BJR open最新文献

Objective: Sport-related concussion (SRC) is a prevalent form of traumatic brain injury that is associated with long-term neurological and psychiatric impairment, particularly among athletes with a history of repetitive concussions. The biological variability of SRC's impact on the brain, as well as a lack of objective biomarkers to diagnose and prognosticate concussion, has prompted interest in advanced neuroimaging methods such as diffusion tensor imaging (DTI). By measuring disruptions in water diffusivity due to head trauma, DTI can detect alterations in white matter integrity that are not visualized by conventional imaging methods. This systematic review aims to synthesize major trends and findings on original research studies that utilized DTI to evaluate subjects for SRC.

Methods: An initial search from PubMed, Web of Science, and Scopus generated 397 articles published from database inception to 2024, with 26 studies included in the final qualitative synthesis.

Results: Findings showed heterogenous changes in DTI parameters during acute injury with more consistent alterations seen in chronic injury, particularly as reduced fractional anisotropy and elevated mean diffusivity. Significant variability was observed in study design and methodology, which may explain discrepancies in findings across studies.

Conclusions: Future research efforts should implement standardized methods capable of accounting for inter-individual differences to further validate DTI's role as an objective biomarker of SRC.

Advances in knowledge: Individualized analysis of DTI could serve as a diagnostic tool and prognostic metric for patients with SRC, thus enabling an objective measure of long-term outcome and suitability for return-to-play.

MRI is a cornerstone of modern clinical medicine and neuroscience, yet it remains largely inaccessible in low- and middle-income countries (LMICs) due to high costs, complex infrastructure requirements, the need for specialized personnel, and dependence on proprietary systems. Portable low-field MRI (LF-MRI), operating below 100 mT, offers a compelling alternative: low-cost, more accessible, and increasingly powerful, thanks to advances in hardware engineering, acquisition physics, image reconstruction, and open-source software. Reviewing and building upon recent progress, we, a multidisciplinary team of clinicians, physicists, engineers, and global health researchers based both in LMIC and HIC settings, present a formal argument for the adoption of LF-MRI as a catalyst for discovery science and healthcare innovation in LMICs. LF-MRI can produce clinically meaningful images and rich research data, enabling population-scale studies in neurodevelopment, ageing, and neurogenetics. But we argue that systems must be open, upgradeable, and co-developed, allowing potential for local teams to maintain, adapt, and scale technology according to their needs. Beyond the scanner, we outline the ecosystem required for success: data infrastructure, training pathways, ethical data governance, and equitable collaboration. We issue a call to researchers, vendors, and funders to reframe MRI as a globally accessible technology, capable of supporting diverse research agendas and delivering transformative health impact, particularly where it is needed most.

Extranodal extension (ENE) is an established adverse prognostic indicator for head and neck cancers (HNC), and its presence entails adjuvant chemoradiotherapy, hence, it had been incorporated for the first time as the advanced regional node N3b category in the 8th edition of the Union for International Cancer Control (UICC)/American Joint Committee on Cancer (AJCC) Tumour Node Metastasis (TNM) classification for cancers of the oral cavity, human papillomavirus-negative oropharynx, hypopharynx, larynx and major salivary gland carcinomas. Pathological ENE is available for cases which are operated on, but cases which are managed non-surgically or unfit for surgery rely on imaging for providing the information on ENE, and this has prompted researchers across the globe to devise radiological grading for ENE. Radiological ENE has finally been given due credit and incorporated in the 9th version of AJCC TNM staging for nasopharyngeal carcinoma, which came into effect from January 2025. Knowledge of ENE status on baseline imaging prior to operation also helps in counselling patients regarding prognosis and planning adjuvant treatment. In this article, we have comprehensively reviewed the radiological/imaging ENE (rENE/iENE) grading proposed by researchers worldwide, extensively reviewed the existing evidence and challenges of using rENE/iENE for staging, grading, prognosticating and treating HNC, and also discussed the future scope of using rENE/iENE for managing patients with HNC of all the subsites, including thyroid cancers. Artificial intelligence-based studies for predicting rENE/iENE have also been discussed briefly.

Objectives: Xerostomia toxicity continues to contribute towards a decrease in quality of life in head and neck cancer patients. Diffusion weighted MRI and the associated apparent diffusion coefficient (ADC) may identify the radiosensitive region within the parotid gland (PG). This study retrospectively assesses the feasibility of using percentile threshold values from the ADC map to generate a biological at-risk volume (BRV). The location and distribution of BRV are evaluated across the PG.

Methods: Image registration between the planning CT and MRI-simulation images was performed and reviewed to ensure accurate translation of ADC data when contouring the PG. Histogram analysis was undertaken using the 20th, 30th, and 50th percentile ADC values of each contoured PG to form the BRV. The whole PG was split into 8 anatomical sectors at a common intersection point to evaluate the distribution of BRV throughout.

Results: The BRV distribution for each percentile was mapped across the whole contoured PG and each anatomical sector contour. The largest distribution was predominantly found in the superolateral sectors.

Conclusions: The 20th and 30th percentile ADC values can be used to form a BRV of the PG. The location of the BRV distribution indicates a potential relationship between ADC thresholds and the functional region of the PG.

Advances in knowledge: The BRV is located in a favourable position within the PG and could be used to further spare this salivary gland during dose optimization. The feasibility of this approach will be explored in a future retrospective dosimetry study.

A subset of patients with interstitial lung diseases (ILDs) experiences disease progression despite standard treatment protocols. Similar to idiopathic pulmonary fibrosis, the archetype of progressive fibrotic ILDs, these patients exhibit worsening clinical symptoms, declining lung function, and progressive radiological changes, often resulting in shortened survival. This progressive disease pattern is classified under the term progressive pulmonary fibrosis or progressive fibrosing ILD. Radiological imaging, particularly high-resolution computed tomography (HRCT), is integral to diagnosing ILDs and plays a critical role within multidisciplinary ILD boards. HRCT is instrumental in identifying patients at a higher risk for disease progression and may provide valuable prognostic insights. Additionally, serial imaging is essential for detecting progression over time. While visual assessment remains the primary method for evaluating disease advancement, emerging quantitative techniques, including those utilizing machine learning, are currently undergoing validation.

Diffusion-weighted imaging (DWI) is an advanced MRI technique that harnesses the movement of water molecules within tissues to assess and characterize a wide range of musculoskeletal disorders. By differentiating between isotropic and anisotropic diffusion, DWI provides critical insights into tissue integrity and pathology, proving instrumental in diagnosing conditions. Its sensitivity to changes in tissue microstructure is quantified through metrics like the apparent diffusion coefficient (ADC) and fractional anisotropy (FA). Advanced methodologies, including diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI), further enhance DWI's ability to evaluate complex tissue architectures, offering vital information on muscle, ligament, tendon, and cartilage health. DWI also excels in the assessment of soft tissue tumours, infections, and joint pathologies, enabling accurate differentiation between benign and malignant lesions and facilitating early detection of conditions like osteomyelitis. Additionally, DWI plays a crucial role in monitoring treatment responses, with ADC changes correlating to tumour necrosis and recurrence. Despite its advantages, DWI faces limitations, such as technical artefacts and challenges in interpretation that can impact diagnostic accuracy. This review explores the diverse applications of DWI and DTI in musculoskeletal imaging, highlighting their potential to improve diagnostic precision and clinical outcomes while addressing ongoing challenges in the field.

Objectives: An accurate morphological classification of distinct pulmonary phenotypes in sarcoidosis is lacking. Recently, a multinational Delphi study was conducted to reach a consensus on recognizable high-resolution computer tomography (HRCT) phenotypes in pulmonary sarcoidosis as a basis for a more distinctive classification. The reliability of these phenotypes has not yet been evaluated.

Methods: HRCT scans of adult sarcoidosis patients from the pulmonology department of a single sarcoidosis referral center were scored by three blinded independent readers. Seven phenotypes were distinguished as described in the Delphi study. They were divided into two subgroups: "non-fibrotic" and "likely-to-be fibrotic". Intra- and inter-reader reliability for scoring the predominant phenotype and the subgroup was assessed using weighted Kappa (K_w) statistics.

Results: Forty-five patients (mean age, 47 years ± 12, 28 men) were included. For the scoring of the predominant phenotype, inter-reader reliability between all readers was substantial with an overall Fleiss' kappa of 0.69 (CI 0.622-0.765, P < .001). We observed a substantial inter-reader reliability between readers A and B (K _w of 0.76), between readers B and C (K_w of 0.66) and between readers A and C (K _w of 0.66). For the scoring of the subgroups "non-fibrotic" vs. "likely-to-be fibrotic," overall Fleiss' Kappa was substantial (K = 0.78, CI 0.607-0.944, P < .001). We observed a K _w score of 0.76 between readers A and B; 0.81 between readers A and C; 0.76 between readers B and C. Intra-reader reliability was substantial between the scores of A in scoring the predominant phenotypes (K _w of 0.71) and it was almost perfect in scoring the subgroups (K _w of 0.95). Intra-reader reliability was substantial between the scores of B in scoring the predominant phenotype (K _w of 0.66) and subgroups (K _w of 0.72).

Conclusions: The inter- and intra-reader reliability of the newly proposed HRCT phenotypes obtained from the Delphi study is very acceptable.

Advances in knowledge: This study is the first to assess the reliability of these HRCT phenotypes and supports the use of them for classification purposes in future clinical and pathogenetic studies.

This study examines the shortage of radiation oncologists in Italy and Europe, analysing systemic challenges in postgraduate training and proposing solutions to enhance the appeal of radiation oncology. A review of literature from Italy and Europe evaluated trends in training programmes, workforce dynamics. Analysis included residency vacancies, economic constraints, training disparities, and visibility of the field during medical education. In Italy, 55.3% of radiation oncology residency positions have gone unfilled or been abandoned since 2016, with 90% of positions vacant in 2023. Contributing factors include inadequate exposure to radiotherapy during medical training, limited financial opportunities, negative societal perceptions, and high levels of burnout. Across Europe, similar challenges persist. Training disparities, outdated infrastructure, and regional inequalities exacerbate workforce shortages, particularly in low-income countries. Addressing the radiation oncology crisis requires a multifaceted strategy, including enhancing visibility of the field in medical education, improving working conditions, offering financial incentives, and addressing disparities in training quality across Europe. The European radiotherapist shortage is a systemic issue requiring coordinated efforts to standardize training, address economic barriers, and improve the specialty's appeal. By fostering collaboration and reform, European nations can meet the growing demand for cancer care and secure a sustainable workforce for the future.

Artificial intelligence (AI) has made significant strides in cardiac imaging, offering advancements in image acquisition, risk prediction, and workflow automation. However, its readiness for widespread clinical adoption remains debated. This review explores both sides of the argument across key domains. It discusses the advantages and challenges of AI for cardiac imaging regarding pre-and post-processing, risk-stratification and prognostication, workflow augmentation, regulatory and ethical frameworks, and cost-effectiveness of AI tools. It will discuss the diagnostic accuracy shown by AI for automated measurements, improved image quality and workflow efficiency with AI-driven worklist prioritization. The potential of personalized care using AI-based prognostic models. It discusses regulatory frameworks for approving AI tools, while ethical frameworks to ensure safe and ethical use of AI are being implemented, simultaneously reimbursement is becoming available, signalling growing trust in their safety and efficacy. It also addresses the challenges AI has yet to overcome, such as the lack of generalizability across diverse populations, limited availability of outcome data and cost-efficacy studies. Despite progress, regulatory and ethical frameworks still struggle to keep pace with AI's rapid evolution, raising concerns about accountability, patient safety, bias, data privacy, and algorithmic transparency.

Background: Rheumatoid arthritis (RA) patients frequently report fatigue, which notably diminishes their quality of life. Emerging research points to a correlation between inflammation-induced fatigue and brain structural alterations.

Objectives: This study evaluates the variance in myelin integrity among patients with RA-related fatigue, investigating the potential of magnetization transfer ratio (MTR) as a biomarker, in comparison with healthy controls.

Methods: A prospective cohort analysis was conducted comprised 60 RA patients with fatigue, categorized into active (n = 30) and non-active (n = 30) disease states, alongside 20 healthy controls (HC). A 3 Tesla MRI system was utilized to perform diffusion tensor imaging (DTI) and magnetization transfer imaging (MTI) sequences. MTR maps were generated using in-house MATLAB code and co-registered with DTI data using SPM8. These were then analyzed through tract-based spatial statistics (TBSS) with threshold-free cluster enhancement (TFCE) and corrected for multiple comparisons. MTR values were assessed using Randomize from the FSL toolkit, applying a general linear model (GLM) for voxel-wise analysis and TFCE for p-value generation, with family-wise error (FWE) control (P < .05) for multiple comparisons.

Results: The RF group exhibited significantly lower myelin integrity (TFCE, P < .05) compared to HCs, particularly in the middle cerebellar peduncle and splenium of the corpus callosum, with no marked difference between active and non-active RA disease statuses. There is a discernible disparity in myelin integrity between RA patients with fatigue and healthy individuals, suggesting microstructural white matter alterations that are congruent with DTI findings.

Conclusion: This study reveals that rheumatoid arthritis (RA) patients with fatigue exhibit significantly lower myelin integrity, particularly in the middle cerebellar peduncle and splenium of the corpus callosum, compared to healthy controls. Notably, this finding was consistent regardless of the active or non-active status of the RA disease, highlighting persistent white matter alterations in this patents cohort.

Advances in knowledge: The research demonstrates that magnetization transfer ratio (MTR) imaging can effectively map microstructural changes in RA patients with fatigue, suggesting its potential as a biomarker for assessing white matter integrity in this condition. While it does not establish a direct causal relationship, it provides valuable insights into the role of MTR mapping in understanding brain alterations in patients with fatigue-related RA.