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Alpha radiation has emerged as a promising modality in cancer treatment due to its unique physical and biological properties. Among these, diffusing alpha-emitters radiation therapy (DaRT) delivers alpha radiation directly into solid tumours using inserted seeds. This review synthesizes both the biological mechanisms and therapeutic implications of alpha irradiation, with a focus on DaRT. We explore how alpha particles induce complex DNA damage, modulate the tumour microenvironment, and interact with immune therapies. Emphasis is placed on preclinical and early clinical findings that suggest DaRT's potential to improve outcomes, especially in difficult-to-treat malignancies. The high linear energy transfer (LET) radiation induces complex DNA damage in tumour cells, leading to increased cell death compared to conventional radiotherapy. Alpha particles have a short range in tissue, allowing for highly localized treatment with minimal damage to surrounding healthy tissue. Recent studies have demonstrated that alpha radiation can stimulate antitumor immune responses, potentially enhancing treatment efficacy. Clinical trials utilizing alpha-emitting radioisotopes have shown encouraging results in various cancer types, particularly for metastatic disease.

Musculoskeletal (MSK) imaging was among the first radiology subspecialties to adopt artificial intelligence (AI), with applications now spanning the entire MSK workflow, from image acquisition to reporting. Deep learning-based reconstruction protocols can accelerate MRI by reducing scan times and artefacts, improving accessibility in high-volume and resource-limited settings. Furthermore, AI interpretation tools have demonstrated strong performance in fracture detection, assessment of meniscal and ligament tears, bone tumour characterization and automated quantification of measurements, supporting greater diagnostic consistency across radiologists with varying experience levels. Large language models (LLMs) extend AI's impact beyond image analysis by simplifying reports for patients, automating classification systems, and streamlining clinical communication. Despite these advances, important challenges remain. Integration of AI into already established clinical workflows can be complex, and requires robust technical solutions, regulatory compliance, and strategies to maintain radiologist oversight. Questions of liability, cost-effectiveness, and the role of AI in medical education further underscore the need for careful implementation. AI is poised to fundamentally reshape MSK radiology by enhancing efficiency, improving diagnostic accuracy, and enabling more patient-centred communication. To fully realize this potential, adoption must balance innovation with safety, equity, and sustainability, ensuring AI remains a trusted assistive tool that strengthens rather than replaces radiologist expertise.

MSK radiologists play a critical role in emergency and trauma settings, where rapid and accurate imaging interpretation is essential for timely diagnosis and treatment. The increasing complexity of trauma cases has driven the adoption of advanced imaging modalities beyond conventional radiographs and computed tomography (CT). Dual-energy CT (DECT) and magnetic resonance imaging (MRI) have revolutionized MSK imaging, offering superior tissue characterization and improved detection of occult fractures, bone marrow edema (BME), infections, and soft tissue injuries. Emerging technologies, such as portable MRI and photon-counting CT (PCCT), further enhance diagnostic capabilities by enabling bedside imaging, reducing radiation exposure, and providing ultra-high-resolution images. MSK radiologists are integral to immediate diagnosis, triaging, differentiating acute from chronic injuries, guiding surgical interventions, and performing image-guided procedures. DECT in particular has proven invaluable in detecting BME, reducing metal artifacts, and improving soft tissue contrast, while MRI remains the gold standard for evaluating soft tissue injuries and occult fractures. Portable MRI offers a radiation-free alternative for point-of-care imaging, especially in spinal cord and soft tissue injuries. PCCT, with its superior spatial resolution and material decomposition capabilities, holds promise for advanced fracture detection and reduced radiation doses. Additionally, 3D printing has emerged as a transformative tool for preoperative planning, surgical simulation, and personalized implant design. Despite challenges such as cost, accessibility, and technical limitations, these advancements are reshaping trauma imaging. As technology evolves, MSK radiologists will continue to integrate these innovations to optimize patient care in emergency and trauma settings, ensuring faster, more accurate diagnoses.

This review explores the dual meaning of the prefix "eco"-ecology and economics-and the transformative idea of synthesizing the two into a single "eco" framework. This framework gives rise to EcoRad, which blends economic and ecologic principles to optimize radiology practice. EcoRad strives to achieve the triple bottom line by approaching economic challenges from a planetary health perspective and by using economic approaches to enhance planetary health. In effect, this expands the traditional focus on financial performance to also include social and environmental impact. With EcoRad as a guide, radiology departments are called upon to consider 5 actions that can help overcome barriers to sustainable radiology: adopt sustainable procurement and maintenance, integrate green information technology (IT) and operational efficiencies, advocate for payment models that reward green radiology, champion green budgeting, and involve patients, industry, third-party payors, and policymakers in sustainability.

Artificial intelligence (AI) is transforming radiology, with nearly 80% of approved AI as medical devices (AIaMDs) being imaging-related. As AI adoption accelerates, radiology training programs must evolve to equip future radiologists with the skills to critically evaluate, implement, and integrate AI into clinical practice. However, despite AI's growing role, its inclusion in medical curricula remains inconsistent, and assessment of AI competency is lacking. This review explores the current state of AI in UK medical training curricula with a more in-depth focus on radiology. We discuss the potential impact of AI on competency evaluations, including the Fellowship of the Royal College of Radiologists (FRCR) examinations, Annual Review of Competence Progression (ARCP), and on-call assessments. Additionally, we examine how AI-driven educational resources, such as AI-assisted training platforms, could enhance radiology education. To future-proof radiology training and careers, we propose strategies to evaluate AI literacy including nationalized structured AI teaching, and AI-focused assessments. Addressing these challenges will be crucial in ensuring that radiologists remain at the forefront of digital healthcare transformation while maintaining their core diagnostic expertise.

Low-field MRI (LF-MRI) is in the spotlight as multidisciplinary experts consider it to be one solution to expanding MRI access worldwide. The clinical scenarios and case-mix in which LF-MRI could play an especially important role in the patient diagnostic algorithm are different in High and Low- and Middle-Income Countries (LMIC). The aim of this article is to suggest a robust structure within which to envision clinical use and advancement of LF-MRI technology in LMICs. This article presents three discrete clinical scenarios-a tertiary care facility with an LF-MRI only, a tertiary care facility with an LF-MRI and an HF-MRI and a peripheral healthcare facility with an LF-MRI only-derived from a combination of the authors' observed practice and hypothetical models in an LMIC and 31 consecutive case reviews within a 32-month timeframe of our experience with the 0.064 T Hyperfine Swoop in Malawi. The authors recognize the important of a holistic approach to the ongoing multifaceted efforts at LMIC-appropriate advancement of LF-MRI technology. This ranges from continued innovation relating to deep learning methods for improved diagnostic accuracy and workflow efficiency, empowerment towards building LF-MRIs in-situ in the LMIC and multidisciplinary capacity building initiatives in LMICs.

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.