The accurate and timely diagnosis of brain tumors is of paramount clinical significance for effective treatment planning and improved patient outcomes. While deep learning has advanced medical image analysis, concurrently achieving high classification accuracy, robust generalization, and computational efficiency remains a formidable challenge. This is often due to the difficulty in optimally capturing both fine-grained local tumor features and their broader global contextual cues without incurring substantial computational costs. This paper introduces NeXtBrain, a novel hybrid architecture meticulously designed to overcome these limitations. NeXtBrain's core innovations, the NeXt Convolutional Block (NCB) and the NeXt Transformer Block (NTB), synergistically enhance feature learning: NCB leverages Multi-Head Convolutional Attention and a SwiGLU-based MLP to precisely extract subtle local tumor morphologies and detailed textures, while NTB integrates self-attention with convolutional attention and a SwiGLU MLP to effectively model long-range spatial dependencies and global contextual relationships, crucial for differentiating complex tumor characteristics. Evaluated on two publicly available benchmark datasets, Figshare and Kaggle, NeXtBrain was rigorously compared against 17 state-of-the-art (SOTA) models. On Figshare, it achieved 99.78 % accuracy and a 99.77 % F1-score. On Kaggle, it attained 99.78 % accuracy and a 99.81 % F1-score, surpassing leading SOTA ViT, CNN, and hybrid models. Critically, NeXtBrain demonstrates exceptional computational efficiency, achieving these SOTA results with only 23.91 million parameters, requiring just 10.32 GFLOPs, and exhibiting a rapid inference time of 0.007 ms. This efficiency allows it to outperform significantly larger models such as DeiT3-Base with 85.82 M parameters, Swin-Base with 86.75 M parameters in both accuracy and computational demand.

Diffusion-weighted imaging (DWI) is widely applied in tumor diagnosis by measuring the diffusion of water molecules. To increase the sensitivity to tumor identification, faithful high b-value DWI images are expected by setting a stronger strength of gradient field in magnetic resonance imaging (MRI). However, high b-value DWI images are heavily affected by reduced signal-to-noise ratio due to the exponential decay of signal intensity. Thus, removing noise becomes important for high b-value DWI images. Here, we propose a Physics-Informed neural Network for high b-value DWI images Denoising (PIND) by leveraging information from physics-informed loss and prior information from low b-value DWI images with high signal-to-noise ratio. Experiments are conducted on a prostate DWI dataset that has 125 subjects. Compared with the original noisy images, PIND improves the peak signal-to-noise ratio from 31.25 dB to 36.28 dB, and structural similarity index measure from 0.77 to 0.92. Our schemes can save 83% data acquisition time since fewer averages of high b-value DWI images need to be acquired, while maintaining 98% accuracy of the apparent diffusion coefficient value, suggesting its potential effectiveness in preserving essential diffusion characteristics. Reader study by 4 radiologists (3, 6, 13, and 18 years of experience) indicates PIND's promising performance on overall quality, signal-to-noise ratio, artifact suppression, and lesion conspicuity, showing potential for improving clinical DWI applications.

This systematic review and meta-analysis aimed to assess the diagnostic accuracy of radiomics in risk stratification of gastrointestinal stromal tumors (GISTs). It focused on evaluating radiomic models as a non-invasive tool in clinical practice. A comprehensive search was conducted across PubMed, Web of Science, EMBASE, Scopus, and Cochrane Library up to May 17, 2025. Studies involving preoperative imaging and radiomics-based risk stratification of GISTs were included. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Radiomics Quality Score (RQS). Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using bivariate random-effects models. Meta-regression and subgroup analyses were performed to explore heterogeneity. A total of 29 studies were included, with 22 (76 %) based on computed tomography scans, while 2 (7 %) were based on endoscopic ultrasound, 3 (10 %) on magnetic resonance imaging, and 2 (7 %) on ultrasound. Of these, 18 studies provided sufficient data for meta-analysis. Pooled sensitivity, specificity, and AUC for radiomics-based GIST risk stratification were 0.84, 0.86, and 0.90 for training cohorts, and 0.84, 0.80, and 0.89 for validation cohorts. QUADAS-2 indicated some bias due to insufficient pre-specified thresholds. The mean RQS score was 13.14 ± 3.19. Radiomics holds promise for non-invasive GIST risk stratification, particularly with advanced imaging techniques. However, radiomic models are still in the early stages of clinical adoption. Further research is needed to improve diagnostic accuracy and validate their role alongside conventional methods like biopsy or surgery.

Transcatheter heart valve (THV) durability is a critical concern, and its deformation may influence long-term performance. Current assessments rely on CT-based single-phase measurements and require a tedious analysis process, potentially overlooking deformation dynamics throughout the cardiac cycle. A fully automated artificial intelligence-based method was developed to assess THV deformation in post-transcatheter aortic valve implantation (TAVI) 4DCT scans. The approach involves segmenting the THV, extracting orthogonal cross-sections along its axis, fitting ellipses to these cross-sections, and computing eccentricity to analyze deformation over the cardiac cycle. The method was evaluated in 21 TAVI patients with different self-expandable THV models, using one post-TAVI 4DCT series per patient. The THV inflow level exhibited the greatest eccentricity variations (0.35-0.69 among patients with the same THV model at end-diastole). Additionally, eccentricity varied throughout the cardiac cycle (0.23-0.57), highlighting the limitations of single-phase assessments in characterizing THV deformation. This method enables automated THV deformation assessment based on cross-sectional eccentricity. Significant differences were observed at the inflow level, and cyclic variations suggest that full cardiac cycle analysis provides a more comprehensive evaluation than single-phase measurements. This approach may aid in optimizing THV durability and function while preventing related complications.

Chronic Obstructive Pulmonary Disease (COPD) is a significant public health challenge globally, with Asia facing unique burdens due to varying demographics, healthcare access, and socioeconomic conditions. Recognizing the limitations of pulmonary function tests (PFTs) in early detection and comprehensive evaluation, the Asian Society of Thoracic Radiology (ASTR) presents this recommendations to guide the use of chest computed tomography (CT) in COPD diagnosis and management. This document consolidates evidence from an extensive literature review and surveys across Asia, highlighting the need for standardized CT protocols and practices. Key recommendations include adopting low-dose paired respiratory phase CT scans, utilizing qualitative and quantitative assessments for airway, vascular, and parenchymal evaluation, and emphasizing structured reporting to enhance clinical decision-making. Advanced technologies, including dual-energy CT and artificial intelligence, are proposed to refine diagnosis, monitor disease progression, and guide personalized interventions. These recommendations aim to improve the early detection of COPD, address its heterogeneity, and reduce its socioeconomic impact by establishing consistent and effective imaging practices across the region. This recommendations underscore the pivotal role of chest CT in advancing COPD care in Asia, providing a foundation for future research and practice refinement.

Photon-counting detector CT (PCD-CT) represents a significant advancement in medical imaging, particularly for musculoskeletal (MSK) applications. Its primary innovation lies in enhanced spatial resolution, which facilitates improved detection of small anatomical structures such as trabecular bone, osteophytes, and subchondral cysts. PCD-CT enables high-quality imaging with reduced radiation doses, making it especially beneficial for populations requiring frequent imaging, such as pediatric patients and individuals with multiple myeloma. Additionally, PCD-CT supports advanced applications like bone quality assessment, which correlates well with gold-standard tests, and can aid in diagnosing osteoporosis and assessing fracture risk. Techniques such as spectral shaping and virtual monoenergetic imaging further optimize the technology, minimizing artifacts and enhancing material decomposition. These capabilities extend to conditions like gout and hematologic malignancies, offering improved detection and assessment. The integration of artificial intelligence could enhance PCD-CT's performance by reducing image noise and improving quantitative assessments. Ultimately, PCD-CT's superior resolution, reduced dose protocols, and multi-energy imaging capabilities will likely have a transformative impact on MSK imaging, improving diagnostic accuracy, patient care, and clinical outcomes.

This study aimed to develop and evaluate a graphical user interface (GUI) for the automated segmentation of the hypothalamus and intracranial volume (ICV) in brain MRI scans. The interface was designed to facilitate efficient and accurate segmentation for research applications, with a focus on accessibility and ease of use for end-users. We developed a web-based GUI using the Gradio library integrating deep learning-based segmentation models trained on annotated brain MRI scans. The model utilizes a U-Net architecture to delineate the hypothalamus and ICV. The GUI allows users to upload high-resolution MRI scans, visualize the segmentation results, calculate hypothalamic volume and ICV, and manually correct individual segmentation results. To ensure widespread accessibility, we deployed the interface using ngrok, allowing users to access the tool via a shared link. As an example for the universality of the approach, the tool was applied to a group of 90 patients with Parkinson's disease (PD) and 39 controls. The GUI demonstrated high usability and efficiency in segmenting the hypothalamus and the ICV, with no significant difference in normalized hypothalamic volume observed between PD patients and controls, consistent with previously published findings. The average processing time per patient volume was 18 s for the hypothalamus and 44 s for the ICV segmentation on a 6 GB NVidia GeForce GTX 1060 GPU. The ngrok-based deployment allowed for seamless access across different devices and operating systems, with an average connection time of less than 5 s. The developed GUI provides a powerful and accessible tool for applications in neuroimaging. The combination of the intuitive interface, accurate deep learning-based segmentation, and easy deployment via ngrok addresses the need for user-friendly tools in brain MRI analysis. This approach has the potential to streamline workflows in neuroimaging research.

To assess the impact of image post-processing steps on the generalisability of MRI-based radiogenomic models. Using a human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC) prediction model, this study examines the potential of different post-processing strategies to increase its generalisability across data from different centres and image acquisition protocols. Contrast-enhanced T1-weighted MR images of OPSCC patients of two cohorts from different centres, with confirmed HPV status, were manually segmented. After radiomic feature extraction, the HPV prediction model trained on a training set with 91 patients was subsequently tested on two independent cohorts: a test set with 62 patients and an externally derived cohort of 157 patients. The data processing options included: data harmonisation, a process to ensure consistency in data from different centres; exclusion of unstable features across different segmentations and scan protocols; and removal of highly correlated features to reduce redundancy. The predictive model, trained without post-processing, showed high performance on the test set, with an AUC of 0.79 (95% CI: 0.66-0.90, p < 0.001). However, when tested on the external data, the model performed less well, resulting in an AUC of 0.52 (95% CI: 0.45-0.58, p = 0.334). The model's generalisability substantially improved after performing post-processing steps. The AUC for the test set reached 0.76 (95% CI: 0.63-0.87, p < 0.001), while for the external cohort, the predictive model achieved an AUC of 0.73 (95% CI: 0.64-0.81, p < 0.001). When applied before model development, post-processing steps can enhance the robustness and generalisability of predictive radiogenomics models. Question How do post-processing steps impact the generalisability of MRI-based radiogenomic prediction models? Findings Applying post-processing steps, i.e., data harmonisation, identification of stable radiomic features, and removal of correlated features, before model development can improve model robustness and generalisability. Clinical relevance Post-processing steps in MRI radiogenomic model generation lead to reliable non-invasive diagnostic tools for personalised cancer treatment strategies.

The past decade has witnessed remarkable advancements in musculoskeletal radiology, driven by increasing demand for medical imaging and rapid technological innovations. Contrary to early concerns about artificial intelligence (AI) replacing radiologists, AI has instead enhanced imaging capabilities, aiding in automated abnormality detection and workflow efficiency. MRI has benefited from acceleration techniques that significantly reduce scan times while maintaining high-quality imaging. In addition, novel MRI methodologies now support precise anatomic and quantitative imaging across a broad spectrum of field strengths. In CT, dual-energy and photon-counting technologies have expanded diagnostic possibilities for musculoskeletal applications. This review explores these key developments, examining their impact on clinical practice and the future trajectory of musculoskeletal radiology.

Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms for detecting and diagnosing dental caries using cone-beam computed tomography (CBCT) with the Mask R-CNN architecture while comparing various hyperparameters to enhance detection. A total of 2,128 CBCT images were divided into training and validation and test datasets in a 7:1:1 ratio. For the verification of tooth recognition, the data from the validation set were randomly selected for analysis. Three groups of Mask R-CNN networks were compared: A scratch-trained baseline using randomly initialized weights (R group); A transfer learning approach with models pre-trained on COCO for object detection (C group); A variant pre-trained on ImageNetfor for object detection (I group). All configurations maintained identical hyperparameter settings to ensure fair comparison. The deep learning model used ResNet-50 as the backbone network and was trained to 300epoch respectively. We assessed training loss, detection and training times, diagnostic accuracy, specificity, positive and negative predictive values, and coverage precision to compare performance across the groups. Transfer learning significantly reduced training times compared to non-transfer learning approach (p < 0.05). The average detection time for group R was 0.269 ± 0.176 s, whereas groups I (0.323 ± 0.196 s) and C (0.346 ± 0.195 s) exhibited significantly longer detection times (p < 0.05). C-group, trained for 200 epochs, achieved a mean average precision (mAP) of 81.095, outperforming all other groups. The mAP for caries recognition in group R, trained for 300 epochs, was 53.328, with detection times under 0.5 s. Overall, C-group demonstrated significantly higher average precision across all epochs (100, 200, and 300) (p < 0.05). Neural networks pre-trained with COCO transfer learning exhibit superior annotation accuracy compared to those pre-trained with ImageNet. This suggests that COCO's diverse and richly annotated images offer more relevant features for detecting dental structures and carious lesions. Furthermore, employing ResNet-50 as the backbone architecture enhances the detection of teeth and carious regions, achieving significant improvements with just 200 training epochs, potentially increasing the efficiency of clinical image interpretation.