Nurse-led multidisciplinary care improves recovery and quality of life after hepatectomy for liver cancer: A radiological imaging-based evaluation

A comparison of high dose versus low dose intranasal midazolam for sedation in the pediatric emergency department.

DBMAF: Dual-branch multimodal attention-based feature fusion network for fusing histopathology and radiology images

Soft tissue-RADS scoring system for musculoskeletal (MSK) tumor and tumor-like lesions is recently published. The system includes diagnostic MRI flowcharts and algorithms for lipomatous masses, cyst-like or high-water content masses, and indeterminate-solid appearing masses with consistent and standardized management recommendations. MSK radiology and cancer imaging training commonly provides the essential skills needed to render appropriate prospective diagnosis for many non-neoplastic tumor-like lesions and various commonly encountered sarcomas. Such trained readers may benefit from additional advanced algorithms to accurately identify and categorize different benign tumors and sarcomas and correctly input them in the soft tissue-RADS scoring system. These algorithms are built from a comprehensive literature search to establish an evidence-based composite and are supported by the expert opinions of the multidisciplinary ACR-soft tissue-RADS team. In this article we provide (1) a literature review of multimodality imaging, immunopathology, and clinical findings of soft tissue tumors and tumor-like lesions, presented in 7 tables (condensed from the 12 tables published in the WHO classification of soft tissue tumors) with respective table summaries; (2) a suggested modality-based lexicon; and (3) advanced diagnostic algorithms for trained readers based on published evidence and expert opinion that feed into the soft tissue-RADS scoring system (categories 1-6). The purpose of this work is to provide comprehensive guidance on soft tissue-RADS scoring to help streamline care for soft tissue tumors and tumor-like lesions and optimize patient outcomes.

COVID-19 is becoming a pandemic worldwide. The disease can cause mild to severe symptoms. The diagnostic methods currently available include laboratory testing using RT-PCR, as well as imaging techniques such as chest X-rays and CT scans. The most common radiological features in COVID-19 patients are ground glass opacity (GGO), reticular changes, and consolidation. Other features that can be found are thickening of the interlobular septa, bronchiectasis, pleural thickening, and a crazy-paving pattern. The most common distribution is bilateral, peripheral, and with a predominance in the middle or lower parts. Chest X-ray (CXR) examination may show abnormalities in 75% of RT-PCR-confirmed positive cases. Although generally not recommended due to its low sensitivity, CXR is still considered an affordable radiologic imaging technique in diagnosing COVID-19 pneumonia. Chest CT-Scan (CCT) has a higher sensitivity in COVID-19 diagnostics compared to CXR. On CCT examination, there is a CO-RADS classification that can help assess the level of suspicion of COVID-19. Ultrasonography has certain advantages, especially when used in pediatric patients.

Evaluation of radiological imaging practices and challenges in disability assessment in Saudi Arabia

For patients with tortuous descending aortas undergoing thoracic endovascular aortic repair (TEVAR), reducing tortuosity may optimize stent-wall conformity and improve surgical outcomes, but its necessity remains unclear. This study aims to verify the necessity of reducing descending aortic tortuosity while sealing the tears during TEVAR for type B aortic dissection (TBAD) patients from a hemodynamic perspective. Radiological imaging data (pre- and post-TEVAR) of nine TBAD patients with tortuous descending aortas were collected, and subject-specific aortic geometries were reconstructed. Meanwhile, virtual TEVAR geometries with pre-TEVAR tortuosity were created as a control group for post-TEVAR. Computational fluid dynamics simulations were conducted and hemodynamic indicators were comparatively analyzed by statistics among three groups, including vorticity, displacement force, time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI) and relative residence time (RRT). Compared to pre-TEVAR, the hemodynamic indicators of the post-TEVAR group improved significantly (p < 0.050), while those in the virtual TEVAR group were not significant (p > 0.050). The post- and virtual TEVAR group exhibited improvements to pre-TEVAR in vorticity (36.44% vs. 27.10%), displacement force (14.51% vs. 4.77%), TAWSS (57.06% vs. 10.90%), OSI (27.24% vs. 6.03%) and RRT (48.58% vs. 16.74%), respectively. Additionally, the left subclavian artery perfusion in post-TEVAR increasedby 58.73% compared with pre-TEVAR (p = 0.022), significantly more than the 22.68% increase (p = 0.175) in the virtual group. Reducing descending aortic tortuosity during TEVAR yields significant hemodynamic benefits. This may potentially enhance graft stability and promote long-term TEVAR efficacy.

Early and accurate diagnosis of breast cancer is critical for minimizing needle biopsies and enhancing patient outcomes and requires effective integration of multimodal information. In this article, we introduce a breast cancer intelligent non-invasive diagnosis system (BINDS) to integrate multimodal medical imaging data for breast cancer risk assessment and subtype classification. BINDS uses a two-stage diagnostic approach to match the clinical workflow, where an initial assessment with ultrasound and/or mammography is performed, followed by a more comprehensive multimodal diagnosis incorporating magnetic resonance imaging. In addition, a new radiology-pathology alignment mechanism is proposed to facilitate extraction of pathology-relevant features from radiological images. BINDS is developed and validated with a diverse dataset of 27,048 participants from 8 centres and 7 public datasets. Importantly, BINDS supports flexible combinations of input modalities during training and validation. Notably, BINDS attains an area under the receiver operating characteristic curve of 0.973, and can assist radiologists in reducing biopsies of benign lesions by up to 32.4%. These findings highlight the potential of BINDS to advance breast cancer diagnosis by enabling precise and adaptable decision-making across diverse clinical scenarios and resource settings.

A thorough understanding of facial canal dehiscence (FCD) is imperative for informed consent before otological surgery. A broad range of published rates for FCD exists, varying by method of assessment, location along the canal, and year of investigation. This study aims to determine a clinically relevant prevalence of FCD in healthy populations. A systematic review of Embase, Medline, Cochrane Library, Google Scholar, Science Direct, and Scopus identified eligible studies reporting the prevalence of FCD in subjects aged 10 years or older without erosive bone disease. Prevalence, location of dehiscence, and method of assessment were extracted. Pooled prevalence was calculated using a random-effects model with the generic inverse variance method. A total of 2186 unique studies provided 52 eligible cohorts for analysis (15,090 facial canals). The overall pooled prevalence of FCD was 22% (95% CI: 17%-28%). Pooled prevalence of dehiscence in the tympanic segment varied significantly by method of assessment (P = 0.0494): radiologic imaging 32% (95% CI: 15%-55%), cadaveric dissection 23% (95% CI: 14%-35%), and intraoperative finding 7% (95% CI: 1%-29%). This is the first clinically focused and comprehensive estimate of FCD in nonerosive ears of healthy subjects. The rate from cadaveric studies showed dehiscence in ~2 in 10 ears, whereas CT was higher at ~3 in 10, and intraoperative findings were lowest at ~1 in 10. Understanding these variations in reported rates of FCD has direct implications for preoperative risk assessment, patient counseling, and surgical planning in otological practice.

Artificial Intelligence-Assisted Interpretation of Veterinary Radiographs: Opportunities, Risks, and Best Practices for Clinicians.

Chronic invasive fungal sinusitis is a rare sinonasal pathology characterized by histopathological evidence of fungi causing chronic inflammation, as well as radiologic imaging of bony destruction and extrasinus invasion. Here, the authors have described a case of chronic invasive fungal sinusitis with the presence of a fungal mass, orbital involvement, and intracranial extension into the anterior skull base. Management involved nasal endoscopy with biopsy, postoperative debridement, and antifungal therapy.

Radiation-free assessment of the 3D morphology of the adolescent scoliotic spine: a feasibility study using MRI derived quantitative synthetic CT.

Cancer comprehensive genomic profiling tests are increasingly used, but drug response rates to matched therapies remain limited, indicating a gap between genomic data and clinical benefit. However, existing cancer evolution simulations focus mainly on basic biology and rarely provide patient‑specific predictions for therapy response. We present tugMedi, a cancer‑cell evolution simulator designed for cancer genome medicine. Integrating patient‑specific genomic data from next‑generation sequencing and radiological imaging, tugMedi reconstructs tumor features and growth, enabling real‑time predictions of clonal dynamics under drug interventions. It explicitly models copy number alterations and SNVs on parental chromosomes with recessive/dominant modes, capturing intra‑tumor heterogeneity and loss‑of‑heterozygosity to yield precise variant allele frequencies and tumor purity estimates. Synthetic tests confirmed recovery of ground‑truth tumor parameters and trajectories. For TCGA samples, tugMedi provided ensemble forecasts of drug response, tumor shrinkage, and recurrence times under specific drug conditions, demonstrating the feasibility of simulation‑driven genome medicine using patient‑derived virtual tumors.

This study aimed to establish the normal range of kidney stiffness (KS) in individuals with normal appearing kidneys (INK) using 3 Tesla (T) magnetic resonance elastography (MRE) while also exploring the potential influence of demographic and anatomical parameters on KS. This retrospective study included cases from March 2021 to October 2024, in May 2025, where abdominal magnetic resonance imaging (MRI) was performed, capturing both kidneys in the cross-sectional area. Only cases with normal kidney signal characteristics and morphology on conventional MRI sequences were included. All relevant data and radiological images were obtained from the hospital computerized database. The study included patient age, sex, comorbidity status, body mass index (BMI), and specific MRI and MRE parameters, including kidney stiffness (KS in Pascal), cranio-caudal (CC) diameter, anteroposterior (AP) diameter, lateral diameter, and kidney volume. The study included 31 males (51.70%) and 29 females (48.30%), with a mean age of 36.30 ± 9.05 years. No significant correlation was found between the stiffness of the right and left kidneys and age, sex, or BMI. The mean stiffness of the right kidneys was 2360.57 ± 93.62 Pa, while that of the left kidneys was 2360.35 ± 144.13 Pa. No significant correlations were observed between the CC diameter, AP diameter, lateral diameter, kidney volume of either kidney, and stiffness. KS values were similar in both kidneys and showed no correlation with demographic or anatomical factors, indicating their consistency for future studies.

Heterogeneous multigroup data often contain both globally shared and group-specific components, posing challenges for conventional regression models. Such data structures are increasingly common in medical research, particularly in radiology and biomedical imaging, where patient populations are naturally heterogeneous across disease subtypes, demographic groups, or imaging modalities. While methods such as Joint and Individual Component Regression (JICO) address this separation, they lack mechanisms to quantify uncertainty and incorporate prior knowledge. In this study, we propose a Bayesian Joint and Individual Component Regression (Bayesian-JICO) framework that extends JICO with a probabilistic formulation. The Bayesian approach enables uncertainty quantification through posterior distributions and credible intervals, offering more reliable inference, especially with limited sample sizes. Posterior estimation was performed via Markov Chain Monte Carlo (MCMC), and the model was evaluated using both simulated scenarios and the publicly available Australian Institute of Sport (AIS) dataset, which contains physiological and hematological measurements of elite athletes. Results demonstrate that Bayesian-JICO outperforms traditional methods in predictive accuracy, interpretability, and robustness, while providing credible intervals for parameter estimates. This framework offers a comprehensive and uncertainty-aware solution for multigroup regression, with broad applicability across biomedical, radiological, environmental, and social sciences.

The aim of this article was to assess agreement between verified self-reported fractures in a clinical trial with Accident Compensation Corporation (ACC) claim data. In a 10-year randomised controlled trial of 1,054 women aged 50-60 years, participants self-reported fractures as they occurred or on routine 6-monthly questionnaires. Radiology imaging and reports were used to verify fractures, which were then compared with ACC claims data (ACC is the New Zealand no-fault accident claims organisation funded through levies). Initially, fracture claim data only were obtained, followed by all ACC claims for each participant for the study period. Three hundred and fifty-six self-reported fractures in 248 women were verified in the trial, whereas there were 328 ACC fracture claims from 238 women for the study period. Out of 356 trial fractures, 211 (59%) had a matching ACC fracture claim, and out of 328 ACC fracture claims 211 (64%) had a matching trial fracture. After obtaining all ACC claims, we identified a matching ACC claim for 340/356 (96%) trial fractures: 59% were fracture claims and 31% soft-tissue injury claims. Repurposing ACC fracture claims data for clinical trials has significant limitations and is likely to introduce false negative and false positive events. When tolerance for misclassification is higher (e.g., large non-randomised studies), ACC claims data may be useful because 60% of claims had a verified fracture, with higher proportions for major fracture types.

Limited clinical utility of mutation-based circulating tumor DNA detection in pseudomyxoma peritonei.

Accurate angular measurements derived from three-dimensional (3D) radiological imaging are essential for morphometric analysis, surgical planning, and implant design. However, measurements performed on standard sagittal, coronal, and transverse sections may not reflect true spatial geometry due to projection-related distortions. This study aimed to develop and validate a reoriented plane protocol to improve the accuracy and reproducibility of angular measurements in 3D imaging.Five anatomically accurate plastic skull models were used to define four craniofacial angles (∠Co-Id-Co, ∠Go-Gn-Go, ∠Co-Go-Gn, ∠N-Rza-Pr), and all measurements were independently performed by three observers. Direct goniometric measurements served as the reference standard. CT scans (0.625mm slice thickness) were analyzed using standard sectional planes and a reoriented plane approach aligning the measurement plane with the true spatial configuration of landmarks. Intraobserver and interobserver reliability were assessed using intraclass correlation coefficients (ICC), and agreement was evaluated with Bland-Altman analyses.Goniometric measurements showed mean values of 55.8°±4.2°, 72.9°±4.9°, 113.5°±6.5°, and 36.5°±5.7° for the respective angles. Reoriented plane measurements produced nearly identical results with minimal bias (e.g., 0.002° for ∠Co-Go-Gn) and narrow limits of agreement. In contrast, standard sectional measurements demonstrated substantial deviations, with mean biases reaching - 36.47° in coronal sections. The reoriented plane method showed excellent intraobserver and interobserver reliability (ICC up to 0.950, p < 0.001).Aligning the measurement plane with the true landmark-defined plane significantly improves angular measurement accuracy and reproducibility in 3D radiological datasets, offering a robust methodological framework for clinical and research applications.

Despite rapid advancements in artificial intelligence (AI) for medical imaging, widespread clinical adoption remains limited. In March 2025, the Academy for Radiology & Biomedical Imaging Research convened a cross-sector roundtable to examine operational and structural challenges in AI development and implementation. Researchers, department leaders, government representatives, and industry executives participated in a structured two-stage discussion using the AI lifecycle and a simplified failure modes and effects analysis (sFMEA) framework. In the first stage, attendees examined each phase of the AI lifecycle to identify domains where implementation barriers arise. In the second stage, mixed stakeholder groups applied a qualitative sFMEA approach to analyze process vulnerabilities within those domains and discuss mitigation approaches. This manuscript summarizes the session design, synthesizes key domains, and presents illustrative mitigation approaches across five areas: governance, use cases, implementation, cost, and regulation. The discussion identified recurring challenges related to fragmented priorities, infrastructure constraints, and regulatory complexity, as well as the need for clearer governance structures and more consistent evaluation processes to improve coordination across stakeholders.

Congenital adrenal hyperplasia (CAH) is an autosomal recessive disorder. It can be observed with varied clinical symptoms. It can be diagnosed with the help of biochemical tests, radiologic imaging, and genetic testing. Sonography can be helpful in identification of bilateral adrenal gland enlargement. A case is presented of a 13-day-old female neonate, who was brought to the radiology department for detailed screening, due to the presence of ambiguous genitalia. She was incidentally found to have bilateral adrenal gland enlargement. The sonographic findings revealed enlarged bilateral adrenal glands. The remainder of the diagnostic findings were deemed to be normal, except for the incidental finding of a uterus-like structure. Hence, the neonate was further evaluated for CAH hormonal and genetic evaluations. This patient case reinforces the need for a multidisciplinary approach involving radiology, endocrinology, and genetics, to provide an early diagnosis, intervention, and improved outcomes associated with CAH. This case also highlights the importance of clinician’s awareness and family counseling.