Carotid artery calcifications are important markers of cardiovascular health, often associated with atherosclerosis and a higher risk of stroke. Recent research shows that dental radiographs can help identify these calcifications, allowing for earlier detection of vascular diseases. Advances in artificial intelligence (AI) have improved the ability to detect carotid calcifications in dental images, making it a useful screening tool. This systematic review and meta-analysis aimed to evaluate how accurately AI methods can identify carotid calcifications in dental radiographs. A systematic search in databases including PubMed, Scopus, Embase, and Web of Science for studies on AI algorithms used to detect carotid calcifications in dental radiographs was conducted. Two independent reviewers collected data on study aims, imaging techniques, and statistical measures such as sensitivity and specificity. A meta-analysis using random effects was performed, and the risk of bias was evaluated with the QUADAS-2 tool. Nine studies were suitable for qualitative analysis, while five provided data for quantitative analysis. These studies assessed AI algorithms using cone beam computed tomography (n = 3) and panoramic radiographs (n = 6). The sensitivity of the included studies ranged from 0.67 to 0.98 and specificity varied between 0.85 and 0.99. The overall effect size, by considering only one AI method in each study, resulted in a sensitivity of 0.92 [95% CI 0.81 to 0.97] and a specificity of 0.96 [95% CI 0.92 to 0.97]. The high sensitivity and specificity indicate that AI methods could be effective screening tools, enhancing the early detection of stroke and related cardiovascular risks. Not applicable.

In the prognosis of breast cancer, the status of axillary lymph nodes (ALN) is critically important. While traditional axillary lymph node dissection (ALND) provides comprehensive information, it is associated with high risks. Sentinel lymph node biopsy (SLND), as an alternative, is less invasive but still poses a risk of overtreatment. In recent years, digital breast tomosynthesis (DBT) technology has emerged as a new precise diagnostic tool for breast cancer, leveraging its high detection capability for lesions obscured by dense glandular tissue. This multi-center study evaluates the feasibility of preoperative DBT-based radiomics, using tumor and peritumoral features, to predict ALN metastasis in breast cancer. We retrospectively collected DBT imaging data from 536 preoperative breast cancer patients across two centers. Specifically, 390 cases were from one Hospital, and 146 cases were from another Hospital. These data were assigned to internal training and external validation sets, respectively. We performed 3D region of interest (ROI) delineation on the cranio-caudal (CC) and mediolateral oblique (MLO) views of DBT images and extracted radiomic features. Using methods such as analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO), we selected radiomic features extracted from the tumor and its surrounding 3 mm, 5 mm, and 10 mm regions, and constructed a radiomic feature set. We then developed a combined model that includes the optimal radiomic features and clinical pathological factors. The performance of the combined model was evaluated using the area under the curve (AUC), and it was directly compared with the diagnostic results of radiologists. The results showed that the AUC of the radiomic features from the surrounding regions of the tumor were generally lower than those from the tumor itself. Among them, the Signature_{tuomor+10 mm} model performed best, achieving an AUC of 0.806 using a logistic regression (LR) classifier to generate the RadScore.The nomogram incorporating both Ki67 and RadScore demonstrated a slightly higher AUC (0.813) compared to the Signature_{tuomor+10 mm} model alone (0.806). By integrating relevant clinical information, the nomogram enhances potential clinical utility. Moreover, it outperformed radiologists' assessments in predictive accuracy, highlighting its added value in clinical decision-making. Radiomics based on DBT imaging of the tumor and surrounding regions can provide a non-invasive auxiliary tool to guide treatment strategies for ALN metastasis in breast cancer. Not applicable.

To develop and validate a deep learning radiomics fusion model (DLR) based on ultrasound (US) images to identify bone erosion in rheumatoid arthritis (RA) patients. A total of 432 patients with RA at two institutions were collected. Three hundred twelve patients from center 1 were randomly divided into a training set (N = 218) and an internal test set (N = 94) in a 7:3 ratio; meanwhile, 124 patients from center 2 were as an external test set. Radiomics (Rad) and deep learning (DL) features were extracted based on hand-crafted radiomics and deep transfer learning networks. The least absolute shrinkage and selection operator regression was employed to establish DLR fusion feature from the Rad and DL features. Subsequently, 10 machine learning algorithms were used to construct models and the final optimal model was selected. The performance of models was evaluated using receiver operating characteristic (ROC) and decision curve analysis (DCA). The diagnostic efficacy of sonographers was compared with and without the assistance of the optimal model. LR was chosen as the optimal algorithm for model construction account for superior performance (Rad/DL/DLR: area under the curve [AUC] = 0.906/0.974/0.979) in the training set. In the internal test set, DLR_LR as the final model had the highest AUC (AUC = 0.966), which was also validated in the external test set (AUC = 0.932). With the aid of DLR_LR model, the overall performance of both junior and senior sonographers improved significantly (P < 0.05), and there was no significant difference between the junior sonographer with DLR_LR model assistance and the senior sonographer without assistance (P > 0.05). DLR model based on US images is the best performer and is expected to become an important tool for identifying bone erosion in RA patients. Key Points • DLR model based on US images is the best performer in identifying BE in RA patients. • DLR model may assist the sonographers to improve the accuracy of BE evaluations.

To verify the capability of the Segment Anything Model for medical images in 3D (SAM-Med3D), tailored with low-rank adaptation (LoRA) strategies, in segmenting breast tumors in Automated Breast Ultrasound (ABUS) images. This retrospective study collected data from 329 patients diagnosed with breast cancer (average age 54 years). The dataset was randomly divided into training (n = 204), validation (n = 29), and test sets (n = 59). Two experienced radiologists manually annotated the regions of interest of each sample in the dataset, which served as ground truth for training and evaluating the SAM-Med3D model with additional customized modules. For semi-automatic tumor segmentation, points were randomly sampled within the lesion areas to simulate the radiologists' clicks in real-world scenarios. The segmentation performance was evaluated using the Dice coefficient. A total of 492 cases (200 from the "Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound (TDSC-ABUS) 2023 challenge") were subjected to semi-automatic segmentation inference. The average Dice Similariy Coefficient (DSC) scores for the training, validation, and test sets of the Lishui dataset were 0.75, 0.78, and 0.75, respectively. The Breast Imaging Reporting and Data System (BI-RADS) categories of all samples range from BI-RADS 3 to 6, yielding an average DSC coefficient between 0.73 and 0.77. By categorizing the samples (lesion volumes ranging from 1.64 to 100.03 cm³) based on lesion size, the average DSC falls between 0.72 and 0.77.And the overall average DSC for the TDSC-ABUS 2023 challenge dataset was 0.79, with the test set achieving a sora-of-art scores of 0.79. The SAM-Med3D model with additional customized modules demonstrates good performance in semi-automatic 3D ABUS breast cancer tumor segmentation, indicating its feasibility for application in computer-aided diagnosis systems.

Cone-Beam Computed Tomography (CBCT) is essential in medical imaging, and the Feldkamp-Davis-Kress (FDK) algorithm is a popular choice for reconstruction due to its efficiency. However, FDK is susceptible to noise and artifacts. While recent deep learning methods offer improved image quality, they often increase computational complexity and lack the interpretability of traditional methods. In this paper, we introduce an enhanced FDK-based neural network that maintains the classical algorithm's interpretability by selectively integrating trainable elements into the cosine weighting and filtering stages. Recognizing the challenge of a large parameter space inherent in 3D CBCT data, we leverage wavelet transformations to create sparse representations of the cosine weights and filters. This strategic sparsification reduces the parameter count by $93.75\%$ without compromising performance, accelerates convergence, and importantly, maintains the inference computational cost equivalent to the classical FDK algorithm. Our method not only ensures volumetric consistency and boosts robustness to noise, but is also designed for straightforward integration into existing CT reconstruction pipelines. This presents a pragmatic enhancement that can benefit clinical applications, particularly in environments with computational limitations.

This study aims to develop and validate a multiregional radiomics model to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC), and further evaluate the performance of the model in different specific subgroups (N2 stage and anti-PD-1/PD-L1). 216 patients with NSCLC who underwent neoadjuvant chemoimmunotherapy followed by surgical intervention were included and assigned to training and validation sets randomly. From pre-treatment baseline CT, one intratumoral (T) and two peritumoral regions (P₃: 0-3 mm; P₆: 0-6 mm) were extracted. Five radiomics models were developed using machine learning algorithms to predict pCR, utilizing selected features from intratumoral (T), peritumoral (P₃, P₆), and combined intra- and peritumoral regions (T + P₃, T + P₆). Additionally, the predictive efficacy of the optimal model was specifically assessed for patients in the N2 stage and anti-PD-1/PD-L1 subgroups. A total of 51.4 % (111/216) of patients exhibited pCR following neoadjuvant chemoimmunotherapy. Multivariable analysis identified that only the T + P₃ radiomics signature served as independent predictor of pCR (P < 0.001). The multiregional radiomics model (T + P₃) exhibited superior predictive performance for pCR, achieving an area under the curve (AUC) of 0.75 in the validation cohort. Furthermore, this multiregional model maintained robust predictive accuracy in both N2 stage and anti-PD-1/PD-L1 subgroups, with an AUC of 0.829 and 0.833, respectively. The proposed multiregional radiomics model showed potential in predicting pCR in NSCLC after neoadjuvant chemoimmunotherapy, and demonstrated good predictive performance in different specific subgroups. This capability may assist clinicians in identifying suitable candidates for neoadjuvant chemoimmunotherapy and promote the advancement in precision therapy.

To develop and validate a deep learning system with guided diffusion-based data augmentation for grading partial-thickness supraspinatus tendon (SST) tears and to compare its performance with experienced radiologists, including external validation. This retrospective study included 1150 patients with arthroscopically confirmed SST tears, divided into a training set (741 patients), validation set (185 patients), and internal test set (185 patients). An independent external test set of 224 patients was used for generalizability assessment. To address data imbalance, MRI images were augmented using a guided diffusion model. A ResNet-34 model was employed for Ellman grading of bursal-sided and articular-sided partial-thickness tears across different MRI sequences (oblique coronal [OCOR], oblique sagittal [OSAG], and combined OCOR+OSAG). Performance was evaluated using AUC and precision-recall curves, and compared to three experienced musculoskeletal (MSK) radiologists. The DeLong test was used to compare performance across different sequence combinations. A total of 26,020 OCOR images and 26,356 OSAG images were generated using the guided diffusion model. For bursal-sided partial-thickness tears in the internal dataset, the model achieved AUCs of 0.99, 0.98, and 0.97 for OCOR, OSAG, and combined sequences, respectively, while for articular-sided tears, AUCs were 0.99, 0.99, and 0.99. The DeLong test showed no significant differences among sequence combinations (P=0.17, 0.14, 0.07). In the external dataset, the combined-sequence model achieved AUCs of 0.99, 0.97, and 0.97 for bursal-sided tears and 0.99, 0.95, and 0.95 for articular-sided tears. Radiologists demonstrated an ICC of 0.99, but their grading performance was significantly lower than the ResNet-34 model (P<0.001). The deep learning system improved grading consistency and significantly reduced evaluation time, while guided diffusion augmentation enhanced model robustness. The proposed deep learning system provides a reliable and efficient method for grading partial-thickness SST tears, achieving radiologist-level accuracy with greater consistency and faster evaluation speed.

Low-dose colchicine (0.5 mg once daily) reduces the risk of major cardiovascular events in coronary disease, but its mechanism of action is not yet fully understood. We investigated whether low-dose colchicine is associated with changes in pericoronary inflammation and plaque composition in patients with chronic coronary disease. We performed a cross-sectional, nationwide, subanalysis of the Low-Dose Colchicine 2 Trial (LoDoCo2, n=5522). CT angiography studies were performed in 151 participants randomised to colchicine or placebo coronary after a median treatment duration of 28.2 months. Pericoronary adipose tissue (PCAT) attenuation measurements around proximal coronary artery segments and quantitative plaque analysis for the entire coronary tree were performed using artificial intelligence-enabled plaque analysis software. Median PCAT attenuation was not significantly different between the two groups (-79.5 Hounsfield units (HU) for colchicine versus -78.7 HU for placebo, p=0.236). Participants assigned to colchicine had a higher volume (169.6 mm³ vs 113.1 mm³, p=0.041) and burden (9.6% vs 7.0%, p=0.035) of calcified plaque, and higher volume of dense calcified plaque (192.8 mm³ vs 144.3 mm³, p=0.048) compared with placebo, independent of statin therapy. Colchicine treatment was associated with a lower burden of low-attenuation plaque in participants on a low-intensity statin, but not in those on a high-intensity statin (p_interaction=0.037). Pericoronary inflammation did not differ among participants who received low-dose colchicine compared with placebo. Low-dose colchicine was associated with a higher volume of calcified plaque, particularly dense calcified plaque, which is considered a feature of plaque stability.

This retrospective study evaluates the diagnostic performance of an optimized comprehensive multi-stage framework based on the Segment Anything Model (SAM), which we named Dr-SAM, for detecting and grading vascular stenosis in the abdominal aorta and iliac arteries using digital subtraction angiography (DSA). A total of 100 DSA examinations were conducted on 100 patients. The infrarenal abdominal aorta (AAI), common iliac arteries (CIA), and external iliac arteries (EIA) were independently evaluated by two experienced radiologists using a standardized 5-point grading scale. Dr-SAM analyzed the same DSA images, and its assessments were compared with the average stenosis grading provided by the radiologists. Diagnostic accuracy was evaluated using Cohen's kappa, specificity, sensitivity, and Wilcoxon signed-rank tests. Interobserver agreement between radiologists, which established the reference standard, was strong (Cohen's kappa: CIA right = 0.95, CIA left = 0.94, EIA right = 0.98, EIA left = 0.98, AAI = 0.79). Dr-SAM showed high agreement with radiologist consensus for CIA (κ = 0.93 right, 0.91 left), moderate agreement for EIA (κ = 0.79 right, 0.76 left), and fair agreement for AAI (κ = 0.70). Dr-SAM demonstrated excellent specificity (up to 1.0) and robust sensitivity (0.67-0.83). Wilcoxon tests revealed no significant differences between Dr-SAM and radiologist grading (p > 0.05). Dr-SAM proved to be an accurate and efficient tool for vascular assessment, with the potential to streamline diagnostic workflows and reduce variability in stenosis grading. Its ability to deliver rapid and consistent evaluations may contribute to earlier detection of disease and the optimization of treatment strategies. Further studies are needed to confirm these findings in prospective settings and to enhance its capabilities, particularly in the detection of occlusions.

Accurately registering breast MR images from different time points enables the alignment of anatomical structures and tracking of tumor progression, supporting more effective breast cancer detection, diagnosis, and treatment planning. However, the complexity of dense tissue and its highly non-rigid nature pose challenges for conventional registration methods, which primarily focus on aligning general structures while overlooking intricate internal details. To address this, we propose \textbf{GuidedMorph}, a novel two-stage registration framework designed to better align dense tissue. In addition to a single-scale network for global structure alignment, we introduce a framework that utilizes dense tissue information to track breast movement. The learned transformation fields are fused by introducing the Dual Spatial Transformer Network (DSTN), improving overall alignment accuracy. A novel warping method based on the Euclidean distance transform (EDT) is also proposed to accurately warp the registered dense tissue and breast masks, preserving fine structural details during deformation. The framework supports paradigms that require external segmentation models and with image data only. It also operates effectively with the VoxelMorph and TransMorph backbones, offering a versatile solution for breast registration. We validate our method on ISPY2 and internal dataset, demonstrating superior performance in dense tissue, overall breast alignment, and breast structural similarity index measure (SSIM), with notable improvements by over 13.01% in dense tissue Dice, 3.13% in breast Dice, and 1.21% in breast SSIM compared to the best learning-based baseline.