Korean Journal of Radiology最新文献

Objective: To evaluate the role of visual and quantitative chest CT parameters in assessing treatment response in patients with severe asthma.

Materials and methods: Korean participants enrolled in a prospective multicenter study, named the Precision Medicine Intervention in Severe Asthma study, from May 2020 to August 2021, underwent baseline and follow-up chest CT scans (inspiration/expiration) 10-12 months apart, before and after biologic treatment. Two radiologists scored bronchiectasis severity and mucus plugging extent. Quantitative parameters were obtained from each CT scan as follows: normal lung area (normal), air trapping without emphysema (AT without emph), air trapping with emphysema (AT with emph), and airway (total branch count, Pi10). Clinical parameters, including pulmonary function tests (forced expiratory volume in 1 s [FEV1] and FEV1/forced vital capacity [FVC]), sputum and blood eosinophil count, were assessed at initial and follow-up stages. Changes in CT parameters were correlated with changes in clinical parameters using Pearson or Spearman correlation.

Results: Thirty-four participants (female:male, 20:14; median age, 50.5 years) diagnosed with severe asthma from three centers were included. Changes in the bronchiectasis and mucus plugging extent scores were negatively correlated with changes in FEV1 and FEV1/FVC (ρ = from -0.544 to -0.368, all P < 0.05). Changes in quantitative CT parameters were correlated with changes in FEV1 (normal, r = 0.373 [P = 0.030], AT without emph, r = -0.351 [P = 0.042]), FEV1/FVC (normal, r = 0.390 [P = 0.022], AT without emph, r = -0.370 [P = 0.031]). Changes in total branch count were positively correlated with changes in FEV1 (r = 0.349 [P = 0.043]). There was no correlation between changes in Pi10 and the clinical parameters (P > 0.05).

Conclusion: Visual and quantitative CT parameters of normal, AT without emph, and total branch count may be effective for evaluating treatment response in patients with severe asthma.

Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity, positive predictive value, and negative predictive value. Particularly when comparing the performance of two diagnostic tests applied on the same set of patients, these metrics are crucial for identifying the more accurate test. However, comparing predictive values presents statistical challenges because their denominators depend on the test outcomes, unlike the comparison of sensitivities and specificities. This paper reviews existing methods for comparing predictive values and proposes using the permutation test. The permutation test is an intuitive, non-parametric method suitable for datasets with small sample sizes. We demonstrate each method using a dataset from MRI and combined modality of mammography and ultrasound in diagnosing breast cancer.

Incidental pancreatic cystic lesions are a common challenge encountered by diagnostic radiologists. Specifically, given the prevalence of benign pancreatic cystic lesions, determining when to recommend aggressive actions such as surgical resection or endoscopic ultrasound with sampling is difficult. In this article, we review the common types of cystic pancreatic lesions including serous cystadenoma, intraductal papillary mucinous neoplasm, and mucinous cystic neoplasm with imaging examples of each. We also discuss high-risk or worrisome imaging features that warrant a referral to a surgeon or endoscopist and provid several examples of these features. These imaging features adhere to the latest guidelines from the International Consensus Guidelines, American Gastroenterological Association (2015), American College of Gastroenterology (2018), American College of Radiology (2010, 2017), and European Guidelines (2013, 2018). Our focused article addresses the imaging dilemma of managing incidental cystic pancreatic lesions, weighing the options between imaging follow-up and aggressive interventions.

Objective: Differentiating intracranial aneurysms from normal variants using CT angiography (CTA) or MR angiography (MRA) poses significant challenges. This study aimed to evaluate the efficacy of proton-density MRA (PD-MRA) compared to high-resolution time-of-flight MRA (HR-MRA) in diagnosing aneurysms among patients with indeterminate findings on conventional CTA or MRA.

Materials and methods: In this retrospective analysis, we included patients who underwent both PD-MRA and HR-MRA from August 2020 to July 2022 to assess lesions deemed indeterminate on prior conventional CTA or MRA examinations. Three experienced neuroradiologists independently reviewed the lesions using HR-MRA and PD-MRA with reconstructed voxel sizes of 0.25³ mm³ or 0.2³ mm³, respectively. A neurointerventionist established the gold standard with digital subtraction angiography. We compared the performance of HR-MRA, PD-MRA (0.25³-mm³ voxel), and PD-MRA (0.2³-mm³ voxel) in diagnosing aneurysms, both per lesion and per patient. The Fleiss kappa statistic was used to calculate inter-reader agreement.

Results: The study involved 109 patients (average age 57.4 ± 11.0 years; male:female ratio, 11:98) with 141 indeterminate lesions. Of these, 78 lesions (55.3%) in 69 patients were confirmed as aneurysms by the reference standard. PD-MRA (0.25³-mm³ voxel) exhibited significantly higher per-lesion diagnostic performance compared to HR-MRA across all three readers: sensitivity ranged from 87.2%-91.0% versus 66.7%-70.5%; specificity from 93.7%-96.8% versus 58.7%-68.3%; and accuracy from 90.8%-92.9% versus 63.8%-69.5% (P ≤ 0.003). Furthermore, PD-MRA (0.25³-mm³ voxel) demonstrated significantly superior per-patient specificity and accuracy compared to HR-MRA across all evaluators (P ≤ 0.013). The diagnostic accuracy of PD-MRA (0.2³-mm³ voxel) surpassed that of HR-MRA and was comparable to PD-MRA (0.25³-mm³ voxel). The kappa values for inter-reader agreements were significantly higher in PD-MRA (0.820-0.938) than in HR-MRA (0.447-0.510).

Conclusion: PD-MRA outperformed HR-MRA in diagnostic accuracy and demonstrated almost perfect inter-reader consistency in identifying intracranial aneurysms among patients with lesions initially indeterminate on CTA or MRA.

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.