Percutaneous transluminal angioplasty and stenting (PTAS) in patients with carotid stenosis may have potential effects on brain parenchyma. However, current studies on parenchymal changes are scarce due to the need for advanced imaging modalities. Consequently, the alterations in brain parenchyma following PTAS remain an unsolved issue. To investigate changes to the brain parenchyma using magnetic resonance elastography (MRE). Prospective. 13 patients (6 women and 7 men; 39 MRI imaging sessions) with severe unilateral carotid stenosis patients indicated for PTAS were recruited between 2021 and 2024. Noncontrast MRI sequences including MRE (spin echo) were acquired using 3 T scanners. All patients underwent MRE before (preprocedural), within 24 h (early postprocedural) and 3 months after (delayed postprocedural) PTAS. Preprocedural and delayed postprocedural ultrasonographic peak systolic velocity (PSV) was recorded. MRE stiffness and damping ratio were evaluated via neural network inversion of the whole brain, in 14 gray matter (GM) and 12 white matter (WM) regions. Stiffness and damping ratio differences between each pair of MR sessions for each subject were identified by paired sample t tests. The correlations of stiffness and damping ratio with stenosis grade and ultrasonographic PSV dynamics were evaluated by Pearson correlation coefficients. The statistical significance was defined as p < 0.05. The stiffness of lesion side insula, deep GM, and deep WM increased significantly from preprocedural to delayed postprocedural MRE. Increasing deep GM stiffness on the lesion side was positively correlated with the DSA stenosis grade significantly (r = 0.609). The lesion side insula stiffness increments were positively correlated with PSV decrements significantly (r = 0.664). Regional brain stiffness increased 3 months after PTAS. Lesion side stiffness was positively correlated with stenosis grades in deep GM and PSV decrements in the insula. EVIDENCE LEVEL: 2. Stage 2.

Thyroid incidentalomas (TIs) are incidental thyroid lesions detected on fluorodeoxy-d-glucose (18F-FDG) PET/computed tomography (PET/CT) scans. This study aims to investigate the role of noninvasive PET/CT-derived radiomic features in characterizing 18F-FDG PET/CT TIs and distinguishing benign from malignant thyroid lesions in oncological patients. We included 46 patients with PET/CT TIs who underwent thyroid ultrasound and thyroid surgery at our oncological referral hospital. Radiomic features extracted from regions of interest (ROI) in both PET and CT images and analyzed for their association with thyroid cancer and their predictive ability. The TIs were graded using the ultrasound TIRADS classification, and histopathological results served as the reference standard. Univariate and multivariate analyses were performed using features from each modality individually and combined. The performance of radiomic features was compared to the TIRADS classification. Among the 46 included patients, 36 patients (78%) had malignant thyroid lesions, while 10 patients (22%) had benign lesions. The combined run length nonuniformity radiomic feature from PET and CT cubical ROIs demonstrated the highest area under the curve (AUC) of 0.88 (P < 0.05), with a negative correlation with malignancy. This performance was comparable to the TIRADS classification (AUC: 0.84, P < 0.05), which showed a positive correlation with thyroid cancer. Multivariate analysis showed higher predictive performance using CT-derived radiomics (AUC: 0.86 ± 0.13) compared to TIRADS (AUC: 0.80 ± 0.08). This study highlights the potential of 18F-FDG PET/CT-derived radiomics to distinguish benign from malignant thyroid lesions. Further studies with larger cohorts and deep learning-based methods could obtain more robust results.

Medical image segmentation is a complex and challenging task, which aims to accurately segment various structures or abnormal regions in medical images. However, obtaining accurate segmentation results is difficult because of the great uncertainty in the shape, location, and scale of the target region. To address these challenges, we propose a higher-order spatial interaction framework with dual cross global efficient attention (DGEAHorNet), which employs a neural network architecture based on recursive gate convolution to adequately extract multi-scale contextual information from images. Specifically, a Dual Cross-Attentions (DCA) is added to the skip connection that can effectively blend multi-stage encoder features and narrow the semantic gap. In the bottleneck stage, global channel spatial attention module (GCSAM) is used to extract image global information. To obtain better feature representation, we feed the output from the GCSAM into the multi-branch dense layer (SENetV2) for excitation. Furthermore, we adopt Depthwise Over-parameterized Convolutional Layer (DO-Conv) in order to replace the common convolutional layer in the input and output part of our network, then add Efficient Attention (EA) to diminish computational complexity and enhance our model's performance. For evaluating the effectiveness of our proposed DGEAHorNet, we conduct comprehensive experiments on four publicly-available datasets, and achieving 0.9320, 0.9337, 0.9312 and 0.7799 in Dice similarity coefficient on ISIC2018, ISIC2017, CVC-ClinicDB and HRF respectively. Our results show that DGEAHorNet has better performance compared with advanced methods. The code is publicly available at https://github.com/penghaixin/mymodel .

Contrast-enhanced magnetic resonance imaging (CE-MRI) monitoring across multiple time points is critical for optimizing hepatocellular carcinoma (HCC) prognosis during transarterial chemoembolization (TACE) treatment. The aim of this retrospective study is to develop and validate an artificial intelligence (AI)-powered models utilizing multi-time-point arterial phase CE-MRI data for HCC prognosis stratification in TACE patients. A total of 543 individual arterial phase CE-MRI scans from 181 HCC patients were retrospectively collected in this study. All patients underwent TACE and longitudinal arterial phase CE-MRI assessments at three time points: prior to treatment, and following the first and second TACE sessions. Among them, 110 patients received TACE monotherapy, while the remaining 71 patients underwent TACE in combination with microwave ablation (MWA). All images were subjected to standardized preprocessing procedures. We developed an end-to-end deep learning model, ProgSwin-UNETR, based on the Swin Transformer architecture, to perform four-class prognosis stratification directly from input imaging data. The model was trained using multi-time-point arterial phase CE-MRI data and evaluated via fourfold cross-validation. Classification performance was assessed using the area under the receiver operating characteristic curve (AUC). For comparative analysis, we benchmarked performance against traditional radiomics-based classifiers and the mRECIST criteria. Prognostic utility was further assessed using Kaplan-Meier (KM) survival curves. Additionally, multivariate Cox proportional hazards regression was performed as a post hoc analysis to evaluate the independent and complementary prognostic value of the model outputs and clinical variables. GradCAM +  + was applied to visualize the imaging regions contributing most to model prediction. The ProgSwin-UNETR model achieved an accuracy of 0.86 and an AUC of 0.92 (95% CI: 0.90-0.95) for the four-class prognosis stratification task, outperforming radiomic models across all risk groups. Furthermore, KM survival analyses were performed using three different approaches-AI model, radiomics-based classifiers, and mRECIST criteria-to stratify patients by risk. Of the three approaches, only the AI-based ProgSwin-UNETR model achieved statistically significant risk stratification across the entire cohort and in both TACE-alone and TACE + MWA subgroups (p < 0.005). In contrast, the mRECIST and radiomics models did not yield significant survival differences across subgroups (p > 0.05). Multivariate Cox regression analysis further demonstrated that the model was a robust independent prognostic factor (p = 0.01), effectively stratifying patients into four distinct risk groups (Class 0 to Class 3) with Log(HR) values of 0.97, 0.51, -0.53, and -0.92, respectively. Additionally, GradCAM +  + visualizations highlighted critical regional features contributing to prognosis prediction, providing interpretability of the model. ProgSwin-UNETR can well predict the various risk groups of HCC patients undergoing TACE therapy and can further be applied for personalized prediction.

To compare the diagnostic performance of deep learning reconstruction (DLR) of zero echo time (ZTE) MRI for structural lesions in patients with axial spondyloarthritis, against T1WI and ZTE MRI without DLR, using CT as the reference standard. From February 2021 to December 2022, 26 patients (52 sacroiliac joints (SIJ) and 104 quadrants) underwent SIJ MRIs. Three readers assessed overall image quality and structural conspicuity, scoring SIJs for structural lesions on T1WI, ZTE, and ZTE DLR 50%, 75%, and 100%, respectively. Diagnostic performance was evaluated using CT as the reference standard, and inter-reader agreement was assessed using weighted kappa. ZTE DLR 100% showed the highest image quality scores for readers 1 and 2, and the best structural conspicuity scores for all three readers. In readers 2 and 3, ZTE DLR 75% showed the best diagnostic performance for bone sclerosis, outperforming T1WI and ZTE (all p < 0.05). In all readers, ZTE DLR 100% showed superior diagnostic performance for bone erosion compared to T1WI and ZTE (all p < 0.01). For bone sclerosis, ZTE DLR 50% showed the highest kappa coefficients between readers 1 and 2 and between readers 1 and 3. For bone erosion, ZTE DLR 100% showed the highest kappa coefficients between readers. ZTE MRI with DLR outperformed T1WI and ZTE MRI without DLR in diagnosing bone sclerosis and erosion of the SIJ, while offering similar subjective image quality and structural conspicuity. Question With zero echo time (ZTE) alone, small structural lesions, such as bone sclerosis and erosion, are challenging to confirm in axial spondyloarthritis. Findings ZTE deep learning reconstruction (DLR) showed higher diagnostic performance for detecting bone sclerosis and erosion, compared with T1WI and ZTE. Clinical relevance Applying DLR to ZTE enhances diagnostic capability for detecting bone sclerosis and erosion in the sacroiliac joint, aiding in the early diagnosis of axial spondyloarthritis.

A chest X-ray radiology report describes abnormal findings not only from X-ray obtained at a given examination, but also findings on disease progression or change in device placement with reference to the X-ray from previous examination. Majority of the efforts on automatic generation of radiology report pertain to reporting the former, but not the latter, type of findings. To the best of the authors' knowledge, there is only one work dedicated to generating summary of the latter findings, i.e., follow-up radiology report summary. In this study, we propose a transformer-based framework to tackle this task. Motivated by our observations on the significance of medical lexicon on the fidelity of report summary generation, we introduce two mechanisms to bestow clinical insight to our model, namely disease probability soft guidance and masked entity modeling loss. The former mechanism employs a pretrained abnormality classifier to guide the presence level of specific abnormalities, while the latter directs the model's attention toward medical lexicon. Extensive experiments were conducted to demonstrate that the performance of our model exceeded the state-of-the-art.

To assess changes in peer-reviewed evidence on commercially available radiological artificial intelligence (AI) products from 2020 to 2023, as a follow-up to a 2020 review of 100 products. A literature review was conducted, covering January 2015 to March 2023, focusing on CE-certified radiological AI products listed on www.healthairegister.com . Papers were categorised using the hierarchical model of efficacy: technical/diagnostic accuracy (levels 1-2), clinical decision-making and patient outcomes (levels 3-5), or socio-economic impact (level 6). Study features such as design, vendor independence, and multicentre/multinational data usage were also examined. By 2023, 173 CE-certified AI products from 90 vendors were identified, compared to 100 products in 2020. Products with peer-reviewed evidence increased from 36% to 66%, supported by 639 papers (up from 237). Diagnostic accuracy studies (level 2) remained predominant, though their share decreased from 65% to 57%. Studies addressing higher-efficacy levels (3-6) remained constant at 22% and 24%, with the number of products supported by such evidence increasing from 18% to 31%. Multicentre studies rose from 30% to 41% (p < 0.01). However, vendor-independent studies decreased (49% to 45%), as did multinational studies (15% to 11%) and prospective designs (19% to 16%), all with p > 0.05. The increase in peer-reviewed evidence and higher levels of evidence per product indicate maturation in the radiological AI market. However, the continued focus on lower-efficacy studies and reductions in vendor independence, multinational data, and prospective designs highlight persistent challenges in establishing unbiased, real-world evidence. Question Evaluating advancements in peer-reviewed evidence for CE-certified radiological AI products is crucial to understand their clinical adoption and impact. Findings CE-certified AI products with peer-reviewed evidence increased from 36% in 2020 to 66% in 2023, but the proportion of higher-level evidence papers (~24%) remained unchanged. Clinical relevance The study highlights increased validation of radiological AI products but underscores a continued lack of evidence on their clinical and socio-economic impact, which may limit these tools' safe and effective implementation into clinical workflows.

This study integrates ultrasound Radiomics with clinical data to enhance the diagnostic accuracy of HER-2 expression status in breast cancer, aiming to provide more reliable treatment strategies for this aggressive disease. We included ultrasound images and clinicopathologic data from 210 female breast cancer patients, employing a Generative Adversarial Network (GAN) to enhance image clarity and segment the region of interest (ROI) for Radiomics feature extraction. Features were optimized through Z-score normalization and various statistical methods. We constructed and compared multiple machine learning models, including Linear Regression, Random Forest, and XGBoost, with deep learning models such as CNNs (ResNet101, VGG19) and Transformer technology. The Grad-CAM technique was used to visualize the decision-making process of the deep learning models. The Deep Learning Radiomics (DLR) model integrated Radiomics features with deep learning features, and a combined model further integrated clinical features to predict HER-2 status. The LightGBM and ResNet101 models showed high performance, but the combined model achieved the highest AUC values in both training and testing, demonstrating the effectiveness of integrating diverse data sources. The study successfully demonstrates that the fusion of deep learning with Radiomics analysis significantly improves the prediction accuracy of HER-2 status, offering a new strategy for personalized breast cancer treatment and prognostic assessments.

Endometriosis affects approximately 190 million females of reproductive age worldwide. Magnetic Resonance Imaging (MRI) has been recommended as the primary non-invasive diagnostic method for endometriosis. This study presents new female pelvic MRI multicenter datasets for endometriosis and shows the baseline segmentation performance of two auto-segmentation pipelines: the self-configuring nnU-Net and RAovSeg, a custom network. The multi-sequence endometriosis MRI scans from two clinical institutions were collected. A multicenter dataset of 51 subjects with manual labels for multiple pelvic structures from three raters was used to assess interrater agreement. A second single-center dataset of 81 subjects with labels for multiple pelvic structures from one rater was used to develop the ovary auto-segmentation pipelines. Uterus and ovary segmentations are available for all subjects, endometrioma segmentation is available for all subjects where it is detectable in the image. This study highlights the challenges of manual ovary segmentation in endometriosis MRI and emphasizes the need for an auto-segmentation method. The dataset is publicly available for further research in pelvic MRI auto-segmentation to support endometriosis research.

This study aims to develop an Exploratory deep learning method to quantitatively predict hematoma progression (EXPEDITION in short) after intracerebral hemorrhage (ICH). Patients with primary ICH in the basal ganglia or thalamus were retrospectively enrolled, and their baseline non-contrast CT (NCCT) image, CT perfusion (CTP) images, and subsequent re-examining NCCT images from the 2nd to the 8th day after baseline CTP were collected. The subjects who had received three or more re-examining scans were categorized into the test data set, and others were assigned to the training data set. Hematoma volume was estimated by manually outlining the lesion shown on each NCCT scan. Cerebral venous hemodynamic feature was extracted from CTP images. Then, EXPEDITION was trained. The Bland-Altman analysis was used to assess the prediction performance. A total of 126 patients were enrolled initially, and 73 patients were included in the final analysis. They were then categorized into the training data set (58 patients with 93 scans) and the test data set (15 patients with 50 scans). For the test set, the mean difference [mean ±1.96SD] of hematoma volume between the EXPEDITION prediction and the reference is -0.96 [-9.64, +7.71] mL. Specifically, in the test set, the consistency between the true and the predicted volume values was compared, indicating that the EXPEDITION achieved the needed accuracy for quantitative prediction of hematoma progression. An Exploratory deep learning method, EXPEDITION, was proposed to quantitatively predict hematoma progression after primary ICH in basal ganglia or thalamus.