Superficial lymph node (LN) enlargement is a common ultrasonographic finding and can be associated with a broad spectrum of conditions, from benign reactive hyperplasia to malignant lymphoproliferative disorders (LPDs). LPDs, which include various hematologic malignancies affecting lymphoid tissue, present with diverse immune-morphological and clinical features, making differentiation from other malignant causes of lymphadenopathy challenging. Radiologic assessment is crucial in characterizing lymphadenopathy, with ultrasonography serving as a noninvasive and widely available imaging modality. High-resolution ultrasound allows the evaluation of key features such as LN size, shape, border definition, echogenicity, and the presence of abnormal cortical thickening, loss of the fatty hilum, or altered vascular patterns, which aid in distinguishing benign from malignant processes. This review aims to describe the ultrasonographic characteristics of lymphadenopathy, offering essential diagnostic insights to differentiate malignant disorders, particularly LPDs. We will discuss standard ultrasound techniques, including grayscale imaging and Doppler ultrasound, and explore more advanced methods such as contrast-enhanced ultrasound (CEUS), elastography, and artificial intelligence-assisted imaging, which are gaining prominence in LN evaluation. By highlighting these imaging modalities, we aim to enhance the diagnostic accuracy of ultrasonography in lymphadenopathy assessment and improve early detection of LPDs and other malignant conditions.

The location of the hemorrhagic of spontaneous intracerebral hemorrhage (sICH) is clinically pivotal for both identifying its etiology and prognosis, but comprehensive and quantitative modeling approach has yet to be thoroughly explored. We employed lesion-symptom mapping to extract the location features of sICH. We registered patients' non-contrast computed tomography image and hematoma masks with standard human brain templates to identify specific affected brain regions. Then, we generated hemorrhage probabilistic maps of different etiologies and prognoses. By integrating radiomics and clinical features into multiple logistic regression models, we developed and validated optimal etiological and prognostic models across three centers, comprising 1162 sICH patients. Hematomas of different etiology have unique spatial distributions. The location-based features demonstrated robust classification of the etiology of spontaneous intracerebral hemorrhage (sICH), with a mean area under the curve (AUC) of 0.825 across diverse datasets. These features provided significant incremental value when integrated into predictive models (fusion model mean AUC = 0.915), outperforming models relying solely on clinical features (mean AUC = 0.828). In prognostic assessments, both hematoma location (mean AUC = 0.762) and radiomic features (mean AUC = 0.837) contributed substantial incremental predictive value, as evidenced by the fusion model's mean AUC of 0.873, compared to models utilizing clinical features alone (mean AUC = 0.771). Our results show that location features were more intrinsically robust, generalizable relative, strong interpretability to the complex modeling of radiomics, our approach demonstrated a novel interpretable, streamlined, comprehensive etiologic classification and prognostic prediction framework for sICH.

As many radiology departments embark on adopting artificial intelligence (AI) solutions in their clinical practice, they face the challenge that commercial applications often do not fit with their needs. As a result, they engage in a co-creation process with technology companies to collaboratively develop and implement AI solutions. Despite its importance, the process of co-creating AI solutions is under-researched, particularly regarding the range of challenges that may occur and how medical and technological parties can monitor, assess, and guide their co-creation process through an effective collaboration framework. Drawing on the multi-case study of three co-creation projects at an academic medical center in the Netherlands, we examine how co-creation processes happen through different scenarios, depending on the extent to which the two parties engage in "resourcing," "adaptation," and "reconfiguration." We offer a relational framework that helps involved parties monitor, assess, and guide their collaborations in co-creating AI solutions. The framework allows them to discover novel use-cases and reconsider their established assumptions and practices for developing AI solutions, also for redesigning their technological systems, clinical workflow, and their legal and organizational arrangements. Using the proposed framework, we identified distinct co-creation journeys with varying outcomes, which could be mapped onto the framework to diagnose, monitor, and guide collaborations toward desired results. The outcomes of co-creation can vary widely. The proposed framework enables medical institutions and technology companies to assess challenges and make adjustments. It can assist in steering their collaboration toward desired goals. Question How can medical institutions and AI startups effectively co-create AI solutions for radiology, ensuring alignment with clinical needs while steering collaboration effectively? Findings This study provides a co-creation framework allowing assessment of project progress, stakeholder engagement, as well as guidelines for radiology departments to steer co-creation of AI. Clinical relevance By actively involving radiology professionals in AI co-creation, this study demonstrates how co-creation helps bridge the gap between clinical needs and AI development, leading to clinically relevant, user-friendly solutions that enhance the radiology workflow.

This investigation proposes a machine learning framework leveraging preoperative MRI and CT imaging data to predict postoperative complications related to distal instrumentation (DIP) in degenerative lumbar scoliosis patients undergoing long-segment fusion procedures. We retrospectively analyzed 136 patients, categorizing based on the development of DIP. Preoperative MRI and CT scans provided muscle function and bone density data, including the relative gross cross-sectional area and relative functional cross-sectional area of the multifidus, erector spinae, paraspinal extensor, psoas major muscles, the gross muscle fat index and functional muscle fat index, Hounsfield unit values of the lumbosacral region and the lower instrumented vertebra. Predictive factors for DIP were selected through stepwise LASSO regression. The filtered and all factors were incorporated into six machine learning algorithms twice, namely k-nearest neighbors, decision tree, support vector machine, random forest, multilayer perceptron (MLP), and Naïve Bayes, with tenfold cross-validation. Among patients, 16.9% developed DIP, with the multifidus' functional cross-sectional area and lumbosacral region's Hounsfield unit value as significant predictors. The MLP model exhibited superior performance when all predictive factors were input, with an average AUC of 0.98 and recall rate of 0.90. We compared various machine learning algorithms and constructed, trained, and validated predictive models based on muscle function and bone density-related variables obtained from preoperative CT and MRI, which could identify patients with high risk of DIP after long-segment spinal fusion surgery.

Sodium neuroimaging provides unique insights into the cellular and metabolic properties of brain tumors. However, at 3T, sodium neuroimaging MRI's low signal-to-noise ratio (SNR) and resolution discourages routine clinical use. We evaluated the recently developed Anatomically constrained GAN using physics-based synthetic MRI artifacts" (ATHENA) for high-resolution sodium neuroimaging of brain tumors at 3T. We hypothesized the model would improve the image quality while preserving the inherent sodium information. 4,573 proton MRI scans from 1,390 suspected brain tumor patients were used for training. Sodium and proton MRI datasets from Twenty glioma patients were collected for validation. Twenty-four image-guided biopsies from seven patients were available for sodium-proton exchanger (NHE1) expression evaluation on immunohistochemistry. High-resolution synthetic sodium images were generated using the ATHENA model, then compared to native sodium MRI and NHE1 protein expression from image-guided biopsy samples. The ATHENA produced synthetic-sodium MR with significantly improved SNR (native SNR 18.20 ± 7.04; synthetic SNR 23.83 ± 9.33, P = 0.0079). The synthetic-sodium values were consistent with the native measurements (P = 0.2058), with a strong linear correlation within contrast-enhancing areas of the tumor (R² = 0.7565, P = 0.0005), T2-hyperintense (R² = 0.7325, P < 0.0001), and necrotic areas (R² = 0.7678, P < 0.0001). The synthetic-sodium MR and the relative NHE1 expression from image-guided biopsies were better correlated for the synthetic (ρ = 0.3269, P < 0.0001) than the native (ρ = 0.1732, P = 0.0276) with higher sodium signal in samples expressing elevated NHE1 (P < 0.0001). ATHENA generates high-resolution synthetic-sodium MRI at 3T, enabling clinically attainable multinuclear imaging for brain tumors that retain the inherent information from the native sodium. The resulting synthetic sodium significantly correlates with tissue expression, potentially supporting its utility as a non-invasive marker of underlying sodium homeostasis in brain tumors.

To investigate the effect of contrast enhancement on the diagnosis of interstitial lung abnormalities (ILA) in automatic quantitative CT measurement in patients with paired pre- and post-contrast scans. Patients who underwent chest CT for thoracic surgery between April 2017 and December 2020 were retrospectively analyzed. ILA quantification was performed using deep learning-based automated software. Cases were categorized as ILA or non-ILA according to the Fleischner Society's definition, based on the quantification results or radiologist assessment (reference standard). Measurement variability, agreement, and diagnostic performance between the pre- and post-contrast scans were evaluated. In 1134 included patients, post-contrast scans quantified a slightly larger volume of nonfibrotic ILA (mean difference: -0.2%), due to increased ground-glass opacity and reticulation volumes (-0.2% and -0.1%), whereas the fibrotic ILA volume remained unchanged (0.0%). ILA was diagnosed in 15 (1.3%), 22 (1.9%), and 40 (3.5%) patients by pre- and post-contrast scans and radiologists, respectively. The agreement between the pre- and post-contrast scans was substantial (κ = 0.75), but both pre-contrast (κ = 0.46) and post-contrast (κ = 0.54) scans demonstrated moderate agreement with the radiologist. The sensitivity for ILA (32.5% vs. 42.5%, p = 0.221) and specificity for non-ILA (99.8% vs. 99.5%, p = 0.248) were comparable between pre- and post-contrast scans. Radiologist's reclassification for equivocal ILA due to unilateral abnormalities increased the sensitivity for ILA (67.5% and 75.0%, respectively) in both pre- and post-contrast scans. Applying automated quantification on post-contrast scans appears to be acceptable in terms of agreement and diagnostic performance; however, radiologists may need to improve sensitivity reclassifying equivocal ILA. Question The effect of contrast enhancement on the automated quantification of interstitial lung abnormality (ILA) remains unknown. Findings Automated quantification measured slightly larger ground-glass opacity and reticulation volumes on post-contrast scans than on pre-contrast scans; however, contrast enhancement did not affect the sensitivity for interstitial lung abnormality. Clinical relevance Applying automated quantification on post-contrast scans appears to be acceptable in terms of agreement and diagnostic performance.

To develop and validate a deep-learning-based automatic method for vessel walls and atherosclerotic plaques segmentation for quantitative evaluation in MR vessel wall images. A total of 193 patients (107 patients for training and validation, 39 patients for internal test, 47 patients for external test) with atherosclerotic plaque from five centers underwent T1-weighted MRI scans and were included in the dataset. The first step of the proposed method was constructing a purely learning-based convolutional neural network (CNN) named Vessel-SegNet to segment the lumen and the vessel wall. The second step is using the vessel wall priors (including manual prior and Tversky-loss-based automatic prior) to improve the plaque segmentation, which utilizes the morphological similarity between the vessel wall and the plaque. The Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), etc., were used to evaluate the similarity, agreement, and correlations. Most of the DSCs for lumen and vessel wall segmentation were above 90%. The introduction of vessel wall priors can increase the DSC for plaque segmentation by over 10%, reaching 88.45%. Compared to dice-loss-based vessel wall priors, the Tversky-loss-based priors can further improve DSC by nearly 3%, reaching 82.84%. Most of the ICC values between the Vessel-SegNet and manual methods in the 6 quantitative measurements are greater than 85% (p-value < 0.001). The proposed CNN-based segmentation model can quickly and accurately segment vessel walls and plaques for quantitative evaluation. Due to the lack of testing with other equipment, populations, and anatomical studies, the reliability of the research results still requires further exploration. Question How can the accuracy and efficiency of vessel component segmentation for quantification, including the lumen, vessel wall, and plaque, be improved? Findings Improved CNN models, manual/automatic vessel wall priors, and Tversky loss can improve the performance of semi-automatic/automatic vessel components segmentation for quantification. Clinical relevance Manual segmentation of vessel components is a time-consuming yet important process. Rapid and accurate segmentation of the lumen, vessel walls, and plaques for quantification assessment helps patients obtain more accurate, efficient, and timely stroke risk assessments and clinical recommendations.

This study aims to investigate whether non-contrast computed tomography radiomics can effectively reflect split renal function and to develop a radiomics model for its assessment. This retrospective study included kidneys from the study center and split them into training (70%) and testing (30%) sets. Renal dynamic imaging was used as the reference standard for measuring split renal function. Based on chronic kidney disease staging, kidneys were categorized into three groups according to glomerular filtration rate: > 45 ml/min/1.73 m², 30-45 ml/min/1.73 m², and < 30 ml/min/1.73 m².Features were selected based on feature importance ranking from a tree model, and a random forest radiomics model was built. A total of 543 kidneys were included, with 381 in the training set and 162 in the testing set. In the training set, 16 features identified as most important for distinguishing between the groups were ultimately included to develop the random forest model. The model demonstrated good discriminatory ability in the testing set. The AUC for the > 45 ml/min/1.73 m², 30-45 ml/min/1.73 m², and < 30 ml/min/1.73 m² categories were 0.859 (95% CI 0.804-0.910), 0.679 (95% CI 0.589-0.760), and 0.901 (95% CI 0.848-0.946), respectively. The calibration curves for the kidneys in each group closely align with the diagonal, with Hosmer-Lemeshow test P-values of 0.124, 0.241, and 0.199 for the three groups, respectively (all P > 0.05). The decision curve analysis confirmed the radiomics model's clinical utility, demonstrating significantly higher net benefit than both treat-all and treat-none strategies at clinically relevant probability thresholds: 1-69% and 71-75% for the > 45 ml/min/1.73 m² group, 15-d50% for the 30-45 ml/min/1.73 m² group, and 0-99% for the < 30 ml/min/1.73 m² group. Non-contrast computed tomography radiomics can effectively reflect split renal function information, and the model developed based on it can accurately assess split renal function, holding great potential for clinical application.

Retrospective study. To develop and validate a computed tomography-based deep learning(DL) model for diagnosing cervical spinal stenosis(CSS). Although magnetic resonance imaging (MRI) is widely used for diagnosing CSS, its inherent limitations, including prolonged scanning time, limited availability in resource-constrained settings, and contraindications for patients with metallic implants, make computed tomography (CT) a critical alternative in specific clinical scenarios. The development of CT-based DL models for CSS detection holds promise in transcending the diagnostic efficacy limitations of conventional CT imaging, thereby serving as an intelligent auxiliary tool to optimize healthcare resource allocation. Paired CT/MRI images were collected. CT images were divided into training, validation, and test sets in an 8:1:1 ratio. The two-stage model architecture employed: (1) a Faster R-CNN-based detection model for localization, annotation, and extraction of regions of interest (ROI); (2) comparison of 16 convolutional neural network (CNN) models for stenosis classification to select the best-performing model. The evaluation metrics included accuracy, F1-score, and Cohen's κ coefficient, with comparisons made against diagnostic results from physicians with varying years of experience. In the multiclass classification task, four high-performing models (DL1-b0, DL2-121, DL3-101, and DL4-26d) achieved accuracies of 88.74%, 89.40%, 89.40%, and 88.08%, respectively. All models demonstrated >80% consistency with senior physicians and >70% consistency with junior physicians.In the binary classification task, the models achieved accuracies of 94.70%, 96.03%, 96.03%, and 94.70%, respectively. All four models demonstrated consistency rates slightly below 90% with junior physicians. However, when compared with senior physicians, three models (excluding DL4-26d) exhibited consistency rates exceeding 90%. The DL model developed in this study demonstrated high accuracy in CT image analysis of CSS, with a diagnostic performance comparable to that of senior physicians.

Evaluating the minimum distance (dTICA) between the internal carotid artery (ICA) and tonsillar tumors (TT) on imaging is essential for preoperative planning; we propose a tool to automatically extract dTICA. CT scans of 96 patients with TT were selected from the cancer imaging archive. nnU-Net, a deep learning framework, was implemented to automatically segment both the TT and ICA from these scans. Dice similarity coefficient (DSC) and average hausdorff distance (AHD) were used to evaluate the performance of the nnU-Net. Thereafter, an automated tool was built to calculate the magnitude of dTICA from these segmentations. The average DSC and AHD were 0.67, 2.44 mm, and 0.83, 0.49 mm for the TT and ICA, respectively. The mean dTICA was 6.66 mm and statistically varied by tumor T stage (p = 0.00456). The proposed pipeline can accurately and automatically capture dTICA, potentially assisting clinicians in preoperative evaluation.