Objective: This study aimed to assess the association between exposure to gadolinium-based contrast agents (GBCAs) and the risk of parkinsonism according to the GBCA type. Materials and Methods: Individuals aged ≥40 years who underwent first-ever magnetic resonance imaging (MRI) examinations between 2011 and 2014 were identified from the Korean nationwide population-based health insurance claims database and followed up until 2022. Individuals were divided into those who underwent at least one GBCA-enhanced MRI, and those who underwent only non-enhanced MRI. GBCA-exposed individuals were further categorized into those exposed only to linear or macrocyclic GBCAs, after excluding those exposed to both types. The primary event of interest was allcause parkinsonism. Secondary events included all-cause parkinsonism requiring medication, Parkinson’s disease (PD), atypical parkinsonism, and secondary parkinsonism. Hazard ratios (HRs) were estimated using multivariable Cox proportional hazard regression models for exposure to linear and macrocyclic GBCAs, with the non-enhanced MRI group serving as a reference. The models were adjusted for age, sex, smoking status, alcohol consumption, regular exercise, body mass index, estimated glomerular filtration rate, and comorbidities. Subgroup analyses were performed according to age, sex, renal function, and history of cancer. Results: A total of 222,977 individuals were included in this study. Among them, 92,230, 48,335, and 82,412 individuals underwent non-enhanced, linear GBCA-enhanced, and macrocyclic GBCA-enhanced MRI, respectively. Exposure to linear GBCAs slightly increased the risk of all-cause parkinsonism (adjusted HR, 1.13 [97.5% confidence interval, 1.08–1.19]), while exposure to macrocyclic GBCAs did not increase the risk (adjusted HR, 1.00 [97.5% confidence interval, 0.95–1.05]).The results were similar for all-cause parkinsonism requiring medication, PD, and secondary parkinsonism, whereas no significant association was observed for atypical parkinsonism. Conclusion Exposure to linear GBCAs may slightly increase the risk of parkinsonism in adults, whereas exposure to macrocyclic GBCAs may not. Caution should be exercised when using linear GBCAs until further evidence emerges.

Purpose: To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. Materials and Methods: This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. Results: Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722–0.846] vs. AUC: 0.815 (95% CI: 0.759–0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. Conclusion A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

Purpose: This study aimed to investigate the effect of respiratory motion on hepatic steatosis quantification using ultrasound attenuation imaging (ATI) or ultrasound-guided attenuation parameter (UGAP) in pediatric patients. Materials and Methods: Pediatric patients (aged ≤18 years) who underwent liver ultrasonography (US) with ATI or UGAP between May 2022 and February 2023 were included retrospectively. Median, interquartile range (IQR), and IQR/median values were calculated in both free-breathing (FB) and breath-holding (BH) states. Subjects were divided into normal and fatty liver groups according to grayscale US. Wilcoxon signed rank test, intraclass correlation coefficient (ICC), and linear regression test were used. Results: A total of 83 patients (M:F=46:37, median age 10 years, range 6–17 years) was included, with 55 patients in the ATI group and 28 patients in the UGAP group. The measured values of ATI and UGAP were not significantly different between FB and BH.The ICC values between FB and BH states were 0.950 [95% confidence interval (CI) 0.916–0.971] for median ATI and 0.786 (95% CI 0.591–0.894) for median UGAP. FB and BH status did not significantly affect the median ATI and UGAP (p=0.852, 0.531, respectively). The IQR/median value showed a significant association with age only in the FB status of the normal group using ATI (β= -0.014, p=0.042). Conclusion Respiratory motion does not significantly affect the measurement of ATI or UGAP. Median ATI value showed excellent agreement in FB and BH status, while UGAP showed good agreement. Younger age may affect measurement variability in FB status of the normal group using ATI.

Purpose: To identify initial abdominal computed tomography (CT) and laboratory findings prior to a diagnosis of Crohn’s disease (CD) in children. Materials and Methods: In this retrospective study, patients (≤18 year-old) who were diagnosed with CD from 2004 to 2019 and had abdominal CT just prior to being diagnosed with CD were included in the CD group. Patients (≤18 years old) who were diagnosed with infectious enterocolitis from 2018 to 2019 and had undergone CT prior to being diagnosed with enterocolitis were included as a control group. We assessed the diagnostic performances of initial CT and laboratory findings for the diagnosis of CD using logistic regression and the area under the curve (AUC). Results: In total, 107 patients (50 CD patients, 57 control patients) were included, without an age difference between groups (median 13 years old vs. 11 years old, p=0.119). On univariate logistic regression analysis, multisegmental bowel involvement, mesenteric vessel engorgement, higher portal vein/aorta diameter ratio, longer liver longitudinal diameter, lower hemoglobin (≤12.5 g/ dL), lower albumin (≤4 g/dL), and higher platelet (>320×103 /μL) levels were significant factors for CD. On multivariate analysis, multisegmental bowel involvement [odds ratio (OR) 111.6, 95% confidence interval (CI) 4.778–2605.925] and lower albumin levels (OR 0.9, 95% CI 0.891–0.993) were significant factors. When these two features were combined, the AUC value was 0.985 with a sensitivity of 96% and specificity of 100% for differentiating CD. Conclusion Multisegmental bowel involvement on CT and decreased albumin levels can help differentiate CD from infectious enterocolitis in children prior to a definite diagnosis of CD.

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

Purpose: This study aimed to evaluate the utility of the 16-cm axial volume scan technique for calculating the coronary artery calcium score (CACS) using non-enhanced chest CT. Materials and Methods: This study prospectively enrolled 20 participants who underwent both, non-enhanced chest CT (16-cm-coverage axial volume scan technique) and calciumscore CT, with the same parameters, differing only in slice thickness (in non-enhanced chest CT = 0.625, 1.25, 2.5 mm; in calcium score CT = 2.5 mm). The CACS was calculated using the conventional Agatston method. The difference between the CACS obtained from the two CT scans was compared, and the degree of agreement for the clinical significance of the CACS was confirmed through sectional analysis. Each calcified lesion was classified by location and size, and a one-to-one comparison of non-contrast-enhanced chest CT and calcium score CT was performed. Results: The correlation coefficients of the CACS obtained from the two CT scans for slice thickness of 2.5, 1.25, and 0.625 mm were 0.9850, 0.9688, and 0.9834, respectively. The mean differences between the CACS were -21.4% at 0.625 mm, -39.4% at 1.25 mm, and -76.2% at 2.5 mm slice thicknesses. Sectional analysis revealed that 16 (80%), 16 (80%), and 13 (65%) patients showed agreement for the degree of coronary artery disease at each slice interval, respectively. Inter-reader agreement was high for each slice interval. The 0.625 mm CT showed the highest sensitivity for detecting calcified lesions. Conclusion The values in the non-contrast-enhanced chest CT, using the 16-cm axial volume scan technique, were similar to those obtained using the CACS in the calcium score CT, at 0.625 mm slice thickness without electrocardiogram gating. This can ultimately help predict cardiovascular risk without additional radiation exposure.

Objective: To assess the feasibility of quantitatively assessing pancreatic steatosis using magnetic resonance imaging (MRI) and its correlation with obesity and metabolic risk factors in pediatric patients. Materials and Methods: Pediatric patients (≤ 18 years) who underwent liver fat quantification MRI between January 2016 and June 2019 were retrospectively included and divided into the obesity and control groups. Pancreatic proton density fat fraction (P-PDFF) was measured as the average value for three circular regions of interest (ROIs) drawn in the pancreatic head, body, and tail. Age, weight, laboratory results, and mean liver MRI values including liver PDFF (L-PDFF), stiffness on MR elastography, and T2* values were assessed for their correlation with P-PDFF using linear regression analysis. The associations between P-PDFF and metabolic risk factors, including obesity, hypertension, diabetes mellitus (DM), and dyslipidemia, were assessed using logistic regression analysis. Results: A total of 172 patients (male:female = 125:47; mean ± standard deviation [SD], 13.2 ± 3.1 years) were included. The mean P-PDFF was significantly higher in the obesity group than in the control group (mean ± SD, 4.2 ± 2.5% vs. 3.4 ± 2.4%; p = 0.037). L-PDFF and liver stiffness values showed no significant correlation with P-PDFF (p = 0.235 and p = 0.567, respectively). P-PDFF was significantly associated with obesity (odds ratio 1.146, 95% confidence interval 1.006–1.307, p = 0.041), but there was no significant association with hypertension, DM, and dyslipidemia. Conclusion MRI can be used to quantitatively measure pancreatic steatosis in children. P-PDFF is significantly associated with obesity in pediatric patients.

Purpose: This study was conducted to evaluate the diagnostic performance of machine learning in differentiating follicular adenoma from carcinoma using preoperative ultrasonography (US). Methods: In this retrospective study, preoperative US images of 348 nodules from 340 patients were collected from two tertiary referral hospitals. Two experienced radiologists independently reviewed each image and categorized the nodules according to the 2015 American Thyroid Association guideline. Categorization of a nodule as highly suspicious was considered a positive diagnosis for malignancy. The nodules were manually segmented, and 96 radiomic features were extracted from each region of interest. Ten significant features were selected and used as final input variables in our in-house developed classifier models based on an artificial neural network (ANN) and support vector machine (SVM). The diagnostic performance of radiologists and both classifier models was calculated and compared. Results: In total, 252 nodules from 245 patients were confirmed as follicular adenoma and 96 nodules from 95 patients were diagnosed as follicular carcinoma. As measures of diagnostic performance, the average sensitivity, specificity, and accuracy of the two experienced radiologists in discriminating follicular adenoma from carcinoma on preoperative US images were 24.0%, 84.0%, and 64.8%, respectively. The sensitivity, specificity, and accuracy of the ANN and SVM-based models were 32.3%, 90.1%, and 74.1% and 41.7%, 79.4%, and 69.0%, respectively. The kappa value of the two radiologists was 0.076, corresponding to slight agreement. Conclusion Machine learning-based classifier models may aid in discriminating follicular adenoma from carcinoma using preoperative US.

Purpose: The purpose of this study was to identify the optimal timing for screening spinal cord ultrasonography (US) to detect filum terminale lipoma in infants. Methods: We retrospectively reviewed infants (<12 months old) who underwent repeated spinal cord US between April 2011 and January 2019. We excluded infants if they only had one US examination, or if they had lesions other than filum terminale lipoma. Infants with filum terminale lipoma on magnetic resonance imaging were included in the lipoma group and the others in the control group. A linear mixed model was used to assess differences in the growth pattern of filum terminale thickness by age and group. The cutoff thickness on US and its diagnostic performance were assessed according to age. Results: Among 442 infants with 901 US examinations, 46 were included in the lipoma group and 58 in the control group. Sixty-seven infants had unmeasurable filum terminale thickness on initial US, including 55 neonates (82.1%) before 1 month of age. The lipoma group had significantly greater filum terminale thickness than the control group (P<0.001). Thickness increased with age in the lipoma group (P=0.027). The sensitivity of US was 87.5% and the area under the receiver operating characteristic curve was 0.949 (95% confidence interval, 0.849 to 0.991) with a cutoff value of 1.1 mm in 4- to 6-month-old infants. Conclusion Screening spinal cord US could effectively diagnose filum terminale lipoma in 4- to 6-month-old infants with a cutoff thickness of 1.1 mm. Spinal cord US can be used to screen young infants with intraspinal abnormalities.