Objective: Evidence supports the efficacy of coronary computed tomography angiography (CCTA)-based risk scores in cardiovascular risk stratification of patients with suspected coronary artery disease (CAD). We aimed to compare two CCTA-based risk score algorithms, Leiden and Confirm scores, in patients with diabetes mellitus (DM) and suspected CAD. Materials and Methods: This single-center prospective cohort study consecutively included 1241 DM patients (54.1% male, 60.2 ± 10.4 years) referred for CCTA for suspected CAD in 2015-2017. Leiden and Confirm scores were calculated and stratified as < 5 (reference), 5-20, and > 20 for Leiden and < 14.3 (reference), 14.3-19.5, and > 19.5 for Confirm. Major adverse cardiovascular events (MACE) were defined as the composite outcomes of cardiovascular death, nonfatal myocardial infarction (MI), stroke, and unstable angina requiring hospitalization. The Cox model and Kaplan-Meier method were used to evaluate the effect size of the risk scores on MACE. The area under the curve (AUC) at the median follow-up time was also compared between score algorithms. Results: During a median follow-up of 31 months (interquartile range, 27.6-37.3 months), 131 of MACE were recorded, including 17 cardiovascular deaths, 28 nonfatal MIs, 64 unstable anginas requiring hospitalization, and 22 strokes. An incremental incidence of MACE was observed in both Leiden and Confirm scores, with an increase in the scores (log-rank p < 0.001). In the multivariable analysis, compared with Leiden score < 5, the hazard ratios for Leiden scores of 5-20 and > 20 were 2.37 (95% confidence interval [CI]: 1.53-3.69; p < 0.001) and 4.39 (95% CI: 2.40-8.01; p < 0.001), respectively, while the Confirm score did not demonstrate a statistically significant association with the risk of MACE. The Leiden score showed a greater AUC of 0.840 compared to 0.777 for the Confirm score (p < 0.001). Conclusion: CCTA-based risk score algorithms could be used as reliable cardiovascular risk predictors in patients with DM and suspected CAD, among which the Leiden score outperformed the Confirm score in predicting MACE.

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). Materials and Methods: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. Results: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of "pre-PTA" shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, Grad-CAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. Conclusion: Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

Objective: To investigate the agreement and reliability of estimating the volumes and normative percentiles (N%) of segmented brain regions among NeuroQuant (NQ), DeepBrain (DB), and FreeSurfer (FS) software programs, focusing on the comparison between NQ and DB. Materials and Methods: Three-dimensional T1-weighted images of 145 participants (48 healthy participants, 50 patients with mild cognitive impairment, and 47 patients with Alzheimer's disease) from a single medical center (SMC) dataset and 130 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were included in this retrospective study. All images were analyzed with DB, NQ, and FS software to obtain volume estimates and N% of various segmented brain regions. We used Bland-Altman analysis, repeated measures ANOVA, reproducibility coefficient, effect size, and intraclass correlation coefficient (ICC) to evaluate inter-method agreement and reliability. Results: Among the three software programs, the Bland-Altman plot showed a substantial bias, the ICC showed a broad range of reliability (0.004-0.97), and repeated-measures ANOVA revealed significant mean volume differences in all brain regions. Similarly, the volume differences of the three software programs had large effect sizes in most regions (0.73-5.51). The effect size was largest in the pallidum in both datasets and smallest in the thalamus and cerebral white matter in the SMC and ADNI datasets, respectively. N% of NQ and DB showed an unacceptably broad Bland-Altman limit of agreement in all brain regions and a very wide range of ICC values (-0.142-0.844) in most brain regions. Conclusion: NQ and DB showed significant differences in the measured volume and N%, with limited agreement and reliability for most brain regions. Therefore, users should be aware of the lack of interchangeability between these software programs when they are applied in clinical practice.

Objective: To compare the clinical and radiological features of various etiologies of chronic diffuse lacrimal gland enlargement. Materials and Methods: We retrospectively reviewed 91 consecutive patients who underwent surgical biopsy for chronic diffuse lacrimal gland enlargement and were diagnosed with non-specific dacryoadenitis (DA) (n = 42), immunoglobulin G4-related dacryoadenitis (IgG4-RD) (n = 33), and lymphoma (n = 16). Data on patient demographics, clinical presentation, and CT imaging findings (n = 73) and MRI (n = 43) were collected. The following radiologic features of lacrimal gland enlargement were evaluated: size, unilaterality, wedge sign, angle with the orbital wall, heterogeneity, signal intensity, degree of enhancement, patterns of dynamic contrast-enhanced, and apparent diffusion coefficient value. Radiological features outside the lacrimal glands, such as extra-lacrimal orbital involvement and extra-orbital head and neck involvement, were also evaluated. The clinical and radiological findings were compared among the three diseases. Results: Compared to the DA and IgG4-RD groups, the lymphoma group was significantly older (mean 59.9 vs. 46.0 and 49.4 years, respectively; p = 0.001) and had a higher frequency of unilateral involvement (62.5% vs. 31.0% and 15.2%, respectively; p = 0.004). Compared to the IgG4-RD and lymphoma groups, the DA group had significantly smaller lacrimal glands (2.3 vs. 2.8 and 3.3 cm, respectively; p < 0.001) and a lower proportion of cases with a wedge sign (54.8% vs. 84.8% and 87.5%, respectively; p = 0.005). The IgG4-RD group showed more frequent involvement of the extra-orbital head and neck structures, including the infraorbital nerve (36.4%), paranasal sinus (72.7%), and salivary gland (58.6%) compared to the DA and lymphoma groups (4.8%-28.6%) (all p < 0.005). Conclusion: Patient age, unilaterality, lacrimal gland size, wedge sign, and extra-orbital head and neck involvement differed significantly different between lymphoma, DA, and IgG4-RD. Our results will be useful for the differential diagnosis and proper management of chronic lacrimal gland enlargement.

Objective: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. Materials and Methods: This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent ¹⁸F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called "individual contribution index" were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUV_mean) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. Results: The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10^-3 and (0.0967 ± 0.0545) × 10^-3 in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785-0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUV_mean of the limbic network (p < 0.001). Conclusion: The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.

Objective: The present study aimed to assess the relationship between incidental abnormalities on thoracic computed tomography (CT) and mortality in a general screening population using a long-term follow-up analysis. Materials and Methods: We retrospectively collected the medical records and CT images of 840 participants (mean age ± standard deviation [SD], 58.5 ± 6.7 years; 564 male) who underwent thoracic CT at a single health promotion center between 2007 and 2010. Two thoracic radiologists independently reviewed all CT images and evaluated any incidental abnormalities (interstitial lung abnormality [ILA], emphysema, coronary artery calcification [CAC], aortic valve [AV] calcification, and pulmonary nodules). Kaplan-Meier analysis with log-rank and z-tests was performed to assess the relationship between incidental CT abnormalities and all-cause mortality in the subsequent follow-up. Cox proportional hazards regression was performed to further identify risk factors of all-cause mortality among the incidental CT abnormalities and clinical factors. Results: Among the 840 participants, 55 (6%), 171 (20%), 288 (34%), 396 (47%), and 97 (11%) had findings of ILA, emphysema, CAC, pulmonary nodule, and AV calcification, respectively, on initial CT. The participants were followed up for a mean period ± SD of 10.9 ± 1.4 years. All incidental CT abnormalities were associated with all-cause mortality in univariable analysis (p < 0.05). However, multivariable analysis further revealed fibrotic ILA as an independent risk factor for all-cause mortality (hazard ratio, 2.52 [95% confidence interval, 1.02-6.22], p = 0.046). ILA were also identified as an independent risk factor for lung cancer or respiratory disease-related deaths. Conclusion: Incidental abnormalities on screening thoracic CT were associated with increased mortality during the long-term follow-up. Among incidental CT abnormalities, fibrotic ILA were independently associated with increased mortality. Appropriate management and surveillance may be required for patients with fibrotic ILA on thoracic CT obtained for general screening purposes.

Objective: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. Materials and Methods: This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). Results: Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. Conclusion: This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

Objective: Thyroidectomy bed lesions frequently show suspicious ultrasound (US) features after thyroid surgery. Fine-needle aspiration (FNA) may not provide definitive pathological information about the lesions. Although core-needle biopsy (CNB) has excellent diagnostic performance in characterizing suspicious thyroid nodules, no published studies have evaluated the performance of CNB specifically for thyroidectomy bed lesions. Therefore, we aimed to evaluate the diagnostic performance and safety of CNB for characterizing thyroidectomy bed lesions. Materials and Methods: A total of 124 thyroidectomy bed lesions in 113 patients (79 female and 34 male; age, 23-85 years) who underwent US-guided CNB between December 2008 and December 2020 were included. We reviewed the US imaging features of the target lesions and the histories of previous biopsies. The pathologic results, diagnostic performance for malignancy, and complications of CNB were analyzed. Results: All samples (100%) obtained by CNB were adequate for pathological analysis. Pathological analysis revealed inconclusive results in two lesions (1.6%). According to the reference standard, 50 lesions were ultimately malignant (40.3%), and 72 were benign (58.1%), excluding the two inconclusive lesions. The performance of CNB for diagnosing malignant thyroidectomy bed lesions in the 122 lesions had a sensitivity of 98.0% (49/50), a specificity of 100% (72/72), positive predictive value of 100% (49/49), and negative predictive value of 98.6% (72/73). Eleven lesions were referred for CNB after prior inconclusive FNA results in thyroidectomy bed lesions, for all of which CNB yielded correct conclusive pathologic diagnoses. According to the pathological analysis of CNB, there were various benign lesions (58.9%, 73/124) besides recurrence, including benign postoperative lesions other than suture granuloma (32.3%, 40/124), suture granuloma (15.3%, 19/124), remnant thyroid tissue (5.6%, 7/124), parathyroid lesions (4%, 5/124), and abscesses (1.6%, 2/124). No major or minor complications were associated with the CNB procedure. Conclusion: US-guided CNB is accurate and safe for characterizing thyroidectomy bed lesions.