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OBJECTIVE: The objective is to evaluate the diagnostic effectiveness of contrast-enhanced spectral mammography (CESM) in the diagnosis of breast cancer. DESIGN: DATA SOURCES: PubMed, Embase and Cochrane libraries up to 18 June 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included trials studies, compared the results of different researchers on CESM in the diagnosis of breast cancer, and calculated the diagnostic value of CESM for breast cancer. DATA EXTRACTION AND SYNTHESIS: Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) evaluated the methodological quality of all the included studies. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses specification. In addition to sensitivity and specificity, other important parameters were explored in an analysis of CESM accuracy for breast cancer diagnosis. For overall accuracy estimation, summary receiver operating characteristic curves were calculated. STATA V.14.0 was used for all analyses. RESULTS: This meta-analysis included a total of 12 studies. According to the summary estimates for CESM in the diagnosis of breast cancer, the pooled sensitivity and specificity were 0.97 (95% CI 0.92 to 0.98) and 0.76 (95% CI 0.64 to 0.85), respectively. Positive likelihood ratio was 4.03 (95% CI 2.65 to 6.11), negative likelihood ratio was 0.05 (95% CI 0.02 to 0.09) and the diagnostic odds ratio was 89.49 (95% CI 45.78 to 174.92). Moreover, there was a 0.95 area under the curve. CONCLUSIONS: The CESM has high sensitivity and good specificity when it comes to evaluating breast cancer, particularly in women with dense breasts. Thus, provide more information for clinical diagnosis and treatment.

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A 48-year-old female with a background history of alcoholic liver cirrhosis and noninsulin-dependent diabetes presented to our institution with emphysematous pyelonephritis of the right kidney diagnosed on CT. Both urine and blood culture grew multidrug-resistant Escherichia coli. The patient developed severe thrombocytopenia, hyponatremia, confusion as well as septic shock. Preoperative right renal arterial embolization was performed, followed by immediate primary nephrectomy. This article reports the first documented case of preoperative angioembolization to prevent severe intraoperative hemorrhage and vascular complications in severe emphysematous pyelonephritis.

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Hepatocellular carcinoma (HCC) is the third most fatal and fifth most common cancer worldwide, with rising incidence due to obesity and nonalcoholic fatty liver disease. Imaging modalities, including ultrasound (US), multidetector computed tomography (MDCT), and magnetic resonance imaging (MRI) play a vital role in detecting HCC characteristics, aiding in early detection, detailed visualization, and accurate differentiation of liver lesions. Liver-specific contrast agents, the Liver Imaging Reporting and Data System, and advanced techniques, including diffusion-weighted imaging and artificial intelligence, further enhance diagnostic accuracy. This review emphasizes the significant role of imaging in managing HCC, from diagnosis to treatment assessment, without the need for invasive biopsies.

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BACKGROUND: Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, precision, and accuracy. The integration of AI as a radiological diagnostic tool has the potential to mitigate delays in diagnosis, which could, in turn, impact patients' prognosis and treatment outcomes. The literature shows conflicting results regarding patients' attitudes to AI as a diagnostic tool. To the best of our knowledge, no similar study has been conducted in Saudi Arabia. OBJECTIVE: The objectives of this study are to examine patients' attitudes toward the use of AI as a tool in diagnostic radiology at King Khalid University Hospital, Saudi Arabia. Additionally, we sought to explore potential associations between patients' attitudes and various sociodemographic factors. METHODS: This descriptive-analytical cross-sectional study was conducted in a tertiary care hospital. Data were collected from patients scheduled for radiological imaging through a validated self-administered questionnaire. The main outcome was to measure patients' attitudes to the use of AI in radiology by calculating mean scores of 5 factors, distrust and accountability (factor 1), procedural knowledge (factor 2), personal interaction and communication (factor 3), efficiency (factor 4), and methods of providing information to patients (factor 5). Data were analyzed using the student t test, one-way analysis of variance followed by post hoc and multivariable analysis. RESULTS: A total of 382 participants (n=273, 71.5% women and n=109, 28.5% men) completed the surveys and were included in the analysis. The mean age of the respondents was 39.51 (SD 13.26) years. Participants favored physicians over AI for procedural knowledge, personal interaction, and being informed. However, the participants demonstrated a neutral attitude for distrust and accountability and for efficiency. Marital status was found to be associated with distrust and accountability, procedural knowledge, and personal interaction. Associations were also found between self-reported health status and being informed and between the field of specialization and distrust and accountability. CONCLUSIONS: Patients were keen to understand the work of AI in radiology but favored personal interaction with a radiologist. Patients were impartial toward AI replacing radiologists and the efficiency of AI, which should be a consideration in future policy development and integration. Future research involving multicenter studies in different regions of Saudi Arabia is required.

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OBJECTIVE: Two-tiered preference signaling has been implemented in the radiology residency application system to reduce congestion in the setting of high-volume applications. Signals are an indicator of strong interest that an applicant can transmit to a limited number of programs. This study assessed the impact of program signaling on interview invitations, how applicants strategically used signals based on their application's competitiveness, and applicants' attitudes toward the current signaling system. METHODS: A survey was sent to radiology residency applicants registered with TheRadRoom during the 2024 application cycle. We queried the applicant's background, applications, signal distribution, and interview outcome depending on the type of signal sent. We also asked whether respondents received an interview invitation from a hypothetical "comparator non-signaled program" if they had one additional signal to use. Group differences were assessed using nonparametric Wilcoxon signed rank test. RESULTS: A total of 202 applicants completed the survey (28% response rate). Most applied to diagnostic radiology (81%). Nearly all respondents utilized all 6 gold (98%) and 6 silver (96.5%) signals. Interview invitation rates were significantly higher for signaled programs (59.8%±27.4%) than non-signaled (8.5%±8.5%); the invitation rate at the comparator non-signaled programs was 37%. Gold signaled programs had significantly higher interview rates (67.8%±29.3) than silver (51.8%±31.3%). Respondents used 49.2%(±21.7%) of their signals for "likely to match" programs, 33.1%(±20.9%) for "aspirational" programs, and 17.6%(±15.8%) for "safety" programs. Most respondents (146;76%) supported continuing the signaling system for future cycles. CONCLUSION: Signaling programs significantly enhanced interview invitation rates, with gold signals being more effective than silver. The applicants used about 6 total signals for "likely-to-match" programs, 2 for "aspirational" programs, and about 4 for "safety" programs.

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PURPOSE: The performance of vision-language models (VLMs) with image interpretation capabilities, such as GPT-4 omni (GPT-4o), GPT-4 vision (GPT-4V), and Claude-3, has not been compared and remains unexplored in specialized radiological fields, including nuclear medicine and interventional radiology. This study aimed to evaluate and compare the diagnostic accuracy of various VLMs, including GPT-4 + GPT-4V, GPT-4o, Claude-3 Sonnet, and Claude-3 Opus, using Japanese diagnostic radiology, nuclear medicine, and interventional radiology (JDR, JNM, and JIR, respectively) board certification tests. MATERIALS AND METHODS: In total, 383 questions from the JDR test (358 images), 300 from the JNM test (92 images), and 322 from the JIR test (96 images) from 2019 to 2023 were consecutively collected. The accuracy rates of the GPT-4 + GPT-4V, GPT-4o, Claude-3 Sonnet, and Claude-3 Opus were calculated for all questions or questions with images. The accuracy rates of the VLMs were compared using McNemar's test. RESULTS: GPT-4o demonstrated the highest accuracy rates across all evaluations with the JDR (all questions, 49%; questions with images, 48%), JNM (all questions, 64%; questions with images, 59%), and JIR tests (all questions, 43%; questions with images, 34%), followed by Claude-3 Opus with the JDR (all questions, 40%; questions with images, 38%), JNM (all questions, 42%; questions with images, 43%), and JIR tests (all questions, 40%; questions with images, 30%). For all questions, McNemar's test showed that GPT-4o significantly outperformed the other VLMs (all P < 0.007), except for Claude-3 Opus in the JIR test. For questions with images, GPT-4o outperformed the other VLMs in the JDR and JNM tests (all P < 0.001), except Claude-3 Opus in the JNM test. CONCLUSION: The GPT-4o had the highest success rates for questions with images and all questions from the JDR, JNM, and JIR board certification tests.

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INTRODUCTION: Non-ventilator-associated hospital-acquired pneumonia (nv-HAP) is the most common healthcare-associated infection (HCAI), is associated with high mortality and morbidity and places a major burden on healthcare systems. Diagnosis currently relies on chest x-rays to confirm pneumonia and sputum cultures to determine the microbiological cause. This approach leads to over-diagnosis of pneumonia, rarely identifies a causative pathogen and perpetuates unnecessary and imprecise antibiotic use. The HAP-FAST study aims to evaluate the feasibility of a randomised trial to evaluate the clinical impact of low-dose, non-contrast-enhanced thoracic CT scans and rapid molecular sputum analysis using the BIOFIRE® FILMARRAY® pneumonia plus panel (FAPP) for patients suspected with nv-HAP. METHODS AND ANALYSIS: The HAP-FAST feasibility study consists of a pilot randomised trial, a qualitative study, a costing analysis and exploratory analyses of clinical samples to investigate the immune-pathophysiology of HAP. Participants are identified and recruited from four acute hospitals in the Northwest of the UK. Using a Research Without Prior Consent model, the pilot trial will recruit 220 adult participants, with or without mental capacity, and with suspected HAP. HAP-FAST is a non-blinded, sequential, multiple assignment, randomised trial with two possible stages of randomisation: first, chest x-ray (CXR) or CT; second, if treated as nv-HAP, FAPP or standard microbiological processing alone (no FAPP). Pathogen-specific antibiotic guidance will be provided for FAPP results. Randomisation uses a web-based platform and followed up for 90 days. The feasibility of a future trial will be determined by assessing trial processes, outcome measures and patient and staff experiences. ETHICS AND DISSEMINATION: This study has undergone combined review by the UK NHS Research Ethics Committee and Health Research Authority. Results will be disseminated via peer-reviewed journals, via the funders' website and through a range of media to engage the public. TRIAL REGISTRATION NUMBER: NCT05483309.

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The magic number, or number of ranks needed to achieve a greater than 90 % chance of matching, has not been investigated for diagnostic radiology (DR). Somewhat reflective of a field's changing competitiveness, this individual metric can be useful for reassuring applicants or identifying a need to reach out to mentors. The NRMP's Charting Outcomes in the Match was accessed over the previous 10 cycles to assess changes to magic number and other match-related metrics. Over the last 10 cycles, there has been an increase in magic number for prospective radiologists. Based on the most 2022 recent report, the magic number was 14 compared to 5 and 2 in 2014 and 2016 respectively. Compared to the average US MD senior, those applying into DR were significantly more likely to match in 2014, 2016 and 2020 (p < 0.01 for all), and significantly less likely to match in 2018 and 2022 (p = 0.03 and p < 0.01, respectively). This trend has had important consequences for applicants and programs as the incentive to apply more widely grows. The increasing magic number demonstrates increasing competitiveness in the field, which might be due to a positive job market, changing medical student preferences, or increased access to radiology electives and mentors. The 2024 Charting Outcomes document will be the first to include data from a class almost entirely affected by the change to a pass/fail Step1 and the new preference signaling supplement. It is currently unclear how either change will affect the overall competitiveness of the field and the magic number.

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Mycobacterium szulgai (MS) is a species of non-tuberculous mycobacterium (NTM), which very rarely is identified as the causative pathogen of pulmonary infections. Due to its rarity, there are limitations in the existing literature regarding the diagnosis, investigation and treatment of MS pulmonary infection. Our case report provides further information regarding the clinical, microbiological and radiological findings associated with MS pulmonary infection with suggestions provided on its long term management.

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BACKGROUND: The impact of intersectionality on academic radiology physician compensation is not well known. PURPOSE: The aim of this study was to assess impact of intersectionality on academic radiology financial compensation, based on rank, gender and race/ethnicity in US medical schools. METHODS: Data were collected from the AAMC Faculty Salary Survey, which collects information for full-time faculty at U.S. medical schools. Financial compensation data for radiology faculty with MD or equivalent degree in diagnostic radiology (DR) as well as interventional radiology (IR) was collected for 2023, stratified by rank, gender, and race/ethnicity. RESULTS: The AAMC Faculty Salary Survey data for 2023 included responses for 683 IR (138 women, 545 men) and 2431 DR (862 women, 1569 men) faculty. Men had a higher median compensation than women at all ranks, for both IR and DR, except DR instructors. The gender pay gap was greater in IR faculty compared to DR faculty of the same rank. All intersectional groups among IR faculty reported a lower median compensation compared to White men of the same rank. All intersectional groups among DR faculty, except Asian Men, had a lower median compensation than White men of the same rank. Among IR faculty, Asian women assistant professors faced the greatest disparity in median compensation, down to $75 K (15%) lower than White men. Among DR faculty, Black/African American women assistant professors faced the greatest disparity on median compensation, down to $48 K (10.5%) lower than White men. CONCLUSION: The study results raise important concerns about impact of intersectionality on faculty compensation in radiology which needs further study and should be addressed as part of broader drive to increase diversity, equity, and inclusion in academic radiology.

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Immediate and strategic action is needed to improve environmental sustainability and reduce the detrimental effects of climate change. Climate change is already adversely affecting the health of Canadians related to worsening air pollution and wildfire smoke, increasing frequency and intensity of extreme weather events, and expansion of vector-borne and infectious illnesses. On one hand, radiology contributes to the climate crisis by generating greenhouse gas emissions and waste during the production, manufacture, transportation, and use of medical imaging equipment and supplies. On the other hand, radiology departments are also susceptible to equipment and infrastructure damage from flooding, extreme temperatures, and power failures, as well as workforce shortages due to injury and illness, potentially disrupting radiology services and increasing costs. The Canadian Association of Radiologists' (CAR) advocacy for environmentally sustainable radiology in Canada encompasses both minimizing the detrimental effects that delivery of radiology services has on the environment and optimizing the resilience of radiology departments to increasing health needs and changing patterns of disease on imaging related to climate change. This statement provides specific recommendations and pathways to help guide radiologists, medical imaging leadership teams, industry partners, governments, and other key stakeholders to transition to environmentally sustainable, net-zero, and climate-resilient radiology organizations. Specific consideration is given to unique aspects of medical imaging in Canada. Finally, environmentally sustainable radiology programs, policies, and achievements in Canada are highlighted.

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Orbital and ocular adnexal lymphoma (OAL) affects the orbit and the surrounding structures and can arise as several subtypes with variable prognoses. We performed an observational study on the relationship between OAL subtype, diagnostic features, and prognosis to offer valuable insights into imaging techniques, such as Positron Emission Tomography with 2-deoxy-2-[fluorine-18] fluoro-D-glucose integrated with Computed Tomography (18F-FDG PET-CT), in predicting outcomes. With this aim, we retrospectively reviewed 99 patients with OALs, recording demographics, cancer subtype, location and treatment, 18FDG avidity, and bone marrow positivity. We divided patients into Group 1 (those presenting with extranodal marginal zone lymphoma-EMZL) and Group 2, including all other subtypes. The primary outcome was long-term cancer-specific survival (CSS) based on key predictors, performed through Kaplan-Meier curves and the log-rank test, with a p < 0.05 significance threshold. The mean patient age was 67 years (57-75.5). The most frequent histopathologic subtypes were EMZL lymphoma in 69 patients (69.7%), small lymphocytic lymphoma (11.1%) and diffuse-large B-cell lymphoma (10.1%). Patients of Group 1 showed a better prognosis (CSS = 80%) compared to those of Group 2 (CSS = 60%) (p = 0.01). In patients with high-grade lymphoma, the occurrence of 18FDG avidity (p = 0.003) and bone marrow positivity (p = 0.005) were related to a worse prognosis. In our group, EMZL was the most prominent subtype of OALs and exhibited the best prognosis, low 18FDG avidity, and bone marrow negativity. By observing specific patterns in radiological findings, it is possible to increase our understanding of disease progression, treatment response, and the overall prognosis in OAL patients.

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In this study tissue equivalency of the polymeric materials was investigated by comparing with ICRP 110 Male Adult Computational Phantom tissues. For this purpose, radiological properties of polyamide (PA), high density polyethylene (HDPE), ultra-high molecular weight polyethylene (UHMWPE), polypropylene (PP), polyvinyl chloride (PVC), polytetrafluoroethylene (PTFE), polyethylene terephthalate (PET), polyoxymethylene (POM) and polyurethane foam (PU FOAM) were evaluated in the diagnostic energy range (15-150 keV). The radiological properties of the materials and ICRP 110 Male and Female Adult Computational Phantom tissues were calculated with Phy-X/PSD software. No major differences were seen except for sex-specific organs, and comparisons were made using an adult male phantom. To confirm the results experimentally, a chest phantom was designed with the polymeric materials. The phantom was scanned by Siemens SOMATOM Edge CT device with tube voltage of 120 kVp and Hounsfield Unit (HU) values were measured. In addition, HU values were calculated using theoretical relationships and significant agreement was obtained between measured and calculated HUs. It was determined that PA, PP, UHMWPE and HDPE were equivalent to muscle and adipose tissue, PVC and PTFE were equivalent to mineral bone, PET and POM were equivalent to spongiosa bone and PU FOAM was equivalent to lung tissue.

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This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

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PURPOSE: To assess the performance of GPT-4 Turbo with Vision (GPT-4TV), OpenAI's latest multimodal large language model, by comparing its ability to process both text and image inputs with that of the text-only GPT-4 Turbo (GPT-4 T) in the context of the Japan Diagnostic Radiology Board Examination (JDRBE). MATERIALS AND METHODS: The dataset comprised questions from JDRBE 2021 and 2023. A total of six board-certified diagnostic radiologists discussed the questions and provided ground-truth answers by consulting relevant literature as necessary. The following questions were excluded: those lacking associated images, those with no unanimous agreement on answers, and those including images rejected by the OpenAI application programming interface. The inputs for GPT-4TV included both text and images, whereas those for GPT-4 T were entirely text. Both models were deployed on the dataset, and their performance was compared using McNemar's exact test. The radiological credibility of the responses was assessed by two diagnostic radiologists through the assignment of legitimacy scores on a five-point Likert scale. These scores were subsequently used to compare model performance using Wilcoxon's signed-rank test. RESULTS: The dataset comprised 139 questions. GPT-4TV correctly answered 62 questions (45%), whereas GPT-4 T correctly answered 57 questions (41%). A statistical analysis found no significant performance difference between the two models (P = 0.44). The GPT-4TV responses received significantly lower legitimacy scores from both radiologists than the GPT-4 T responses. CONCLUSION: No significant enhancement in accuracy was observed when using GPT-4TV with image input compared with that of using text-only GPT-4 T for JDRBE questions.

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A 26-year-old male with Behçet's disease (BD) presented with recurrent oral and genital ulcers, bilateral pneumonia, and a left lower pulmonary artery aneurysm. Endovascular coil embolization was initially performed, followed by treatment with prednisone, colchicine, and azathioprine. Despite treatment, disease progression occurred, requiring additional embolization, intravenous pulse methylprednisolone, and cyclophosphamide. Ultimately, a combination of medical and endovascular interventions resolved the pulmonary arterial aneurysms. This case highlights BD's systemic inflammatory nature and vascular complications like pulmonary artery aneurysms. It emphasizes the importance of early detection and individualized, multidisciplinary care for such complications.

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Abnormal uterine bleeding (AUB) poses a multifaceted challenge in women's health, necessitating an integrated approach that addresses its diverse etiologies and clinical presentations. The International Federation of Gynecology and Obstetrics PALM-COEIN classification system provides a systematic approach to the diagnosis of AUB in non-pregnant women, based on clinical and imaging-based categorization of causes into structural (Polyps, Adenomyosis, Leiomyomas and Malignancy; PALM), and non-structural causes (Coagulopathies, Ovulatory disorders, primary Endometrial disorders, Iatrogenic and Not otherwise classified; COEIN). On the other hand, placental disorders, uterine rupture, ectopic pregnancy and retained products of conceptions are the main causes of uterine bleeding during pregnancy and in the peripartum period. Ultrasound is usually the first-line imaging technique for the differential diagnosis of causes of AUB. Computed Tomography may be useful if ultrasound findings are unclear, especially in emergency settings. Magnetic resonance imaging, when indicated, is an excellent second-line diagnostic tool for a better non-invasive characterization of the underlying cause of AUB. This pictorial review aims to illustrate the main causes of AUB from the point of view of diagnostic imaging and to show not-so-common cases that can be treated by means of interventional radiology.

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INTRODUCTION: The complex practice environment and responsibilities incumbent on diagnostic radiologists creates a workflow susceptible to disruption. While interruptions have been shown to contribute to medical errors in the healthcare delivery environment, the exact impact on highly subspecialized services such as diagnostic radiology is less certain. One potential source of workflow disruption is the use of a departmental instant messaging system (Webex), to facilitate communications between radiology faculty, residents, fellows, and technologists. A retrospective review was conducted to quantify the frequency of interruption experienced by our neuroradiology fellows. MATERIALS AND METHODS: Data logs were gathered comprising all instant messages sent and received within the designated group chats from July 5-December 31, 2021, during weekday shifts staffed by neuroradiology fellows. Interruptions per shift were calculated based on month, week, and day of the week. RESULTS: 14,424 messages were sent across 289 total shifts. The 6 fellows assigned to the main neuroradiology reading room sent 3258 messages and received 10,260 messages from technologists and other staff. There was an average of 50 interruptions per shift when examined by month (range 48-53), and 52 interruptions per shift when examined by day of the week (range 40-60). CONCLUSION: Neuroradiology fellows experience frequent interruptions from the departmental instant messaging system. These disruptions, when considered in conjunction with other non-interpretative tasks, may have negative implications for workflow efficiency, requiring iterative process improvements when incorporating new technology into the practice environment of diagnostic radiology.

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The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

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