Diagnostic and interventional radiology最新文献

Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.

Purpose: To explore sonographic cortical bone thickness (CoT) as a potential indicator of bone mineral density (BMD) measured by dual-energy X-ray absorptiometry for screening and diagnosing pediatric osteoporosis.

Methods: A prospective study included 41 osteopenic or osteoporotic patients and 52 healthy children. Radius cortical thickness (R-CoT), tibial cortical thickness (T-CoT), and second metatarsal cortical thickness (M-CoT) were measured by B-mode ultrasound; CoT values were compared between groups and the correlation between BMD and CoT was examined.

Results: There were no significant differences in R-CoT (P = 0.433), T-CoT (P = 0.057), and M-CoT (P = 0.978) values between the patient and control groups. No significant correlations were found between BMD T-scores and R-CoT (r = -0.073, P = 0.490), T-CoT (r = -0.154, P = 0.141), and M-CoT (r = 0.047, P = 0.657) values.

Conclusion: Sonographic CoT values in children do not correlate with BMD values. Unlike in adults, sonographic CoT measurements do not appear to have a role in assessing BMD in the pediatric population.

Purpose: To systematically investigate the impact of image preprocessing parameters on the segmentation-based reproducibility of magnetic resonance imaging (MRI) radiomic features.

Methods: The MRI scans of 50 patients were included from the multi-institutional Brain Tumor Segmentation 2021 public glioma dataset. Whole tumor volumes were manually segmented by two independent readers, with the participation of eight readers. Radiomic features were extracted from two sequences: T2-weighted (T2) and contrast-enhanced T1-weighted (T1ce). Two methods were considered for discretization: bin count (i.e., relative discretization) and bin width (i.e., absolute discretization). Ten discretization (five for each method) and five resampling parameters were varied while other parameters were fixed. The intraclass correlation coefficient (ICC) was used for reliability analysis based on two commonly used cut-off values (0.75 and 0.90).

Results: Image preprocessing parameters had a significant impact on the segmentation-based reproducibility of radiomic features. The bin width method yielded more reproducible features than the bin count method. In discretization experiments using the bin width on both sequences, according to the ICC cut-off values of 0.75 and 0.90, the rate of reproducible features ranged from 70% to 84% and from 35% to 57%, respectively, with an increasing percentage trend as parameter values decreased (from 84 to 5 for T2; 100 to 6 for T1ce). In the resampling experiments, these ranged from 53% to 74% and from 10% to 20%, respectively, with an increasing percentage trend from lower to higher parameter values (physical voxel size; from 1 x 1 x 1 to 2 x 2 x 2 mm³).

Conclusion: The segmentation-based reproducibility of radiomic features appears to be substantially influenced by discretization and resampling parameters. Our findings indicate that the bin width method should be used for discretization and lower bin width and higher resampling values should be used to allow more reproducible features.

Purpose: The categorization of Breast Imaging Reporting and Data System (BI-RADS) 3 lesions is not as clear in magnetic resonance imaging (MRI) as it is in mammography (MG). With the increasing number of MRI scans currently being conducted globally, incidentally detected lesions falling into the probably benign category are frequently being observed. In this study, our aim was to investigate the imaging characteristics and follow-up results of BI-RADS 3 lesions detected by MRI and to determine their malignancy rates.

Methods: Breast MRI scans performed between January 2010 and January 2020 and classified as BI-RADS 3 lesions were retrospectively analyzed. The study included 216 lesions with known biopsy or surgical excision results or with at least one year of radiological follow-up. We assessed the patients' age, the presence of breast cancer, the follow-up interval, and the imaging findings at the beginning and during the follow-up. Lesions that remained stable, disappeared, or decreased in size and had a benign histopathological diagnosis were classified as benign. Lesions with the histopathological diagnosis of malignancy, identified by either biopsy or surgical excision, were classified as malignant. We determined the malignancy rate based on the histopathology and follow-up results.

Results: Considering the follow-up results of all cases, 8% of lesions were excised, 0.5% decreased in size, 1.4% became enlarged, 17.1% disappeared, and 73% remained stable. The malignancy rate was 2.8%. A significant relationship was found between lesion shape and malignancy, as progression to malignancy was more likely in round lesions than in other types. An irregular margin, heterogeneous enhancement, and kinetic curve (type 2) features were significant for lesion upgrade to malignancy.

Conclusion: The malignancy rate in BI-RADS 3 lesions detected by MRI is low and falls within the accepted cancer rate for MG and sonography. Changes in size, morphology, and enhancement pattern should be considered in terms of malignancy development during follow-up. The follow-up intervals should be determined on a case-by-case basis.

Purpose: To compare images generated by synthetic diffusion-weighted imaging (sDWI) with those from conventional DWI in terms of their diagnostic performance in detecting breast lesions when performing breast magnetic resonance imaging (MRI).

Methods: A total of 128 consecutive patients with 135 enhanced lesions who underwent dynamic MRI between 2018 and 2021 were included. The sDWI and DWI signals were compared by three radiologists with at least 10 years of experience in breast radiology.

Results: Of the 82 malignant lesions, 91.5% were hyperintense on sDWI and 73.2% were hyperintense on DWI. Of the 53 benign lesions, 71.7% were isointense on sDWI and 37.7% were isointense on DWI. sDWI provides accurate signal intensity data with statistical significance compared with DWI (P < 0.05). The diagnostic performance of DWI and sDWI to differentiate malignant breast masses from benign masses was as follows: sensitivity 73.1% [95% confidence interval (CI): 62-82], specificity 37.7% (95% CI: 24-52); sensitivity 91.5% (95% CI: 83-96), specificity 71.7% (95% CI: 57-83), respectively. The diagnostic accuracy of DWI and sDWI was 59.2% and 83.7%, respectively. However, when the DWI images were evaluated with apparent diffusion coefficient mapping and compared with the sDWI images, the sensitivity was 92.68% (95% CI: 84-97) and the specificity was 79.25% (95% CI: 65-89) with no statistically significant difference. The inter-reader agreement was almost perfect (P < 0.001).

Conclusion: Synthetic DWI is superior to DWI for lesion visibility with no additional acquisition time and should be taken into consideration when conducting breast MRI scans. The evaluation of sDWI in routine MRI reporting will increase diagnostic accuracy.

With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions.

Purpose: The current meta-analysis aimed to compare the diagnostic performance of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) with ¹⁸F-FDG PET/magnetic resonance imaging (MRI) in non-small cell lung cancer (NSCLC) lymph node metastasis staging.

Methods: We searched the PubMed, Web of Science, and Embase databases for relevant articles between November 1992 and September 2022. Studies evaluating the head-to-head comparison of ¹⁸F-FDG PET/CT and ¹⁸F-FDG PET/MRI for lymph node metastasis in patients with NSCLC were included. The quality of each study was assessed using the Quality Assessment of Diagnostic Performance Studies-2 tool.

Results: The analysis includes six studies with a total of 434 patients. The pooled sensitivity of ¹⁸F-FDG PET/CT and ¹⁸F-FDG PET/MRI was 0.78 [95% confidence interval (CI): 0.59-0.90] and 0.84 (95% CI: 0.68-0.93), and the pooled specificity was 0.87 (95% CI: 0.72-0.94) and 0.87 (95% CI: 0.80-0.92), respectively. The accuracy of ¹⁸F-FDG PET/CT and ¹⁸F-FDG PET/MRI was 0.81 (95% CI: 0.71-0.90) and 0.84 (95% CI: 0.75-0.92), respectively. When the pre-test probability was set at 50%, the post-test probability for ¹⁸F-FDG PET/CT could increase to 85%, and the post-test probability for ¹⁸F-FDG PET/MRI could increase to 87%.

Conclusion: ¹⁸F-FDG PET/CT and ¹⁸F-FDG PET/MRI have similar diagnostic performance in detecting lymph node metastasis in NSCLC. However, the results of this study were from a small sample study, and further studies with larger sample sizes are needed.

Purpose: To propose a novel, inclusive classification that facilitates the selection of the appropriate donor and surgical technique in living-donor liver transplantation (LDLT).

Methods: The magnetic resonance cholangiography examinations of 201 healthy liver donors were retrospectively evaluated. The study group was classified according to the proposed classification. The findings were compared with the surgical technique used in 93 patients who underwent transplantation. The Couinaud, Huang, Karakas, Choi, and Ohkubo classifications were also applied to all cases.

Results: There were 118 right-lobe donors (58.7%) and 83 left-lateral-segment donors (41.3%). Fifty-six (28.8%) of the cases were classified as type 1, 136 (67.7%) as type 2, and 7 (3.5%) as type 3 in the proposed classification; all cases could be classified. The number of individuals able to become liver donors was 93. A total of 36 cases were type 1, 56 were type 2, and 1 was type 3. Of the type 1 donors, 83% required single anastomosis during transplantation, whereas six patients classified as type 1 required two anastomoses, all of which were caused by technical challenges during resection. Moreover, 51.8% of the cases classified as type 2 required additional anastomosis during transplantation. The type 3 patient required three anastomoses. The type 1 and type 2 donors required a different number of anastomoses (P < 0.001).

Conclusion: The proposed classification in this study includes all anatomical variations. This inclusive classification accurately predicts the surgical technique for LDLT.

Purpose: The reproducibility of relative cerebral blood volume (rCBV) measurements among readers with different levels of experience is a concern. This study aimed to investigate the inter-reader reproducibility of rCBV measurement of glioblastomas using the hotspot method in dynamic susceptibility contrast perfusion magnetic resonance imaging (DSC-MRI) with various strategies.

Methods: In this institutional review board-approved single-center study, 30 patients with glioblastoma were retrospectively evaluated with DSC-MRI at a 3.0 Tesla scanner. Three groups of reviewers, including neuroradiologists, general radiologists, and radiology residents, calculated the rCBV based on the number of regions of interest (ROIs) and reference areas. For statistical analysis of feature reproducibility, the intraclass correlation coefficient (ICC) and Bland-Altman plots were used. Analyses were made among individuals, reader groups, reader-group pooling, and a population that contained all of them.

Results: For individuals, the highest inter-reader reproducibility was observed between neuroradiologists [ICC: 0.527; 95% confidence interval (CI): 0.21-0.74] and between residents (ICC: 0.513; 95% CI: 0.20-0.73). There was poor reproducibility in the analyses of individuals with different levels of experience (ICC range: 0.296-0.335) and in reader-wise and group-wise pooling (ICC range: 0.296-0.335 and 0.397-0.427, respectively). However, an increase in ICC values was observed when five ROIs were used. In an analysis of all strategies, the ICC for the centrum semiovale was significantly higher than that for contralateral white matter (P < 0.001).

Conclusion: The inter-reader reproducibility of rCBV measurement was poor to moderate regardless of whether it was calculated by neuroradiologists, general radiologists, or residents, which may indicate the need for automated methods. Choosing five ROIs and using the centrum semiovale as a reference area may increase reliability for all users.

Purpose: This retrospective study evaluates the impact of preoperative lipiodol marking on the outcomes of computed tomography (CT)-guided cryoablation for histologically diagnosed sporadic renal cell carcinoma (RCC).

Methods: This study analyzed the data of 173 patients who underwent CT-guided cryoablation for histologically proven sporadic RCC at a single institution between April 2014 and December 2020. The local control rate (LCR), recurrence-free survival rate (RFSR), overall survival rate (OSR), changes in renal function, and complications in patients with (n = 85) and without (n = 88) preoperative lipiodol marking were compared.

Results: The 5-year LCR and 5-year RFSR were significantly higher in patients with lipiodol marking (97.51% and 93.84%, respectively) than in those without (72.38% and 68.10%, respectively) (P value <0.01, log-rank test). There were no significant differences between the two groups regarding the 5-year OSR (97.50% vs. 86.82%) or the deterioration in chronic kidney disease stage (12.70% vs. 16.43%). Grade ≥3 complications occurred in patients with lipiodol marking (n = 2, retroperitoneal hematoma and cerebral infarction in 1 patient each) and without (n = 5; urinary fistula in 2, colonic perforation in 2, urinary infection in 1).

Conclusion: Lipiodol marking before CT-guided cryoablation for sporadic RCC is a feasible approach to improving local control and RFS while mitigating the decline in renal function. Additionally, it may help reduce complications.