Korean Journal of Radiology最新文献

Objective: The trends in chest computed tomography (CT) utilization among patients with pneumonia and its association with pneumonia incidence and mortality remain unclear. This study aimed to investigate these trends and their associations in older adults.

Materials and methods: We conducted a retrospective analysis of a Korean population aged 61-81 years at each calendar year between 2009 and 2018, using data from the government-provided National Health Insurance claims database (annual cohort size: 511,931-725,843 individuals). For each calendar year, we evaluated population-level, age- and sex-standardized pneumonia incidence and mortality rates; age- and sex-standardized frequency of chest CT acquisition, and 30-day mortality among patients with pneumonia. Pneumonia severity was categorized based on hospitalization and use of supplemental oxygen therapy. Incidence and mortality rates of pneumonia with and without chest CT were also evaluated for each severity subtype.

Results: The age- and sex-standardized incidence rate of pneumonia increased from 27.7 to 29.4 per 1,000 person-years between 2009 and 2018. Incidence rate of pneumonia with chest CT increased from 3.7 to 5.9 per 1,000 person-years, whereas incidence rate of pneumonia without chest CT remained stable (24.1 to 23.4 per 1,000 person-years). The frequency of chest CT acquisition among patients with pneumonia rose from 13.4% to 20.4%, regardless of severity. Over the same period, the age- and sex-standardized pneumonia mortality rate decreased from 51.9 to 44.2 per 100,000 person-years, and 30-day mortality among patients with pneumonia declined from 2.1% to 1.7%, regardless of severity.

Conclusion: Chest CT acquisition among older Korean patients with pneumonia increased steadily between 2009 and 2018. The population-level pneumonia incidence also increased, mainly in pneumonia diagnosed with chest CT acquisition. Further research is needed to assess the potential impact of increased chest CT utilization on mortality and the risk of overdiagnosis.

Body-composition analysis (BCA) is gaining increasing clinical importance, because abnormalities in muscle and fat distribution are closely associated with patient outcomes for various diseases. Although several methods for assessing body composition are available, including bioelectrical impedance analysis, dual-energy X-ray absorptiometry, and magnetic resonance imaging, computed tomography (CT) has emerged as the most widely used imaging modality owing to its accuracy, accessibility, and artificial intelligence-driven automated analytical capabilities. CT-based BCA enables the precise quantification of skeletal muscle and adipose tissues, but its measurements can be influenced by various technical factors, such as the contrast phase, tube current and voltage, slice thickness, reconstruction algorithm, and scanner type. These parameters particularly affect attenuation-based metrics such as muscle density. Recent technological advancements, such as iterative reconstruction, dual-energy CT, and photon-counting CT, have resulted in new capabilities but may further introduce variability. This review summarizes the effects of CT parameters on BCA results and underscores the need for awareness and consistency when performing CT-based BCA. A better understanding of these factors may improve measurement reproducibility and support broader clinical and research applications.

Dynamic contrast-enhanced (DCE) MRI is an advanced imaging technique that involves intravenous administration of a contrast agent followed by serial imaging to characterize temporal enhancement patterns. This technique provides essential information on tissue vascularity, perfusion, and capillary permeability, which are essential for characterizing soft tissue lesions. DCE-MRI plays a valuable role in differentiating benign from malignant lesions, distinguishing neoplastic from non-neoplastic conditions, evaluating histological grades, and monitoring post-treatment changes by enabling both qualitative and quantitative assessments of tissue enhancement dynamics. This review provides a comprehensive overview of the technical principles of DCE-MRI, summarizes current analytical approaches, and discusses its clinical applications in the evaluation of soft tissue tumors and tumor-like lesions.

Objective: To evaluate the diagnostic potential of viscosity (Vi) imaging and shear wave elastography (SWE) of the tibial nerve in diabetic peripheral neuropathy (DPN).

Materials and methods: This prospective study enrolled 40 patients with type II diabetes mellitus (T2DM) accompanied by DPN, 40 T2DM patients without DPN, and 40 healthy controls between January 2025 and April 2025. The bilateral tibial nerves were examined using SWE and Vi imaging to measure shear wave speed (Cs, m/s) and Vi (Pa·s). The reference standards for the DPN diagnosis comprised clinical examination, electromyography, and quantitative sensory testing. Diagnostic performance was assessed using receiver operating characteristic curve analysis and by calculating sensitivity and specificity at the optimal cutoff values for Cs and Vi. The areas under the curve (AUCs) were compared using DeLong's test.

Results: On the right side, the DPN group exhibited significantly higher Cs_mean (median: 4.05 m/s [interquartile range: 3.30-4.51] vs. 3.25 m/s [2.95-3.45]; P < 0.05) and Vi_mean (median: 3.51 Pa·s [2.70-4.58] vs. 2.43 Pa·s [2.20-2.97]; P < 0.05) compared to the non-DPN group, with similar trends observed on the left side. Both Cs_mean (AUC = 0.826 [95% confidence interval: 0.725-0.902]) and Vi_mean (AUC = 0.765 [0.657-0.852]) demonstrated favorable diagnostic performance for DPN, without a significant difference (P = 0.144). Combining Cs_mean and Vi_mean resulted in a sensitivity of 62.5% (25/40), a specificity of 95.0% (38/40), and an AUC of 0.828 (0.727-0.903), without significant improvement compared to Cs_mean or Vi_mean alone (P = 0.573 and 0.148, respectively).

Conclusion: Vi imaging quantifies nerve Vi in DPN and offers a novel, non-invasive diagnostic approach to distinguish patients with DPN from those without the condition. However, viscoelastic imaging does not provide greater diagnostic value than SWE.

The integration of artificial intelligence (AI) into radiology has the potential to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes. However, successful real-world adoption hinges on robust systems for ongoing monitoring to maintain safety, efficacy, and compliance with regulatory standards. This article delves into the critical need for such monitoring in radiology, examining current regulatory frameworks and proposing actionable strategies for overseeing technical performance, algorithm reliability, and human-AI interactions. Key topics include methods for aligning imaging studies with appropriate AI tools, addressing challenges related to data transmission and processing delays, and evaluating approaches to algorithm performance monitoring, ranging from vendor-based and specialized systems to in-house solutions. The potential of using large language models to help algorithm monitoring is also highlighted as a promising avenue. Additionally, the article explores human-AI interaction challenges, such as automation bias (the tendency of users to overly trust automated decisions), misuse, and underuse, offering strategies to mitigate these risks through structured protocols and ongoing education. By aligning regulatory requirements with practical implementation strategies, comprehensive AI monitoring can optimize diagnostic decision-making while ensuring patient safety.

Objective: This study aimed to investigate the feasibility of pretreatment ⁶⁸Ga-labeled prostate-specific membrane antigen-11 ([⁶⁸Ga]-PSMA-11) PET/CT for predicting treatment response in patients with metastatic renal cell carcinoma (mRCC) undergoing first-line therapy with tyrosine kinase inhibitors (TKIs) in combination with immune checkpoint inhibitors (ICIs).

Materials and methods: This retrospective study included 108 patients (age, 69.4 ± 6.2 years; 38 males) with mRCC who underwent pretreatment [⁶⁸Ga]-PSMA-11 PET/CT and were treated with TKIs plus ICIs between January 2019 and March 2023. Evaluation of the therapeutic response to treatment with TKIs plus ICIs was based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Univariable and multivariable logistic regression analyses were performed to identify the independent predictors of response, defined as complete response (CR) or partial response (PR), to combinations of TKIs and ICIs. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance.

Results: Of the 108 patients with mRCC, 12 (11.1%), 24 (22.2%), 45 (41.7%), and 27 (25.0%) achieved CR, PR, stable disease, and progressive disease, respectively. The area under the curve of the cumulative standardized uptake value (SUV)-volume histogram (AUC-CSH) (adjusted odds ratio [aOR], 8.358; P = 0.002) and maximum SUV (SUVmax; aOR, 1.092; P = 0.024) from pretreatment [⁶⁸Ga]-PSMA-11 PET/CT scans and the programmed death-ligand 1 (PD-L1) status (aOR, 6.248; P = 0.026) were identified as independent predictors of treatment response. A predictive model combining AUC-CSH, SUVmax, and PD-L1 status achieved an area under ROC curve (AUROC) value of 0.880 (95% confidence interval: 0.804-0.935), which was significantly higher than the AUROC values of AUC-CSH alone (0.812 [0.726-0.881], P = 0.020), SUVmax alone (0.757 [0.665-0.834], P = 0.031), and PD-L1 status alone (0.637 [0.532-0.733], P < 0.001).

Conclusion: AUC-CSH and SUVmax from pretreatment [⁶⁸Ga]-PSMA-11 PET/CT scans were independent predictors of the response to treatment with TKIs plus ICIs in mRCC. When combined with PD-L1 status, they may enhance patient stratification and facilitate clinical decision-making before treatment initiation.

Objective: To evaluate the prognostic significance of right ventricular hypertrophy (RVH) in patients with hypertrophic cardiomyopathy (HCM) using cardiac magnetic resonance imaging (CMR) and to assess its incremental value when incorporated into the established 5-year sudden cardiac death (SCD) risk prediction model.

Materials and methods: This retrospective study included 320 patients with HCM who underwent CMR and echocardiography between 2007 and 2019. RVH was defined as a right ventricular wall thickness of ≥5 mm. The primary event was heart failure (HF) hospitalization. The secondary events were a composite of HF hospitalization, cardiovascular death, and heart transplantation. The prognostic role of RVH was assessed using Kaplan-Meier survival analysis, Cox proportional hazards regression, and model performance metrics, including time-dependent receiver operating characteristic curve analysis for a 5-year follow-up and Harrell's C-index.

Results: Among 320 patients (mean age 57.5 ± 12.8 years; 66.3% men), 65 (20.1%) had RVH. Over a median follow-up of 7.7 years, 28 (8.8%) patients experienced HF hospitalization, and 34 (10.6%) experienced composite adverse events. In multivariable Cox models, RVH was an independent predictor of both events: HF hospitalization (hazard ratio [HR] = 3.19, 95% confidence interval [CI] = 1.23-8.28, P = 0.017) and the composite events (HR = 2.38, 95% CI = 1.01-5.64, P = 0.048). Incorporating RVH into the 5-year SCD risk model increased the time-dependent area under the curve, though not significant, from 0.620 to 0.725 for HF hospitalization (P = 0.057) and from 0.712 to 0.848 for composite events (P = 0.062). Harrell's C-index improved significantly from 0.606 to 0.688 (P = 0.033) and from 0.641 to 0.727 (P = 0.030), respectively.

Conclusion: CMR-detected RVH independently predicts adverse events in patients with HCM. Incorporating RVH into a conventional risk model may enhance its predictive performance, supporting the importance of routine biventricular assessments in HCM evaluation.