The patient experience during diagnostic radiological procedures in inflammatory bowel disease (IBD) management remains an underexplored aspect of healthcare delivery. Despite advancements in imaging technologies, understanding patient perspectives is critical to optimizing adherence, satisfaction, and overall outcomes. This systematic review aims to address existing gaps in knowledge by synthesizing evidence on the acceptability, perception, preference, comfort, and burden associated with radiological investigations in IBD. A comprehensive systematic review was conducted in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Five electronic databases were searched for studies reporting on adult IBD patients undergoing radiological investigations. Data were extracted on study characteristics, diagnostic modalities compared, and patient-reported outcomes including tolerability, satisfaction, acceptability, and perceived clinical utility. Results were stratified by comparator type. The search identified 737 citations, of which 14 studies involving 2,076 IBD patients met the inclusion criteria. There was significant variability in patient acceptability across imaging modalities, with gastrointestinal ultrasound (GIUS) consistently emerging as the most acceptable and preferred radiological tool. Factors influencing acceptability included pain, embarrassment, procedural invasiveness, clarity of instructions, and the degree of patient-centred communication. This review emphasizes the necessity of tailoring radiological practices to align with patient needs and expectations. The inclusion of non-radiological comparators provides a comprehensive assessment of patient experience but also highlights the importance of context when interpreting patient preferences. Further research is needed to develop standardized measurement tools and to explore the broader psychological impacts of radiological procedures. Promoting the adoption of highly acceptable modalities such as GIUS through targeted training and integration into routine care may enhance patient experience.

We review burnout risk factors in interventional radiology (IR) and explore how artificial intelligence (AI) would address burnout from a workplace aspect. We performed a literature search on PubMed on risk factors for burnout in interventional radiology and AI tools to address burnout challenges. IR specialists face burnout risk at personal, workplace and system levels. AI could identify burnout using demographic data and free text, alleviate administrative workload, and manage workflow. AI could also enhance procedural efficiency via automated navigation systems, reducing stress from radiation exposure. Future directions include enhanced burnout identification and medical coding for access to longitudinal data. AI may be a solution to addressing specific burnout risk factors in interventional radiology. No level of evidence. Review Article.

Button battery ingestions are time-sensitive emergencies that require prompt identification and removal. Button batteries can be mistaken for coins, which can lead to dangerous delays in management. We designed the "LIME Wedge" radiologic reference device to hold four coins and one 20 mm button battery. We used a radiologic phantom to simulate button battery and coin ingestions on anterior and lateral chest X-ray (CXR) images. This series of images demonstrates how our tool can be used to help visually differentiate esophageal button batteries from coins. The LIME Wedge is an inexpensive, 3D-printed, radiologic tool that provides physicians with reference examples of various coins and a button battery in two radiologic planes. This radiologic tool is accessible for free download in the appendices.

Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is an autosomal dominant condition that may co-occur with cardiac, renal, and skeletal abnormalities. It affects females causing utero-vaginal agenesis. Radiological assessment tools such as ultrasound and Magnetic Resonance Imaging (MRI) can be utilised for the confirmation of MRKH syndrome and for planning further management. Management of MRKH syndrome includes both non-surgical and surgical approaches, depending on the clinical presentation. The current report presents a case of a 21-year-old female who presented with a complaint of primary amenorrhoea and dyspareunia. Examination of the genitalia revealed normal external urethral meatus, labia majora and minora, and pubic hair development. Speculum examination confirmed the lack of a vaginal canal. Radiological findings were suggestive of uterine agenesis. Genetic analysis showed a 46,XX karyotype, thereby ruling out the chromosomal abnormalities. Based on the above findings and clinical evaluations, a final diagnosis of MRKH syndrome was confirmed. The patient declined surgical interventions at present and was managed non-surgically. In MRKH syndrome patients, counselling is crucial attributed to the associated physical abnormalities, related queries and mental stress.

Noninfectious pulmonary complications are a significant cause of morbidity and mortality in immunocompromised patients, particularly in those undergoing hematopoietic stem cell transplantation, solid organ transplantation, chemotherapy, or immunotherapy. These syndromes often mimic infections, leading to delayed diagnosis and inappropriate treatment. Acute complications include peri-engraftment respiratory distress syndrome, diffuse alveolar hemorrhage, drug-induced lung injury, immune checkpoint inhibitor-related pneumonitis, and radiation pneumonitis, while late or chronic complications, such as organizing pneumonia, interstitial lung disease, bronchiolitis obliterans syndrome, and chronic graft-versus-host disease-related lung involvement, typically develop months to years after therapy. Accurate and timely diagnosis is essential, relying on high-resolution CT, bronchoalveolar lavage, and, in selected cases, lung biopsy to differentiate these conditions from infections. Current treatments remain largely empirical, focusing on corticosteroids, supportive intensive care, and immunosuppressive adjustment, although novel strategies, including inhaled hemostatic agents and JAK inhibitors, are emerging. Despite advances in supportive management, late-onset complications remain associated with poor long-term functional outcomes. Future directions include the development of biomarkers, artificial intelligence-assisted radiological tools, and multicenter registries to improve classification, risk stratification, and treatment. In this narrative review, we highlight current evidence around noninfectious pulmonary complications in the critical care setting, diagnosis, and treatment.

Abstract Ayurveda, with its holistic and individualized diagnostic approach, is increasingly intersecting with modern radiological tools like X-ray, etc. Recent research explores how combining these approaches can enhance diagnostic accuracy and standard care and improve patient outcomes, especially in musculoskeletal and surgical care. Ayurveda diagnosis is personalized, leading to leading ton variability across practitioners and a lack of a standardized diagnostic tool. Current tools are based on classical text and lack scientific validation. Modern diagnostic tools like X-ray, computed tomography, etc., can improve the diagnostic precision and guide interventions. These technologies complement Ayurveda assessment. X-ray imaging remains a cornerstone in medical diagnosis, integrating it in Ayurveda diagnosis, especially for musculoskeletal diseases, has proven to be a significant diagnostic tool in our perception and practice. There is a need for developing X-ray diagnosis of Ayurveda diseases in proper and appropriate Ayurveda terminologies to guide the classical line of treatment. Using standardized terminologies in Ayurveda diagnostics is essential for ensuring clear communication, consistency in clinical practice, and effective interprofessional collaboration. However, the lack of terminological clarity has led to confusion in both research and practice, highlighting the need for reflection and refinement at national and international levels. The integration of standardized X-ray terminologies in diagnostic and clinical settings has been less explored, thus increasing the need for understanding the benefits and challenges in implementation in Ayurveda.

Background: The number of patients requiring home-based medical care will increase given global ageing. One challenge in homecare is the lack of radiological tools to aid with clinical assessment. Ultrasound devices have become increasingly portable. This allows doctors to perform point-of-care ultrasound examinations (POCUS) and enhance their patient assessment in homecare. We are an established homecare team called Community Health Team (CHT) in Tan Tock Seng Hospital, Singapore, with >700 annual home visits. We augmented our patient assessment using POCUS and collaborations with ultrasound-accredited doctors in the hospital. Point-of-care ultrasound examinations can transform the care of home-bound patients, by bringing a valuable radiological assessment tool to the home setting, thereby enhancing physician assessment and patient care. Approach: Two doctors in CHT obtained training in POCUS. A handheld ultrasound device was borrowed from the Intensive Care Unit. We worked with ultrasound-accredited clinicians and the hospital quality team to come up with a workflow for image acquisition, transmission, and interpretation. We performed a prospective study to assess the utility of POCUS in aiding diagnosis for CHT patients. A doctor acquired and analyzed the ultrasound images, with image interpretation reaffirmed through a secure telemedicine platform by an ultrasound-accredited clinician. Results: From January 2022 to April 2022, 39 POCUS examinations were performed for 21 patients. POCUS helped determine fluid status by dynamic measurements of inferior vena cava diameter (21 examinations, 53.8%). Lung ultrasound allowed assessment for pulmonary edema, pneumonia, or pleural effusion (13 examinations, 33.3%). Bladder ultrasound in 3 examinations (7.7%) enabled residual urine volume assessment or confirmation of urine catheter placement. 2-point lower limb ultrasound excluded deep vein thromboses in 2 cases (5.1%). 33 of 39 POCUS examinations (84.6%) influenced clinical decisions and treatment. 5 of 39 POCUS examinations (12.8%) did not influence clinical decision making but aided confirmation of physical findings. In 1 case (2.6%) with early aspiration pneumonia, POCUS did not detect consolidation. In 4 out of 21 cases (19.0%), POCUS examination helped in avoiding a hospital attendance for further radiological assessment. In conclusion, we found that POCUS enables patient-centred care at home by: (a)Real-time delivery of clinical information (b)Improving confidence of clinical decision making, with telemedicine allowing support in the image interpretation (c)Enabling potential cost-savings and reduction in need to travel for dedicated radiological imaging Implications: From clinicians’ perspective, POCUS in homecare aids with patient assessment and management. From patients’ and family’s perspective, POCUS in homecare reduces the hassle of travelling for radiological imaging, especially for those who are bed-bound. From a systems point of view, POCUS in homecare reduces preventable hospital attendance and leads to potential cost savings. The next steps are to develop this program further by looking at the three pillars of workflow, education and patient safety. Within workflow, the scope of practice for POCUS in homecare settings should be defined and accompanying clinical pathways and standard documentations norms established. Within education, curriculum should be developed for training of homecare doctors in POCUS. For patient safety, guidelines for quality assurance and governance should be formulated.

Most commercially available artificial intelligence (AI) tools in radiology are trained and approved for adult use, creating an access gap for pediatric patients. Intracranial hemorrhage (ICH) detection is a common adult AI application without pediatric FDA clearance. To evaluate the performance of an FDA-cleared, adult-trained AI tool for ICH detection on non-contrast head CT (NCHCT) in pediatric patients aged 6-17years. This retrospective, multi-institution study analyzed consecutive pediatric NCHCTs performed between January 2017 and November 2022 across 21 sites. Inclusion criteria were patient age 6-17years and adequate imaging quality. Radiology reports were classified as ICH-positive or ICH-negative using a validated natural language processing (NLP) tool. The AI tool analyzed DICOM images independently. Discordant AI-NLP cases underwent blinded adjudication by three radiologists to establish ground truth. Performance metricsincludingsensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV)were calculated with Wilson 95% confidence intervals (CIs). The cohort included 1,996 NCHCTs (768 females, 1,223 males, 5 unknown). ICH prevalence was 8.6% (172/1,996). Compared with ground truth, AI achieved 94.2% sensitivity (162/172, 95% CI, 89.6-97.2%), 94.7% specificity (1,727/1,824, 95% CI, 93.6-95.7%), 94.6% accuracy (1,889/1,996, 95% CI, 93.6-95.6%), 62.5% PPV (162/259, 95% CI, 57.8-67.0%), and 99.4% NPV (1,727/1,737, 95% CI, 99.0-99.7%). AI correctly identified ICH in cases missed by radiologists, but false positives were common, most often due to streak artifact (21.6%) and misclassified anatomy (18.6%). Interrater agreement for ground truth adjudication was substantial (κ=0.683). An adult-trained AI tool demonstrated high sensitivity, specificity, and accuracy for ICH detection in pediatric patients aged 6-17years, comparable to its adult performance. Selective adaptation of adult-trained AI tools could expand access to AI-assisted triage for certain pediatric populations, potentially reducing delays in critical imaging interpretation. However, prospective validation is required before clinical deployment.

Visualization Tools in Radiology: A RRA Perspective on Virtual Reality, Augmented Reality, and 3D Printing.

Diagnosing cardioembolic stroke/transient ischemic attack (TIA) rapidly remains a major challenge. Current emergency cardiac assessments often provide insufficient data. This study tests whether bolus tracking, a readily available technique during brain CTA, can be leveraged to quickly identify reduced ejection fraction (EF) and potential cardioembolic etiology in acute stroke patients. We retrospectively analyzed CTA and echocardiography data from acute ischemic stroke/TIA patients across two comprehensive stroke centers. We measured total transit time (TTT) based on aortic attenuation values of 40 Hounsfield Units (T40HU) and 100 HU (T100HU) obtained during routine CTA. ΔT100-T40HU, representing the time difference required for aortic contrast enhancement to increase from 40 to 100 HU, was also calculated as a key parameter. EF was measured by transthoracic echocardiography using the Simpson biplane method. In a cohort of 443 patients, we found a statistically significant inverse correlation between EF and T40HU, T100HU, and ΔT100-T40HU. Notably, ΔT100-T40HU demonstrated good discriminatory ability for identifying heart failure (AUROC = 0.88) and poor discriminatory ability for cardioembolic stroke (AUROC = 0.66). Multivariate analysis further confirmed that T40HU, T100HU, and ΔT100-T40HU were independent predictors of both heart failure and cardioembolic stroke based on the TOAST classification criteria. Bolus tracking during CTA provides a reliable and easily accessible radiological tool for the identification of heart failure and cardioembolic stroke, enabling a faster diagnostic workup and facilitating appropriate selection of further diagnostic investigations within the acute emergency setting.

This scoping review aims to evaluate the performance of artificial intelligence (AI) models designed for adults when applied to paediatric imaging datasets without additional adaptations, and to quantify performance degradation across different modalities, use-cases and age groups. A literature search was conducted covering 10 years (1/01/2014-23/06/2025) using terms relating to "child", "adult", "artificial intelligence", "radiology" and "validation/performance". Two reviewers independently extracted data using standardised templates and conducted a narrative analysis. Of 5642 abstracts, 20 studies met the inclusion criteria. The studies evaluated AI tools across 16 paediatric dataset cohorts ranging from 30 to 7357 subjects. Three datasets were used more than once to evaluate different AI model performance metrics. The tools were applied to radiography (n = 7), CT (n = 7), MRI (n = 2), Dual-energy-x-ray-absorptiometry (DEXA) (n = 2) and ultrasound (n = 2) across different AI tasks: segmentation (n = 9), classification (n = 4), detection (n = 3), and mixed tasks (n = 4). Apart from two studies, all articles reported performance reduction when adult-trained AI tools were applied to paediatric populations. Cohort overlap represents the risk of duplication bias. Detection tasks showed the most severe deterioration, with sensitivity dropping from 68-100% in adults to 26-68% in children for pulmonary nodule detection. For segmentation tasks, Dice score reductions > 0.10 were noted across organs and imaging modalities. Children ≤ 2 years consistently showed the greatest performance deficits across all task types. AI tools intended for adult use do not perform to the same standard when used in a paediatric population without additional adaptation, particularly for children under 2 years. Careful model evaluation is required before clinical implementation. Question How do artificial intelligence-based radiology tools designed for adults perform when applied to paediatric imaging without additional adaptation? Findings Adult-trained AI models consistently demonstrated reduced performance in children, particularly in those under 2 years, with detection tasks showing the most severe deterioration. Clinical relevance Healthcare professionals should not assume that adult-trained radiology AI tools intended for adult use can be directly applied to the paediatric population without validation, additional training or fine-tuning, particularly for the youngest age groups.

Lessons Learned From Helene: The Role of a Rural Community Hospital in Disaster Response After a Major Hurricane.

Multiple sclerosis (MS) progressively impairs brain network function, often driving disability even in the absence of overt structural MRI changes. Current clinical and radiological tools frequently fail to capture early, subtle disruptions in cortical activity that may indicate ongoing disease progression. Functional assessment methods capable of detecting these early network alterations are therefore critically needed. This study aimed to determine whether brain responses recorded by combining transcranial magnetic stimulation (TMS) with electroencephalography (EEG) from the primary motor cortex differ in MS, correlate with clinical disability and predict disease activity. Sixty-nine right-handed participants [mean age: MS 38.5 ± 9.1 years, healthy controls (HCs) 36.9 ± 8.8 years; 41 females] were enrolled, including 43 patients with relapsing-remitting MS and 26 HCs matched for age and sex. MS patients were clinically stable and off corticosteroids or CNS-acting medications at least 1 month prior to testing. All underwent single-pulse stimulation over the left primary motor cortex during EEG recording. Transcranial-evoked potentials (TEPs) and spectral perturbations were extracted. Patients were followed for 2 years and classified as active or stable based on 'No Evidence of Disease Activity-3' criteria. Patients showed significantly reduced P60 amplitude compared with controls (P = 0.0098, FDR-corrected P adj. = 0.0491), and a trend-level reduction in gamma-band desynchronization (i.e. less negative values) (P = 0.025, P adj. = 0.075), which correlated inversely with 9-Hole Peg Test times (r s = -0.504, P = 0.001). A trend towards lower P15 amplitude was observed in patients with active disease (P = 0.0178, P adj. = 0.0891), and P15 amplitude significantly predicted disease stability at 2 years (accuracy = 74.4%, P = 0.023). TMS combined with EEG detects altered motor cortical network dynamics in MS. Less-pronounced (i.e. less negative) gamma-band desynchronization correlated with preserved fine-motor network efficiency, potentially reflecting a compensatory mechanism. The P15-evoked potential amplitude may predict disease activity. This perturbation-based approach provides a privileged window into network dysfunction in MS, with potential to guide early prognosis and treatment.

Large language models (LLMs) are emerging as transformative tools in radiology, with potential to enhance diagnostic workflows. However, their performance in bone tumour imaging – a domain requiring both knowledge-based reasoning and visual interpretation -- remains unclear. This study compares the diagnostic performance of LLMs with radiologists across text and image-based tasks. In this cross-sectional study, two LLMs and two radiologists (a junior and a senior) were evaluated using fifty text-based multiple-choice questions (MCQs) and fifty radiographs with clinical vignettes from a public dataset. Participants classified lesions as benign or malignant, identified “don't-touch” lesions, and provided the most likely diagnosis. Responses were benchmarked against a reference standard using McNemar's tests. In MCQs, ChatGPT-5 (92.0%) and Gemini 2.5 Pro (90.0%) achieved accuracies comparable to SR (88.0%) and JR (84.0%) (p > 0.05). For benign--malignant classification, LLMs (50.0%, 48.0%) were similar to JR (66.0%) but inferior to SR (94.0%) (p < 0.05). In identifying “don't-touch”' lesions, LLMs (46.0%) matched JR (64.0%) yet underperformed compared to SR (92.0%) (p < 0.05). For specific diagnosis, LLMs showed low accuracy (38.0%, 30.0%) versus JR (60.0%) and SR (86.0%) (p < 0.01). LLMs may serve as useful adjuncts for clinicians and radiologists in text-based tasks and in distinguishing between benign and malignant bone tumours. However, their diagnostic accuracy remains limited.

BackgroundMedical imaging remains at the forefront of advancements in adopting digital health technologies in clinical practice. Regulator-approved artificial intelligence (AI) clinical decision support systems are commercially available and being embedded into routine practices for radiologists internationally. These decision support solutions show promising clinical validity compared to standard practice conditions; however, their implementation over time and implications on radiologists’ practice are poorly understood.ObjectiveThis paper aims to examine the real-world implementation of an AI clinical decision support tool in radiology through a qualitative evaluation across pre-, peri-, and postimplementation phases. Specifically, it seeks to identify the key contextual, organizational, and human factors shaping adoption and sustainability, to map these influences using the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework, and to generate insights that inform evidence-based strategies and policy for integrating AI safely and effectively into public hospital imaging services.MethodsThis prospective study was conducted in a large public tertiary referral hospital in Brisbane, Queensland, Australia. One-to-one participant interviews were undertaken across the 3 implementation phases. Participants comprised radiology consultants, registrars, and radiographers involved in chest computed tomography studies during the study period. Interviews were guided by the NASSS framework to identify contextual factors influencing implementation.ResultsA total of 43 semistructured interviews were conducted across baseline (n=16), peri-implementation (n=9), and postimplementation (n=18) phases, comprising 7 (16%) radiographers, 20 (47%) registrar radiologists, and 16 (37%) consultant radiologists. Across NASSS domains, 56 barriers and 18 enablers were identified at baseline, 55 and 14 during peri-implementation, and 82 and 33 postimplementation. Organizational barriers dominated early phases, while technological issues such as system accuracy, interoperability, and information overload became most prominent during and after rollout. Enablers increased over time, particularly within the technology and value proposition domains, as some clinicians adapted the AI as a secondary safety check. Trust and adoption remained constrained by performance inconsistency, weak communication, and medicolegal uncertainty.ConclusionsThe implementation of AI decision support in radiology is as much an organizational and cultural process as a technological one. Clinicians remain willing to engage, but sustainable adoption depends on consolidating early positive experiences and addressing negative ones, embedding communication and training, and maintaining iterative feedback between users, vendors, and system leaders. Applying the NASSS framework revealed how domains interact dynamically across time, offering both theoretical insight into sociotechnical complexity and practical guidance for hospitals seeking to move from pilot to routine, trustworthy AI integration.

Developing shorter treatment regimens for tuberculosis requires careful characterization of the clinical phenotype, which is defined by patient characteristics, radiological extent of disease, mycobacterial burden, drug susceptibility, and host response. Advances in 'omics and model-informed precision dosing, as well as integrated algorithms using artificial intelligence, need to be adapted and validated in clinical trials to improve classification of patients for stratified treatment. When treatment is initiated based on the clinical phenotype, monitoring of treatment response can be improved by quantification of bacterial load, transcriptomic and epigenetic biosignatures for sputum-free monitoring, and assessing disease burden by radiological and symptom scoring tools. Many of these tools are suitable for high-endemic settings. Such integrated monitoring allows prompt drug adjustments for rapid reduction in bacterial load, which prevents development of drug resistance and achieves relapse-free cure even with shorter treatment.

The integration of artificial intelligence (AI) with medical imaging tools has enabled faster and more accurate diagnostic processes, transforming radiology into a more precise, efficient, and data-driven medical discipline. However, the successful implementation of AI-based medical imaging tools in emotionally sensitive, life-critical domains such as radiology depends heavily on public trust and acceptance. This study examines how value perceptions and trust shape behavioral intentions to adopt AI-based tools in radiology by extending Esmaeilzadeh’s Value-Based Model, which is conceptually aligned with Privacy Calculus Theory. To enhance the model’s explanatory power, additional variables were incorporated, including perceived knowledge as a predictor and trust as a mediating factor. A cross-sectional online survey (N = 961) was conducted, and data was analyzed through structural equation modeling. The findings indicate that perceived risk, perceived benefit, and perceived knowledge significantly influence trust perception. Importantly, trust served as a key mediating variable, partially mediating the effects of these factors on the intention to use AI-based medical imaging tools. The inclusion of trust increased the model’s explanatory power from R 2 = 0.68 to R 2 = 0.74. Multigroup analysis based on gender, age, and education level revealed significant differences in certain pathways; however, the effect sizes were small. These findings highlight the importance of developing inclusive and targeted strategies that address both technical and emotional concerns, enhance perceived benefits, foster public trust, and strengthen the intention to use AI-based tools in radiology. • Extends Esmaeilzadeh’s Value-Based Model to AI adoption in radiology. • Identifies trust as the key mediator linking risk, benefit, and AI knowledge. • Highlights user trust as a critical perception shaping public adoption of medical AI. • Shows perceived benefit outweighs perceived risk in high-stakes radiology. • Demonstrates AI literacy shapes trust through benefit-oriented evaluations.

Objective: With the rapid development of artificial intelligence, large language models (LLMs) have emerged as powerful tools capable of processing and interpreting complex medical data. These models can assist in the interpretation of imaging data, especially in conditions that require detailed anatomical analysis such as temporomandibular joint disorders. This study evaluated and compared the performance of 2 LLMs, Chat Generative Pre-trained Transformer-4 Omni (ChatGPT-4o) and Grok, in diagnosing temporomandibular joint disc displacement using magnetic resonance imaging (MRI). Material and Methods: A total of 129 sagittal MRI, including T1- and T2- weighted sequences, were retrospectively analyzed. The images were annotated to identify the disc and mandibular condyle, with diagnoses confirmed by oral and maxillofacial radiology experts. Both models were tasked with identifying anatomical structures and assessing the disc-condyle relationship. Results: Among the analyzed images, 65 showed disc displacement and 64 did not. ChatGPT-4o achieved an overall diagnostic accuracy of 67.4%, with a perfect sensitivity of 100% but lower specificity and precision. In contrast, Grok demonstrated an accuracy of 49.7% (p<0.005), but outperformed ChatGPT-4o in specificity (76.9%), precision (61.5%), and F1-score (58.1%). While ChatGPT-4o showed superior performance in identifying all pathological cases, Grok exhibited greater balance in reducing false positives. Conclusion: This study highlights the potential of LLMs as supplementary tools in oral and maxillofacial radiology while emphasizing the need for further advancements to improve their diagnostic capabilities.

Interstitial lung disease (ILD) encompasses a spectrum of inflammatory and fibrotic lung conditions. Interstitial lung abnormalities (ILA) are incidental radiological findings with the potential to progress to clinical ILD. Evidence guiding radiotherapy in patients with ILD and ILA is very limited. This retrospective cohort study included patients receiving curative-intent radiotherapy with or without chemotherapy at a UK tertiary oncology centre over a seven- year period. Patients with a prior ILD diagnosis or computer tomography (CT) features suggestive of ILA were identified and reviewed by specialist ILD radiologists. Patients were classified into 3 groups: ILD, ILA, or no radiological evidence of ILD/ILA. Clinical outcomes and adverse events were analysed. Of 1693 patients referred for radiotherapy, 163 underwent specialist radiological review: 53 ILD, 53 ILA, and 57 with no ILD/ILA features. Survival outcomes differed significantly between groups. Median overall survival (OS) was 9.4months (ILD), 14.7months (ILA), and 22.5months (no ILD/ILA) (p=0.001). On multivariable analysis, ILD was independently associated with worse OS (HR 2.88). Grade 5 pneumonitis occurred in 13% of ILD patients, 6% with ILA, and 0% with no ILD/ILA features. Conventional radiotherapy was associated with higher treatment-related adverse events compared to hypofractionated regimens. Patients with ILD experience significantly worse survival and higher risk of adverse events, including fatal pneumonitis, following radiotherapy. ILA represents an intermediate-risk group. These findings highlight the need for improved pre-treatment identification and risk stratification using radiological and clinical tools, and highlights the importance of prospective validation in future studies.

Goal: Skull fractures, especially those involving the cranial base and facial regions, present significant diagnostic challenges due to the skull's complex anatomy and subtle radiographic findings. Accurate detection requires repeated and meticulous examination of multiple CT slices, which is a significant cognitive burden, and requires considerable interpretation time. The primary objective of this study is to develop a visualization flattening technique that effectively transforms the curved skull surface into planar representations that enhance fracture features. Methods: A novel visualization process was developed that extracted the cranial surface and subsurface layers from head CT scans and used disk harmonic mapping to generate flattened representations of the lower, upper, occipital, and frontal hemispheres of the skull. The technique was applied to nine cases from the CQ500 dataset, with varying levels of inter-reader agreement, or lack thereof, among the original radiologists who interpreted the dataset. These cases encompass both straightforward and diagnostically challenging fractures that exemplify the advantages of the proposed methodology. Results: The flattened views unwrapped the fractures into continuous, high contrast features, with improved conspicuity compared to the fragmented appearance across multiplanar reconstruction slices. Comparison with existing skull visualization methods, the proposed technique demonstrated high contrast of fractures features, and delineation between emissary veins, with less distortion and high preservation of anatomical continuity. Conclusions: Disk harmonic flattening offers a new approach to skull fracture visualization, providing radiologists and emergency department staff with a valuable addition to the conventional radiological tools, particularly in diagnostically challenging cases.