• Korean Journal of Radiology
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• v.23 no.10
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• pp.986-997
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• 2022

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.

• Korean Journal of Radiology
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• v.24 no.10
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• pp.1028-1037
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• 2023

Objective: To evaluate the computed tomography (CT) features for diagnosing metastatic cervical lymph nodes (LNs) in patients with differentiated thyroid cancer (DTC) and validate the CT-based risk stratification system suggested by the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) guidelines. Materials and Methods: A total of 463 LNs from 399 patients with DTC who underwent preoperative CT staging and ultrasound-guided fine-needle aspiration were included. The following CT features for each LN were evaluated: absence of hilum, cystic changes, calcification, strong enhancement, and heterogeneous enhancement. Multivariable logistic regression analysis was performed to identify independent CT features associated with metastatic LNs, and their diagnostic performances were evaluated. LNs were classified into probably benign, indeterminate, and suspicious categories according to the K-TIRADS and the modified LN classification proposed in our study. The diagnostic performance of both classification systems was compared using the exact McNemar and Kosinski tests. Results: The absence of hilum (odds ratio [OR], 4.859; 95% confidence interval [CI], 1.593-14.823; P = 0.005), strong enhancement (OR, 28.755; 95% CI, 12.719-65.007; P < 0.001), and cystic changes (OR, 46.157; 95% CI, 5.07-420.234; P = 0.001) were independently associated with metastatic LNs. All LNs showing calcification were diagnosed as metastases. Heterogeneous enhancement did not show a significant independent association with metastatic LNs. Strong enhancement, calcification, and cystic changes showed moderate to high specificity (70.1%-100%) and positive predictive value (PPV) (91.8%-100%). The absence of the hilum showed high sensitivity (97.8%) but low specificity (34.0%). The modified LN classification, which excluded heterogeneous enhancement from the K-TIRADS, demonstrated higher specificity (70.1% vs. 62.9%, P = 0.016) and PPV (92.5% vs. 90.9%, P = 0.011) than the K-TIRADS. Conclusion: Excluding heterogeneous enhancement as a suspicious feature resulted in a higher specificity and PPV for diagnosing metastatic LNs than the K-TIRADS. Our research results may provide a basis for revising the LN classification in future guidelines.

• Korean Journal of Radiology
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• v.24 no.9
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• pp.827-837
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• 2023

Objective: To investigate the predictive value of radiomics features based on cardiac magnetic resonance (CMR) cine images for left ventricular adverse remodeling (LVAR) after acute ST-segment elevation myocardial infarction (STEMI). Materials and Methods: We conducted a retrospective, single-center, cohort study involving 244 patients (random-split into 170 and 74 for training and testing, respectively) having an acute STEMI (88.5% males, 57.0 ± 10.3 years of age) who underwent CMR examination at one week and six months after percutaneous coronary intervention. LVAR was defined as a 20% increase in left ventricular end-diastolic volume 6 months after acute STEMI. Radiomics features were extracted from the oneweek CMR cine images using the least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the selected features was evaluated using receiver operating characteristic curve analysis and the area under the curve (AUC). Results: Nine radiomics features with non-zero coefficients were included in the LASSO regression of the radiomics score (RAD score). Infarct size (odds ratio [OR]: 1.04 (1.00-1.07); P = 0.031) and RAD score (OR: 3.43 (2.34-5.28); P < 0.001) were independent predictors of LVAR. The RAD score predicted LVAR, with an AUC (95% confidence interval [CI]) of 0.82 (0.75-0.89) in the training set and 0.75 (0.62-0.89) in the testing set. Combining the RAD score with infarct size yielded favorable performance in predicting LVAR, with an AUC of 0.84 (0.72-0.95). Moreover, the addition of the RAD score to the left ventricular ejection fraction (LVEF) significantly increased the AUC from 0.68 (0.52-0.84) to 0.82 (0.70-0.93) (P = 0.018), which was also comparable to the prediction provided by the combined microvascular obstruction, infarct size, and LVEF with an AUC of 0.79 (0.65-0.94) (P = 0.727). Conclusion: Radiomics analysis using non-contrast cine CMR can predict LVAR after STEMI independently and incrementally to LVEF and may provide an alternative to traditional CMR parameters.

• Korean Journal of Radiology
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• v.24 no.9
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• pp.838-848
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• 2023

Objective: To quantitatively analyze the cardiac magnetic resonance imaging (CMR) characteristics of chemotherapy-related cardiac dysfunction (CTRCD) and explore their prognostic value for major adverse cardiovascular events (MACE). Materials and Methods: A total of 145 patients (male:female = 76:69, mean age = 63.0 years) with cancer and heart failure who underwent CMR between January 2015 and January 2021 were included. CMR was performed using a 3T scanner (Siemens). Biventricular functions, native T1 T2, extracellular volume fraction (ECV) values, and late gadolinium enhancement (LGE) of the left ventricle (LV) were compared between those with and without CTRCD. These were compared between patients with mild-to-moderate CTRCD and those with severe CTRCD. Cox proportional hazard regression analysis was used to evaluate the association between the CMR parameters and MACE occurrence during follow-up in the CTRCD patients. Results: Among 145 patients, 61 had CTRCD and 84 did not have CTRCD. Native T1, ECV, and T2 were significantly higher in the CTRCD group (1336.9 ms, 32.5%, and 44.7 ms, respectively) than those in the non-CTRCD group (1303.4 ms, 30.5%, and 42.0 ms, respectively; P = 0.013, 0.010, and < 0.001, respectively). They were not significantly different between patients with mild-to-moderate and severe CTRCD. Indexed LV mass was significantly smaller in the CTRCD group (65.0 g/m² vs. 78.9 g/mm²; P < 0.001). According to the multivariable Cox regression analysis, T2 (hazard ratio [HR]: 1.14, 95% confidence interval [CI]: 1.01-1.27; P = 0.028) and quantified LGE (HR: 1.07, 95% CI: 1.01-1.13; P = 0.021) were independently associated with MACE in the CTRCD patients. Conclusion: Quantitative parameters from CMR have the potential to evaluate myocardial changes in CTRCD. Increased T2 with reduced LV mass was demonstrated in CTRCD patients even before the development of severe cardiac dysfunction. T2 and quantified LGE may be independent prognostic factors for MACE in patients with CTRCD.

• Journal of Preventive Medicine and Public Health
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• v.57 no.3
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• pp.288-297
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• 2024

Objectives: This study explored the prevalence and predictors of alcohol and cannabis co-use among 9263 Filipino adolescents, using data from the 2019 Global School-based Student Health Survey (GSHS). Methods: We conducted a cross-sectional secondary analysis of the GSHS, targeting adolescents aged 13-17 years and excluding cases with incomplete data on alcohol and cannabis use. Our analysis employed the bivariate chi-square test of independence and multivariable logistic regression using Stata version 18 to identify significant predictors of co-use, with a p-value threshold set at 0.05. Results: The weighted prevalence of co-users was 4.2% (95% confidence interval [CI], 3.4 to 5.3). Significant predictors included male sex (adjusted odds ratio [aOR], 4.50; 95% CI, 3.31 to 6.10; p<0.001) and being in a lower academic year, specifically grade 7 (aOR, 4.08; 95% CI, 2.39 to 6.99; p<0.001) and grade 8 (aOR, 2.20; 95% CI, 1.30 to 3.72; p=0.003). Poor sleep quality was also a significant predictor (aOR, 1.77; 95% CI, 1.29 to 2.44; p<0.001), as was a history of attempted suicide (aOR, 5.31; 95% CI, 4.00 to 7.06; p<0.001). Physical inactivity was associated with lower odds of co-use (aOR, 0.45; 95% CI, 0.33 to 0.62; p<0.001). Additionally, non-attendance of physical education classes (aOR, 1.48; 95% CI, 1.06 to 2.05; p=0.021), infrequent unapproved parental checks (aOR, 1.37; 95% CI, 1.04 to 1.80; p=0.024), and lower parental awareness of free-time activities (aOR, 0.63; 95% CI, 0.45 to 0.87; p=0.005) were associated with higher odds of co-use. Factors not significantly linked to co-use included age group, being in grade 9, always feeling lonely, having no close friends, being bullied outside school, and whether a parent or guardian understood the adolescent's worries. Conclusions: The findings highlight the critical need for comprehensive interventions in the Philippines, addressing not only physical inactivity and parental monitoring but also focusing on sex, academic grade, participation in physical education classes, sleep quality, and suicide attempt history, to effectively reduce alcohol and cannabis co-use among adolescents.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1886-1893
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• 2021

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.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.1964-1973
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• 2021

Objective: To investigate the diagnostic performance of CT fractional flow reserve (CT-FFR) for myocardial bridging-related ischemia using dynamic CT myocardial perfusion imaging (CT-MPI) as a reference standard. Materials and Methods: Dynamic CT-MPI and coronary CT angiography (CCTA) data obtained from 498 symptomatic patients were retrospectively reviewed. Seventy-five patients (mean age ± standard deviation, 62.7 ± 13.2 years; 48 males) who showed myocardial bridging in the left anterior descending artery without concomitant obstructive stenosis on the imaging were included. The change in CT-FFR across myocardial bridging (ΔCT-FFR, defined as the difference in CT-FFR values between the proximal and distal ends of the myocardial bridging) in different cardiac phases, as well as other anatomical parameters, were measured to evaluate their performance for diagnosing myocardial bridging-related myocardial ischemia using dynamic CT-MPI as the reference standard (myocardial blood flow < 100 mL/100 mL/min or myocardial blood flow ratio ≤ 0.8). Results: ΔCT-FFR_systolic (ΔCT-FFR calculated in the best systolic phase) was higher in patients with vs. without myocardial bridging-related myocardial ischemia (median [interquartile range], 0.12 [0.08-0.17] vs. 0.04 [0.01-0.07], p < 0.001), while CT-FFR_systolic (CT-FFR distal to the myocardial bridging calculated in the best systolic phase) was lower (0.85 [0.81-0.89] vs. 0.91 [0.88-0.96], p = 0.043). In contrast, ΔCT-FFR_diastolic (ΔCT-FFR calculated in the best diastolic phase) and CT-FFR_diastolic (CT-FFR distal to the myocardial bridging calculated in the best diastolic phase) did not differ significantly. Receiver operating characteristic curve analysis showed that ΔCT-FFR_systolic had largest area under the curve (0.822; 95% confidence interval, 0.717-0.901) for identifying myocardial bridging-related ischemia. ΔCT-FFR_systolic had the highest sensitivity (91.7%) and negative predictive value (NPV) (97.8%). ΔCT-FFR_diastolic had the highest specificity (85.7%) for diagnosing myocardial bridging-related ischemia. The positive predictive values of all CT-related parameters were low. Conclusion: ΔCT-FFR_systolic reliably excluded myocardial bridging-related ischemia with high sensitivity and NPV. Myocardial bridging showing positive CT-FFR results requires further evaluation.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1185-1193
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• 2021

Objective: Clear cell sarcoma of the kidney (CCSK) is the second-most common but extremely rare primary renal malignancy in children after Wilms' tumor. The aims of this study were to evaluate the imaging features that could distinguish between CCSK and Wilms' tumor and to assess the features with diagnostic value for identifying CCSK. Materials and Methods: We reviewed the initial contrast-enhanced abdominal-pelvic CT scans of children with CCSK and Wilms' tumor between 2010 to 2019. Fifty-eight children (32 males and 26 females; age, 0.3-10 years), 7 with CCSK, and 51 with Wilms' tumor, were included. The maximum tumor diameter, presence of engorged perinephric vessels, maximum density of the tumor (Tmax) of the enhancing solid portion, paraspinal muscle, contralateral renal vein density, and density ratios (Tmax/muscle and Tmax/vein) were analyzed on the renal parenchymal phase of contrast-enhanced CT. Fisher's exact tests and Mann-Whitney U tests were conducted to analyze the categorical and continuous variables, respectively. Logistic regression and receiver operating characteristic curve analyses were also performed. Results: The age, sex, and tumor diameter did not differ between the two groups. Engorged perinephric vessels were more common in patients in the CCSK group (71% [5/7] vs. 16% [8/51], p = 0.005). Tmax (median, 148.0 vs. 111.0 Hounsfield unit, p = 0.004), Tmax/muscle (median, 2.64 vs. 1.67, p = 0.002), and Tmax/vein (median, 0.94 vs. 0.59, p = 0.002) were higher in the CCSK compared to the Wilms' group. Multiple logistic regression revealed that engorged vessels (odds ratio 13.615; 95% confidence interval [CI], 1.770-104.730) and Tmax/muscle (odds ratio 5.881; 95% CI, 1.337-25.871) were significant predictors of CCSK. The cutoff values of Tmax/muscle (86% sensitivity, 77% specificity) and Tmax/vein (71% sensitivity, 86% specificity) for the diagnosis of CCSK were 1.97 and 0.76, respectively. Conclusion: Perinephric vessel engorgement and greater tumor enhancement (Tmax/muscle > 1.97 or Tmax/vein > 0.76) are helpful for differentiating between CCSK and Wilms' tumor in children aged below 10 years.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.867-879
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• 2021

Objective: To compare the screening performance of diffusion-weighted (DW) MRI and combined mammography and ultrasound (US) in detecting clinically occult contralateral breast cancer in women with newly diagnosed breast cancer. Materials and Methods: Between January 2017 and July 2018, 1148 women (mean age ± standard deviation, 53.2 ± 10.8 years) with unilateral breast cancer and no clinical abnormalities in the contralateral breast underwent 3T MRI, digital mammography, and radiologist-performed whole-breast US. In this retrospective study, three radiologists independently and blindly reviewed all DW MR images (b = 1000 s/mm² and apparent diffusion coefficient map) of the contralateral breast and assigned a Breast Imaging Reporting and Data System category. For combined mammography and US evaluation, prospectively assessed results were used. Using histopathology or 1-year follow-up as the reference standard, cancer detection rate and the patient percentage with cancers detected among all women recommended for tissue diagnosis (positive predictive value; PPV2) were compared. Results: Of the 30 cases of clinically occult contralateral cancers (13 invasive and 17 ductal carcinoma in situ [DCIS]), DW MRI detected 23 (76.7%) cases (11 invasive and 12 DCIS), whereas combined mammography and US detected 12 (40.0%, five invasive and seven DCIS) cases. All cancers detected by combined mammography and US, except two DCIS cases, were detected by DW MRI. The cancer detection rate of DW MRI (2.0%; 95% confidence interval [CI]: 1.3%, 3.0%) was higher than that of combined mammography and US (1.0%; 95% CI: 0.5%, 1.8%; p = 0.009). DW MRI showed higher PPV2 (42.1%; 95% CI: 26.3%, 59.2%) than combined mammography and US (18.5%; 95% CI: 9.9%, 30.0%; p = 0.001). Conclusion: In women with newly diagnosed breast cancer, DW MRI detected significantly more contralateral breast cancers with fewer biopsy recommendations than combined mammography and US.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.