity and vessel branching points are indicators of breast malignancy. These promising results suggest that our system could significantly enhance breast cancer diagnosis and classification.

To develop a deep learning (DL) model for predicting disease-free survival (DFS) in clinical stage I lung cancer patients who underwent surgical resection using pre-treatment CT images, and further validate it in patients receiving stereotactic body radiation therapy (SBRT). A retrospective cohort of 2489 clinical stage I non-small cell lung cancer (NSCLC) patients treated with operation (2015-2017) was enrolled to develop a DL-based DFS prediction model. Tumor features were extracted from CT images using a three-dimensional convolutional neural network. External validation was performed on 248 clinical stage I patients receiving SBRT from two hospitals. A clinical model was constructed by multivariable Cox regression for comparison. Model performance was evaluated with Harrell's concordance index (C-index), which measures the model's ability to correctly rank survival times by comparing all possible pairs of subjects. In the surgical cohort, the DL model effectively predicted DFS with a C-index of 0.85 (95% CI: 0.80-0.89) in the internal testing set, significantly outperforming the clinical model (C-index: 0.76). Based on the DL model, 68 patients in the SBRT cohort identified as high-risk had significantly worse DFS compared to the low-risk group (p < 0.01, 5-year DFS rate: 34.7% vs 77.4%). The DL-score was demonstrated to be an independent predictor of DFS in both cohorts (p < 0.01). The CT-based DL model improved DFS prediction in clinical stage I lung cancer patients, identifying populations at high risk of recurrence and metastasis to guide clinical decision-making. Question The recurrence or metastasis rate of early-stage lung cancer remains high and varies among patients following radical treatments such as surgery or SBRT. Findings This CT-based DL model successfully predicted DFS and stratified varying disease risks in clinical stage I lung cancer patients undergoing surgery or SBRT. Clinical relevance The CT-based DL model is a reliable predictive tool for the prognosis of early-stage lung cancer. Its accurate risk stratification assists clinicians in identifying specific patients for personalized clinical decision making.