Clinical Imaging最新文献

Objective

We aimed to evaluate the diagnostic performance of dual-energy computed tomography (DECT) in detecting bone marrow edema (BME) in patients with lower limb joint injuries.

Methods

A thorough literature search was conducted across the PubMed, Embase, and Web of Science databases to identify relevant studies up to April 2024. Studies examining the diagnostic performance of DECT in detecting BME in lower limb joint injuries patients were included. Sensitivity and specificity were evaluated using the inverse variance method and transformed via the Freeman-Tukey double arcsine transformation. Furthermore, the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was utilized to evaluate the methodological quality of the included studies.

Results

This meta-analysis included 17 articles involving 625 patients. The pooled sensitivity, specificity, and AUC for DECT in detecting BME in lower limb joint injuries patients were 0.82 (95 % CI: 0.76–0.87), 0.95 (95 % CI: 0.92–0.97), and 0.95 (95 % CI: 0.93–0.97), respectively. The pooled sensitivity of DECT for detecting BME in knee, hip, and ankle joint injuries was 0.80, 0.84, and 0.80, with no significant difference among these joints (P = 0.55). The pooled specificity for knee, hip, and ankle injuries was 0.95, 0.97, and 0.89. Specificity differed significantly among the joints (P < 0.01), with the highest specificity in hip injuries.

Conclusions

Our meta-analysis indicates that DECT demonstrates high diagnostic performance in detecting BME in patients with lower limb joint injuries, with the highest specificity observed in hip joint injuries. To validate these findings, further larger prospective studies are necessary.

Purpose

MRI-based VI-RADS score aids in differentiating MIBC and NMIBC, but the experience's impact remains unexplored. We aimed to determine the effect of accumulating experience in the diagnostic performance of VI-RADS.

Methods

In our previously published series 71 primary bladder cancer patients who underwent multiparametric MRI before the transurethral resection were analyzed. The radiologist who assessed the VI-RADS scores at the time the study was performed, re-evaluated all cases after 3 years, in a blinded fashion. During these three years, more than 300 additional bladder MRIs were performed for VI-RADS assessment. The diagnostic performances of the initial and subsequent VI-RADS analyses were compared. Moreover, VIRADS results obtained by a newly trained abdominal radiologist was also compared with experienced radiologist's results. For this study, VI-RADS ≥3 was accepted for predicting MIBC.

Results

Overall 71 patients [62 (87.3 %) males, 67.4 ± 10.2 years] who underwent bladder MRI before TURBT were included. Histopathology revealed MIBC in 16 (26.2 %) cases.

The initial MRI analysis revealed VI-RADS score ≥ 3 in 36 (50.7 %) cases. The sensitivity and specificity for depicting MIBC were 75 % and 56.4 % respectively. The subsequent MRI analysis revealed VI-RADS score ≥ 3 in 23 (32.4 %) cases. The sensitivity and specificity were 93.8 % and 85.5 % respectively. The MRI analysis performed by the recently trained abdominal radiologist revealed VI-RADS score ≥ 3 in 24 (33.8 %) cases. The sensitivity and specificity were 87.5 % and 56.4 % respectively.

Conclusion

The diagnostic performance of VI-RADS for the interpretation of bladder MRI can improve over time by increasing the experience of the urogenital radiologist.

The “hot cross bun” sign is a rare radiologic sign seen on magnetic resonance imaging that can help direct the diagnosis of the cerebellar subtype of multiple system atrophy. It indicates damage to the transverse pontocerebellar fibers and can be seen in other pathologies including spinocerebellar ataxia.

The name for this radiologic sign was coined in 1998, likening the cruciform hyperintensity on imaging to the English spiced bun marked with a cross and historically eaten on the Christian religious holiday Good Friday.

Objective

The purpose of this paper was to perform an exploratory reader study to assess the utility of a web-based application in assisting non-chest radiologist in correctly diagnosing the radiographic pattern of pulmonary fibrosis.

Methods

Three non-chest radiologists with 5 to 20 years of experience individually reviewed 3 rounds of randomly chosen chest CT scans (round 1: 100 scans, round 2: 50 scans, round 3: 25 scans) from a list of patients with established diagnosis of pulmonary fibrosis. In round 1, radiologists were asked to directly record their diagnosis for the pattern of fibrosis. In round 2 and 3 they were asked to review for features provided in a web-based application and provide diagnosis based on the most likely predicted diagnosis from the application. There was an approximate 1-month interval and relevant tutorials were provided between each round. Diagnosis accuracy is reported by readers at each round.

Results

The overall accuracy increased from 63 % (n = 188/299) in round 1 to 74 % in round 3 (n = 52/70) (p = 0.0265). Difficulty in recognition of mosaic attenuation and homogeneous has led to misdiagnosis. Refining the definition for feature homogeneous increased the diagnosis accuracy of NSIP from 42 % (n = 20/48) in round 2 to 65 % (n = 24/37) in round 3(p = 0.0179). The Fleiss Kappa across readers varied from Round 1 to Round 3 with values 0.36 to 0.42.

Conclusions

Using the web-based application with refined definition for feature homogeneous helps to improve the non-subspecialty radiologist's accuracy in diagnosing different types of fibrosis.

The advent of large language models (LLMs) marks a transformative leap in natural language processing, offering unprecedented potential in radiology, particularly in enhancing the accuracy and efficiency of coronary artery disease (CAD) diagnosis. While previous studies have explored the capabilities of specific LLMs like ChatGPT in cardiac imaging, a comprehensive evaluation comparing multiple LLMs in the context of CAD-RADS 2.0 has been lacking. This study addresses this gap by assessing the performance of various LLMs, including ChatGPT 4, ChatGPT 4o, Claude 3 Opus, Gemini 1.5 Pro, Mistral Large, Meta Llama 3 70B, and Perplexity Pro, in answering 30 multiple-choice questions derived from the CAD-RADS 2.0 guidelines. Our findings reveal that ChatGPT 4o achieved the highest accuracy at 100 %, with ChatGPT 4 and Claude 3 Opus closely following at 96.6 %. Other models, including Mistral Large, Perplexity Pro, Meta Llama 3 70B, and Gemini 1.5 Pro, also demonstrated commendable performance, though with slightly lower accuracy ranging from 90 % to 93.3 %. This study underscores the proficiency of current LLMs in understanding and applying CAD-RADS 2.0, suggesting their potential to significantly enhance radiological reporting and patient care in coronary artery disease. The variations in model performance highlight the need for further research, particularly in evaluating the visual diagnostic capabilities of LLMs—a critical component of radiology practice. This study provides a foundational comparison of LLMs in CAD-RADS 2.0 and sets the stage for future investigations into their broader applications in radiology, emphasizing the importance of integrating both text-based and visual knowledge for optimal clinical outcomes.

Dr. Carolyn Meltzer is an extraordinary radiologist, researcher, mentor, and distinguished leader who deserves recognition for her immense impact on the discipline of radiology. This article serves to acknowledge and celebrate Dr. Meltzer for winning the 2023 American College of Radiology (ACR) Gold Medal. The ACR Gold Medal award is the highest honor awarded to distinguished radiologists with exceptional contributions to the field, and Dr. Meltzer is no exception. She is the 14th woman to win this prestigious award, compared to 191 male winners, although it began as an annual tradition in 1927. Throughout this piece, Dr. Meltzer discusses her journey to where she is today as the dean of Keck School of Medicine at USC, the guidance and development that lead her to this point and provides sound advice for those who seek to follow in her footsteps as a leader and mentor committed to seeking ways to advance and contribute immensely to the field of radiology.

Imaging-based screening is an important public health focus and a fundamental part of Diagnostic Radiology. Hence, radiologists should be familiar with the concepts that drive imaging-based screening practice including goals, risks, biases and clinical trials. This review article discusses an array of imaging-based screening exams including the key epidemiology and evidence that drive screening guidelines for abdominal aortic aneurysm, breast cancer, carotid artery disease, colorectal cancer, coronary artery disease, lung cancer, osteoporosis, and thyroid cancer. We will provide an overview on societal interests in screening, screening-related inequities, and opportunities to address them. Emerging evidence for opportunistic screening and the role of AI in imaging-based screening will be explored. In-depth knowledge and formalized training in imaging-based screening strengthens radiologists as clinician scientists and has the potential to broaden our public health leadership opportunities.

Summary sentence

An overview of key screening concepts, the evidence that drives today's imaging-based screening practices, and the need for radiologist leadership in screening policies and evidence development.

Objective

To compare the performance between CT-derived fractional flow reserve (CT-FFR) and ΔCT-FFR measurements in patients with deep myocardial bridging (MB) along the left anterior descending artery, and explore the potential predictors of discordance.

Methods

175 patients with deep MB who underwent coronary computed tomography angiography (CCTA) and CT-FFR assessment were included. Clinical, anatomical and atherosclerotic variables were compared between patients with concordant and discordant CT-FFR and ΔCT-FFR.

Results

30.9 % patients were discordantly classified, in which 94.4 % patients were classified as CT-FFR+/△CT-FFR-. The discordant group showed significantly higher upstream stenosis degree, distance from MB to the aorta, △CT-FFR (P 0.007, 0.009 and 0.002, respectively), and lower CT-FFR (P < 0.001). In multivariate analysis, upstream stenosis degree (P 0.023, OR 1.628, 95 % CI: 1.068–2.481) and distance from MB to the aorta (P 0.001, OR 1.04, 95 % CI: 1.016–1.064) were independent predictors for discordance between CT-FFR and ΔCT-FFR.

Conclusion

The discordance between CT-FFR and ΔCT-FFR measurements underscores the challenges in clinical decision-making, necessitating tailored approaches for MB evaluation.

The hot cross bun sign is a radiological sign seen on MRI due to pontocerebellar demyelination and loss of neurons along with preservation of the pontine tegmentum and corticospinal tracts which is classically seen in Multiple System Atrophy (MSA). Hot cross buns have been in existence since as early as the 14th century up until the point when Schrag et al. (1998) coupled the appearance of this age-old bread with the T2 imaging characteristics of MSA. Over time the radiological sign has expanded with a differential diagnosis of spinocerebellar ataxia, progressive multifocal leukoencephalopathy, paraneoplastic cerebellar degeneration, and variant Creutzfeldt-Jakob disease.