Cervical spondylosis, a complex and prevalent condition, demands precise and efficient diagnostic techniques for accurate assessment. While MRI offers detailed visualization of cervical spine anatomy, manual interpretation remains labor-intensive and prone to error. To address this, we developed an innovative AI-assisted Expert-based Diagnosis System that automates both segmentation and diagnosis of cervical spondylosis using MRI. Leveraging multi-center datasets of cervical MRI images from patients with cervical spondylosis, our system features a pathology-guided segmentation model capable of accurately segmenting key cervical anatomical structures. The segmentation is followed by an expert-based diagnostic framework that automates the calculation of critical clinical indicators. Our segmentation model achieved an impressive average Dice coefficient exceeding 0.90 across four cervical spinal anatomies and demonstrated enhanced accuracy in herniation areas. Diagnostic evaluation further showcased the system's precision, with the lowest mean average errors (MAE) for the C2-C7 Cobb angle and the Maximum Spinal Cord Compression (MSCC) coefficient. In addition, our method delivered high accuracy, precision, recall, and F1 scores in herniation localization, K-line status assessment, T2 hyperintensity detection, and Kang grading. Comparative analysis and external validation demonstrate that our system outperforms existing methods, establishing a new benchmark for segmentation and diagnostic tasks for cervical spondylosis.

To develop a CT-based deep learning radiomics nomogram (DLRN) for the preoperative prediction of peritoneal metastasis (PM) in patients with ovarian cancer (OC). A total of 296 patients with OCs were randomly divided into training dataset (N = 207) and test dataset (N = 89). The radiomics features and DL features were extracted from CT images of each patient. Specifically, radiomics features were extracted from the 3D tumor regions, while DL features were extracted from the 2D slice with the largest tumor region of interest (ROI). The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomics and DL features, and the radiomics score (Radscore) and DL score (Deepscore) were calculated. Multivariate logistic regression was employed to construct clinical model. The important clinical factors, radiomics and DL features were integrated to build the DLRN. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and DeLong's test. Nine radiomics features and 10 DL features were selected. Carbohydrate antigen 125 (CA-125) was the independent clinical predictor. In the training dataset, the AUC values of the clinical, radiomics and DL models were 0.618, 0.842, and 0.860, respectively. In the test dataset, the AUC values of these models were 0.591, 0.819 and 0.917, respectively. The DLRN showed better performance than other models in both training and test datasets with AUCs of 0.943 and 0.951, respectively. Decision curve analysis and calibration curve showed that the DLRN provided relatively high clinical benefit in both the training and test datasets. The DLRN demonstrated superior performance in predicting preoperative PM in patients with OC. This model offers a highly accurate and noninvasive tool for preoperative prediction, with substantial clinical potential to provide critical information for individualized treatment planning, thereby enabling more precise and effective management of OC patients.

Radiomics research has been hindered by inconsistent and often poor methodological quality, limiting its potential for clinical translation. To address this challenge, the METhodological RadiomICs Score (METRICS) was recently introduced as a tool for systematically assessing study rigor. However, its effective application requires clearer guidance. The METRICS-E3 (Explanation and Elaboration with Examples) resource was developed by the European Society of Medical Imaging Informatics-Radiomics Auditing Group in response. This international initiative provides comprehensive support for users by offering detailed rationales, interpretive guidance, scoring recommendations, and illustrative examples for each METRICS item and condition. Each criterion includes positive examples from peer-reviewed, open-access studies and hypothetical negative examples. In total, the finalized METRICS-E3 includes over 200 examples. The complete resource is publicly available through an interactive website. CRITICAL RELEVANCE STATEMENT: METRICS-E3 offers deeper insights into each METRICS item and condition, providing concrete examples with accompanying commentary and recommendations to enhance the evaluation of methodological quality in radiomics research. KEY POINTS: As a complementary initiative to METRICS, METRICS-E3 is intended to support stakeholders in evaluating the methodological aspects of radiomics studies. In METRICS-E3, each METRICS item and condition is supplemented with interpretive guidance, positive literature-based examples, hypothetical negative examples, and scoring recommendations. The complete METRICS-E3 explanation and elaboration resource is accessible at its interactive website.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content</i>. The MammosighTR dataset, derived from Türkiye's national breast cancer screening mammography program, provides BI-RADS-labeled mammograms with detailed annotations on breast composition and lesion quadrant location, which may be useful for developing and testing AI models in breast cancer detection. ©RSNA, 2025.

To establish an ultrasound-based radiomics model to differentiate fibro adipose vascular anomaly (FAVA) and intramuscular venous malformation (VM). The clinical data of 65 patients with VM and 31 patients with FAVA who were treated and pathologically confirmed were retrospectively analyzed. Dimensionality reduction was performed on these features using the least absolute shrinkage and selection operator (LASSO). An ultrasound-based radiomics model was established using support vector machine (SVM) and random forest (RF) models. The diagnostic efficiency of this model was evaluated using the receiver operating characteristic. A total of 851 features were obtained by feature extraction, and 311 features were screened out using the <i>t</i>-test and Mann-Whitney <i>U</i> test. The dimensionality reduction was performed on the remaining features using LASSO. Finally, seven features were included to establish the diagnostic prediction model. In the testing group, the AUC, accuracy and specificity of the SVM model were higher than those of the RF model (0.841 [0.815-0.867] vs. 0.791 [0.759-0.824], 96.6% vs. 93.1%, and 100.0% vs. 90.5%, respectively). However, the sensitivity of the SVM model was lower than that of the RF model (88.9% vs. 100.0%). In this study, a prediction model based on ultrasound radiomics was developed to distinguish FAVA from VM. The study achieved high classification accuracy, sensitivity, and specificity. SVM model is superior to RF model and provides a new perspective and tool for clinical diagnosis.

The differentiation between traumatic and degenerative rotator cuff tears (RCTs remains a diagnostic challenge with significant implications for treatment planning. While magnetic resonance imaging (MRI) is standard practice, traditional radiological interpretation has shown limited reliability in distinguishing these etiologies. This study evaluates the potential of artificial intelligence (AI) models, specifically a multimodal vision transformer (ViT), to differentiate between traumatic and degenerative RCT. In this retrospective, single-centre study, 99 shoulder MRIs were analysed from patients who underwent surgery at a specialised university shoulder unit between 2016 and 2019. The cohort was divided into training (n = 79) and validation (n = 20) sets. The traumatic group required a documented relevant trauma (excluding simple lifting injuries), previously asymptomatic shoulder and MRI within 3 months posttrauma. The degenerative group was of similar age and injured tendon, with patients presenting with at least 1 year of constant shoulder pain prior to imaging and no trauma history. The ViT was subsequently combined with demographic data to finalise in a multimodal ViT. Saliency maps are utilised as an explainability tool. The multimodal ViT model achieved an accuracy of 0.75 ± 0.08 with a recall of 0.8 ± 0.08, specificity of 0.71 ± 0.11 and a F1 score of 0.76 ± 0.1. The model maintained consistent performance across different patient subsets, demonstrating robust generalisation. Saliency maps do not show a consistent focus on the rotator cuff. AI shows potential in supporting the challenging differentiation between traumatic and degenerative RCT on MRI. The achieved accuracy of 75% is particularly significant given the similar groups which presented a challenging diagnostic scenario. Saliency maps were utilised to ensure explainability, the given lack of consistent focus on rotator cuff tendons hints towards underappreciated aspects in the differentiation. Not applicable.

Hand and foot digital radiography (DR) is an indispensable tool in medical imaging, with varying diagnostic requirements necessitating different hand and foot positionings. Accurate positioning is crucial for obtaining diagnostically valuable images. Furthermore, adjusting exposure parameters such as exposure area based on patient conditions helps minimize the likelihood of image retakes. We propose a personalized positioning and exposure assistant capable of automatically recognizing hand and foot positionings and recommending appropriate exposure parameters to achieve these objectives. The assistant comprises three modules: (1) Progressive Iterative Hand-Foot Tracker (PIHFT) to iteratively locate hands or feet in RGB images, providing the foundation for accurate pose estimation; (2) Multi-Task Shared Pose Estimation Transformer (MTSPET), a Transformer-based model that encompasses hand and foot estimation branches with similar network architectures, sharing a common backbone. MTSPET outperformed MediaPipe in the hand pose estimation task and successfully transferred this capability to the foot pose estimation task; (3) Domain Expertise-embedded Positioning and Exposure Assistant (DEPEA), which combines the key-point coordinates of hands and feet with specific positioning and exposure parameter requirements, capable of checking patient positioning and inferring exposure areas and Regions of Interest (ROIs) of Digital Automatic Exposure Control (DAEC). Additionally, two datasets were collected and used to train MTSPET. A preliminary clinical trial showed strong agreement between PPEA's outputs and manual annotations, indicating the system's effectiveness in typical clinical scenarios. The contributions of this study lay the foundation for personalized, patient-specific imaging strategies, ultimately enhancing diagnostic outcomes and minimizing the risk of errors in clinical settings.

Prostate MRI has traditionally relied on qualitative interpretation. However, quantitative components hold the potential to markedly improve performance. The ADC from DWI is probably the most widely recognized quantitative MRI biomarker and has shown strong discriminatory value for clinically significant prostate cancer as well as for recurrent cancer after treatment. Advanced diffusion techniques, including intravoxel incoherent motion imaging, diffusion kurtosis imaging, diffusion-tensor imaging, and specific implementations such as restriction spectrum imaging, purport even better discrimination but are more technically challenging. The inherent T1 and T2 of tissue also provide diagnostic value, with more advanced techniques deriving luminal water fraction and hybrid multidimensional MRI metrics. Dynamic contrast-enhanced imaging, primarily using a modified Tofts model, also shows independent discriminatory value. Finally, quantitative lesion size and shape features can be combined with the aforementioned techniques and can be further refined using radiomics, texture analysis, and artificial intelligence. Which technique will ultimately find widespread clinical use will depend on validation across a myriad of platforms and use cases.

Low-dose CT (LDCT) protocols reduce radiation exposure but increase image noise, compromising diagnostic confidence. Diffusion-based generative models have shown promise for LDCT denoising by learning image priors and performing iterative refinement. In this work, we introduce AST-n, an accelerated inference framework that initiates reverse diffusion from intermediate noise levels, and integrate high-order ODE solvers within conditioned models to further reduce sampling steps. We evaluate two acceleration paradigms--AST-n sampling and standard scheduling with high-order solvers -- on the Low Dose CT Grand Challenge dataset, covering head, abdominal, and chest scans at 10-25 % of standard dose. Conditioned models using only 25 steps (AST-25) achieve peak signal-to-noise ratio (PSNR) above 38 dB and structural similarity index (SSIM) above 0.95, closely matching standard baselines while cutting inference time from ~16 seg to under 1 seg per slice. Unconditional sampling suffers substantial quality loss, underscoring the necessity of conditioning. We also assess DDIM inversion, which yields marginal PSNR gains at the cost of doubling inference time, limiting its clinical practicality. Our results demonstrate that AST-n with high-order samplers enables rapid LDCT reconstruction without significant loss of image fidelity, advancing the feasibility of diffusion-based methods in clinical workflows.

Advances in image registration and machine learning have recently enabled volumetric analysis of \emph{postmortem} brain tissue from conventional photographs of coronal slabs, which are routinely collected in brain banks and neuropathology laboratories worldwide. One caveat of this methodology is the requirement of segmentation of the tissue from photographs, which currently requires costly manual intervention. In this article, we present a deep learning model to automate this process. The automatic segmentation tool relies on a U-Net architecture that was trained with a combination of \textit{(i)}1,414 manually segmented images of both fixed and fresh tissue, from specimens with varying diagnoses, photographed at two different sites; and \textit{(ii)}~2,000 synthetic images with randomized contrast and corresponding masks generated from MRI scans for improved generalizability to unseen photographic setups. Automated model predictions on a subset of photographs not seen in training were analyzed to estimate performance compared to manual labels -- including both inter- and intra-rater variability. Our model achieved a median Dice score over 0.98, mean surface distance under 0.4~mm, and 95\% Hausdorff distance under 1.60~mm, which approaches inter-/intra-rater levels. Our tool is publicly available at surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools.