Diagnostic and interventional radiology最新文献

Purpose: To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions.

Methods: The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling.

Results: Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (P < 0.05 for all).

Conclusion: The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful.

Clinical significance: This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.

Burnout is a widespread issue among physicians, including radiologists and radiology trainees. Long hours, isolation, and substantial stress levels contribute to healthcare workers experiencing a substantially higher rate of burnout compared with other professionals. Resident physicians, continuously exposed to stressors such as new clinical situations and performance feedback, are particularly susceptible. Mentorship has proven to be an effective strategy in mitigating burnout. Various mentorship delivery models exist, all aiming to have mentors serve as role models to mentees, thereby alleviating stress and anxiety. Physician groups and healthcare enterprises have actively implemented these programs, recognizing them as both successful and cost-effective. This article explores different mentorship models, their implementation processes, and the effectiveness of these programs as a standard component of academic departments.

Purpose: This study aimed to evaluate the validity of two artificial intelligence (AI)-based bone age assessment programs, BoneXpert and VUNO Med-Bone Age (VUNO), compared with manual assessments using the Greulich-Pyle method in Turkish children.

Methods: This study included a cohort of 292 pediatric cases, ranging in age from 1 to 15 years with an equal gender and number distribution in each age group. Two radiologists, who were unaware of the bone age determined by AI, independently evaluated the bone age. The statistical study involved using the intraclass correlation coefficient (ICC) to measure the level of agreement between the manual and AI-based assessments.

Results: The ICC coefficients for the agreement between the manual measurements of two radiologists indicate almost perfect agreement. When all cases, regardless of gender and age group, were analyzed, a nearly perfect positive agreement was observed between the manual and software measurements. When bone age calculations were separated and analyzed separately for girls and boys, there was no statistically significant difference between the two AI-based methods for boys; however, ICC coefficients of 0.990 and 0.982 were calculated for VUNO and BoneXpert, respectively, and this difference of 0.008 was significant (z = 2.528, P = 0.012) for girls. Accordingly, VUNO showed higher agreement with manual measurements compared with BoneXpert. The difference between the agreements demonstrated by the two software packages with manual measurements in the prepubescent group was much more pronounced in girls compared with boys. After the age of 8 years for girls and 9 years for boys, the agreement between manual measurements and both AI software packages was equal.

Conclusion: Both BoneXpert and VUNO showed high validity in assessing bone age. Furthermore, VUNO has a statistically higher correlation with manual assessment in prepubertal girls. These results suggest that VUNO may be slightly more effective in determining bone age, indicating its potential as a highly reliable tool for bone age assessment in Turkish children.

Clinical significance: Investigating the most suitable AI program for the Turkish population could be clinically significant.

Purpose: To evaluate the diagnostic efficacy of computed diffusion-weighted imaging (DWI) in pediatric posterior fossa tumors generated using high b-values.

Methods: We retrospectively performed our study on 32 pediatric patients who had undergone brain magnetic resonance imaging for a posterior fossa tumor between January 2016 and January 2022. The DWIs were evaluated for each patient by two blinded radiologists. The computed DWI (cDWI) was mathematically derived using a mono-exponential model from images with b = 0 and 1,000 s/mm² and high b-values of 1,500, 2,000, 3,000, and 5,000 s/mm². The posterior fossa tumors were divided into two groups, low grade and high grade, and the tumor/thalamus signal intensity (SI) ratios were compared. The Mann-Whitney U test and receiver operating characteristic (ROC) curves were used to compare the diagnostic performance of the acquired DWI (DWI₁₀₀₀), apparent diffusion coefficient (ADC)₁₀₀₀ maps, and cDWI (cDWI1500, cDWI₂₀₀₀, cDWI₃₀₀₀, and cDWI₅₀₀₀).

Results: The comparison of the two tumor groups revealed that the tumor/thalamus SI ratio on the DWI₁₀₀₀ and cDWI (cDWI1500, cDWI₂₀₀₀, cDWI₃₀₀₀, and cDWI₅₀₀₀) was statistically significantly higher in high-grade tumors (P < 0.001). In the ROC curve analysis, higher sensitivity and specificity were detected in the cDWI1500, cDWI₂₀₀₀, cDWI₃₀₀₀, and ADC₁₀₀₀ maps (100%, 90.90%) compared with the DWI₁₀₀₀ (80%, 81.80%). cDWI₃₀₀₀ had the highest area under the curve (AUC) value compared with other parameters (AUC: 0.976).

Conclusion: cDWI generated using high b-values was successful in differentiating between low-grade and high-grade posterior fossa tumors without increasing imaging time.

Clinical significance: cDWI created using high b-values can provide additional information about tumor grade in pediatric posterior fossa tumors without requiring additional imaging time.

This study assesses the efficacy of the quadratus lumborum block (QLB) in the management of procedural and periprocedural pain associated with small renal mass cryoablation. To the best of our knowledge, this is the first study that examines the use of QLB for pain management during percutaneous cryoablation of renal cell carcinoma (RCC). A single-center retrospective review was conducted for patients who underwent cryoablation for RCC with QLB between October 2020 and October 2021. The primary study endpoint included a total dose of procedural conscious sedation and administered, postprocedural analgesia. Technical success in cryoablation was achieved in every case. No patients required additional analgesic during or after the procedure, and no complications resulted from the use of the QLB. The QLB procedure appears to be an effective locoregional block for the management of procedural and periprocedural pain associated with renal mass cryoablation.

As an umbrella term, artificial intelligence (AI) covers machine learning and deep learning. This review aimed to elaborate on these terms to act as a primer for radiologists to learn more about the algorithms commonly used in musculoskeletal radiology. It also aimed to familiarize them with the common practices and issues in the use of AI in this domain.

Purpose: To evaluate the performance of Microsoft Bing with ChatGPT-4 technology in analyzing abdominal computed tomography (CT) and magnetic resonance images (MRI).

Methods: A comparative and descriptive analysis was conducted using the institutional picture archiving and communication systems. A total of 80 abdominal images (44 CT, 36 MRI) that showed various entities affecting the abdominal structures were included. Microsoft Bing's interpretations were compared with the impressions of radiologists in terms of recognition of the imaging modality, identification of the imaging planes (axial, coronal, and sagittal), sequences (in the case of MRI), contrast media administration, correct identification of the anatomical region depicted in the image, and detection of abnormalities.

Results: Microsoft Bing detected that the images were CT scans with 95.4% accuracy (42/44) and that the images were MRI scans with 86.1% accuracy (31/36). However, it failed to detect one CT image (2.3%) and misidentified another CT image as an MRI (2.3%). On the other hand, it also misidentified four MRI as CT images (11.1%) and one as an X-ray (2.7%). Bing achieved an 83.75% success rate in correctly identifying abdominal regions, with 90% accuracy for CT scans (40/44) and 77.7% for MRI scans (28/36). Concerning the identification of imaging planes, Bing achieved a success rate of 95.4% for CT images and 83.3% for MRI. Regarding the identification of MRI sequences (T1-weighted and T2-weighted), the success rate was 68.75%. In the identification of the use of contrast media for CT scans, the success rate was 64.2%. Bing detected abnormalities in 35% of the images but achieved a correct interpretation rate of 10.7% for the definite diagnosis.

Conclusion: While Microsoft Bing, leveraging ChatGPT-4 technology, demonstrates proficiency in basic task identification on abdominal CT and MRI, its inability to reliably interpret abnormalities highlights the need for continued refinement to enhance its clinical applicability.

Clinical significance: The contribution of large language models (LLMs) to the diagnostic process in radiology is still being explored. However, with a comprehensive understanding of their capabilities and limitations, LLMs can significantly support radiologists during diagnosis and improve the overall efficiency of abdominal radiology practices. Acknowledging the limitations of current studies related to ChatGPT in this field, our work provides a foundation for future clinical research, paving the way for more integrated and effective diagnostic tools.

Purpose: To describe the experience of a single level 1 trauma center in the management of blunt splenic injuries (BSI).

Methods: This is a retrospective study with Institutional Review Board approval. The medical records of 450 patients with BSI treated between January 2016 and December 2022 were reviewed. Seventy-two patients were treated with splenic artery embolization (SAE), met the study criteria, and were eligible for data analysis. Spleen injuries were graded in accordance with the American Association for the Surgery of Trauma Organ Injury Scale. Univariate data analysis was performed, with P < 0.05 considered statistically significant.

Results: The splenic salvage rate was 90.3% (n = 65/72). Baseline demographics were similar between the groups (P > 0.05). Distal embolization with Gelfoam^® had similar rates of splenic salvage to proximal embolization with coils (90% vs. 94.1%, P > 0.05). There was no significant difference in the rate of splenic infarction between distal embolization with Gelfoam^® (20%, 4/20) and proximal embolization with coils (17.6%, 3/17) (P > 0.05). There was no significant difference in procedure length (68 vs. 75.8 min) or splenic salvage rate (88.5% vs. 92.1%) between proximal and distal embolization (P > 0.05). There was no significant difference in procedure length (69.1 vs. 73.6 min) or splenic salvage rate (93.1% vs. 86.4%) between Gelfoam^® and coil embolization (P > 0.05). Combined proximal and distal embolization was associated with a higher rate of splenic abscess formation (25%, 2/8) when compared with proximal (0%, 0/26) or distal (0%, 0/38) embolization alone (P = 0.0003). The rate of asymptomatic and symptomatic splenic infarction was significantly higher in patients embolized at combined proximal and distal locations (P = 0.04, P = 0.01).

Conclusion: The endovascular management of BSI is safe and effective. The overall splenic salvage rate was 90.3%. Distal embolization with Gelfoam^® was not associated with higher rates of splenic infarction when compared with proximal embolization with coils. Combined proximal and distal embolization was associated with a higher incidence of splenic infarction and splenic abscess formation.

Clinical significance: Distal splenic embolization with Gelfoam^® is safe and may be beneficial in the setting of blunt splenic trauma.