• Korean Journal of Radiology
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• v.23 no.8
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• pp.811-820
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• 2022

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.772-783
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• 2023

Objective: Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma. Materials and Methods: This study included diffuse glioma cases from The Cancer Genome Atlas (training set: 141 patients) and Asan Medical Center (validation set: 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification: group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-codeleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model. Results: Both OS and PFS were stratified according to the five imaging-based risk subgroups (P < 0.001) and three imaging-based risk groups (P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889). Conclusion: Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1151-1163
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• 2023

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7-12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343; median age [IQR], 10 [4-15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5-14] years; male: female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion: The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.466-478
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• 2022

Objective: ¹⁸F-fluorodeoxyglucose (FDG) PET/CT is often used for detecting malignancy in patients with newly diagnosed hemophagocytic lymphohistiocytosis (HLH), with acceptable sensitivity but relatively low specificity. The aim of this study was to improve the diagnostic ability of ¹⁸F-FDG PET/CT in identifying malignancy in patients with HLH by combining ¹⁸F-FDG PET/CT and clinical parameters. Materials and Methods: Ninety-seven patients (age ≥ 14 years) with secondary HLH were retrospectively reviewed and divided into the derivation (n = 71) and validation (n = 26) cohorts according to admission time. In the derivation cohort, 22 patients had malignancy-associated HLH (M-HLH) and 49 patients had non-malignancy-associated HLH (NM-HLH). Data on pretreatment ¹⁸F-FDG PET/CT and laboratory results were collected. The variables were analyzed using the Mann-Whitney U test or Pearson's chi-square test, and a nomogram for predicting M-HLH was constructed using multivariable binary logistic regression. The predictors were also ranked using decision-tree analysis. The nomogram and decision tree were validated in the validation cohort (10 patients with M-HLH and 16 patients with NM-HLH). Results: The ratio of the maximal standardized uptake value (SUVmax) of the lymph nodes to that of the mediastinum, the ratio of the SUVmax of bone lesions or bone marrow to that of the mediastinum, and age were selected for constructing the model. The nomogram showed good performance in predicting M-HLH in the validation cohort, with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.686-0.971). At an appropriate cutoff value, the sensitivity and specificity for identifying M-HLH were 90% (9/10) and 68.8% (11/16), respectively. The decision tree integrating the same variables showed 70% (7/10) sensitivity and 93.8% (15/16) specificity for identifying M-HLH. In comparison, visual analysis of ¹⁸F-FDG PET/CT images demonstrated 100% (10/10) sensitivity and 12.5% (2/16) specificity. Conclusion: ¹⁸F-FDG PET/CT may be a practical technique for identifying M-HLH. The model constructed using ¹⁸F-FDG PET/CT features and age was able to detect malignancy with better accuracy than visual analysis of ¹⁸F-FDG PET/CT images.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1269-1280
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• 2022

Objective: This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in patients with advanced chronic liver disease (ACLD). Materials and Methods: We included patients who underwent baseline and 1-year follow-up MRI from a prospective cohort that underwent gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance between November 2011 and August 2012 at a tertiary medical center. Baseline liver condition was categorized as non-ACLD, compensated ACLD, and decompensated ACLD. The liver-to-spleen signal intensity ratio (LS-SIR) and liver-to-spleen volume ratio (LS-VR) were automatically measured on the HBP images using a deep learning algorithm, and their percentage changes at the 1-year follow-up (ΔLS-SIR and ΔLS-VR) were calculated. The associations of the MRI indices with hepatic decompensation and a composite endpoint of liver-related death or transplantation were evaluated using a competing risk analysis with multivariable Fine and Gray regression models, including baseline parameters alone and both baseline and follow-up parameters. Results: Our study included 280 patients (153 male; mean age ± standard deviation, 57 ± 7.95 years) with non-ACLD, compensated ACLD, and decompensated ACLD in 32, 186, and 62 patients, respectively. Patients were followed for 11-117 months (median, 104 months). In patients with compensated ACLD, baseline LS-SIR (sub-distribution hazard ratio [sHR], 0.81; p = 0.034) and LS-VR (sHR, 0.71; p = 0.01) were independently associated with hepatic decompensation. The ΔLS-VR (sHR, 0.54; p = 0.002) was predictive of hepatic decompensation after adjusting for baseline variables. ΔLS-VR was an independent predictor of liver-related death or transplantation in patients with compensated ACLD (sHR, 0.46; p = 0.026) and decompensated ACLD (sHR, 0.61; p = 0.023). Conclusion: MRI indices automatically derived from the deep learning analysis of gadoxetic acid-enhanced HBP MRI can be used as prognostic markers in patients with ACLD.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1281-1289
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• 2022

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.683-692
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• 2019

Objective: We aimed to evaluate correlations between computed tomography (CT) parameters and pulmonary function test (PFT) parameters according to disease severity in patients with chronic obstructive pulmonary disease (COPD), and to determine whether CT parameters can be used to predict PFT indices. Materials and Methods: A total of 370 patients with COPD were grouped based on disease severity according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) I-IV criteria. Emphysema index (EI), air-trapping index, and airway parameters such as the square root of wall area of a hypothetical airway with an internal perimeter of 10 mm (Pi10) were measured using automatic segmentation software. Clinical characteristics including PFT results and quantitative CT parameters according to GOLD criteria were compared using ANOVA. The correlations between CT parameters and PFT indices, including the ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC) and FEV₁, were assessed. To evaluate whether CT parameters can be used to predict PFT indices, multiple linear regression analyses were performed for all patients, Group 1 (GOLD I and II), and Group 2 (GOLD III and IV). Results: Pulmonary function deteriorated with increase in disease severity according to the GOLD criteria (p < 0.001). Parenchymal attenuation parameters were significantly worse in patients with higher GOLD stages (P < 0.001), and Pi10 was highest for patients with GOLD III (4.41 ± 0.94 mm). Airway parameters were nonlinearly correlated with PFT results, and Pi10 demonstrated mild correlation with FEV1/FVC in patients with GOLD II and III (r = 0.16, p = 0.06 and r = 0.21, p = 0.04, respectively). Parenchymal attenuation parameters, airway parameters, EI, and Pi10 were identified as predictors of FEV₁/FVC for the entire study sample and for Group 1 (R² = 0.38 and 0.22, respectively; p < 0.001). However, only parenchymal attenuation parameter, EI, was identified as a predictor of FEV₁/FVC for Group 2 (R² = 0.37, p < 0.001). Similar results were obtained for FEV₁. Conclusion: Airway and parenchymal attenuation parameters are independent predictors of pulmonary function in patients with mild COPD, whereas parenchymal attenuation parameters are dominant independent predictors of pulmonary function in patients with severe COPD.

• Korean Journal of Radiology
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• v.2 no.2
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• pp.63-67
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• 2001

Objective: Atrophy and a high T2 signal of the hippocampus are known to be the principal MR imaging findings of hippocampal sclerosis. The purpose of this study was to determine whether or not individual MRI findings correlate with surgical outcome in patients with this condition. Materials and Methods: Preoperative MR imaging findings in 57 consecutive patients with pathologically-proven hippocampal sclerosis who underwent anterior temporal lobectomy and were followed-up for 24 months or more were retrospectively reviewed, and the results were compared with the postsurgical outcome (Engel classification). The MR images included routine sagittal T1-weighted and axial T2-weighted spin-echo images, and oblique coronal T1-weighted 3D gradient-echo and T2-weighted 2D fast spin-echo images obtained on either a 1.5 T or 1.0 T unit. The images were visually evaluated by two neuroradiologists blinded to the outcome; their focus was the presence or absence of atrophy and a high T2 hippocampal signal. Results: Hippocampal atrophy was seen in 96% of cases (55/57) [100% (53/53) of the good outcome group (Engel class I and II), and 50% (2/4) of the poor outcome group (class III and IV)]. A high T2 hippocampal signal was seen in 61% of cases (35/57) [62% (33/53) of the good outcome group and 50% (2/4) of the poor outcome group]. All 35 patients with a high T2 signal had hippocampal atrophy. 'Normal' hippocampus, as revealed by MR imaging, occurred in 4% of patients (2/57), both of whom showed a poor outcome (Engel class III). The presence or absence of hippocampal atrophy correlated well with surgical outcome (p<0.01). High T2 signal intensity did not, however, significantly correlate with surgical outcome (p>0.05). Conclusion: Compared with a high T2 hippocampal signal, hippocampal atrophy is more common and correlates better with surgical outcome. For the prediction of this, it thus appears to be the more useful indicator.

• Animal Bioscience
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• v.37 no.2
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• pp.193-202
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• 2024

Objective: Oxidative stress (OS) is a pathological process arising from the excessive production of free radicals in the body. It has the potential to alter animal gene expression and cause damage to the jejunum. However, there have been few reports of changes in the expression of long noncoding RNAs (lncRNAs) in the jejunum in piglets under OS. The purpose of this research was to examine how lncRNAs in piglet jejunum change under OS. Methods: The abdominal cavities of piglets were injected with diquat (DQ) to produce OS. Raw reads were downloaded from the SRA database. RNA-seq was utilized to study the expression of lncRNAs in piglets under OS. Additionally, six randomly selected lncRNAs were verified using quantitative real-time polymerase chain reaction (qRT-PCR) to examine the mechanism of oxidative damage. Results: A total of 79 lncRNAs were differentially expressed (DE) in the treatment group compared to the negative control group. The target genes of DE lncRNAs were enriched in gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathways. Chemical carcinogenesis-reactive oxygen species, the Foxo signaling pathway, colorectal cancer, and the AMPK signaling pathway were all linked to OS. Conclusion: Our results demonstrated that DQ-induced OS causes differential expression of lncRNAs, laying the groundwork for future research into the processes involved in the jejunum's response to OS.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.407-417
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• 2024

The objective of this study is to predict and reduce potential damage caused by chlorine gas leaks, a hazardous material, when vehicles transporting it overturn due to accidents or other incidents. The goal is to forecast the anticipated damages caused by chlorine toxicity levels (ppm) and to design effective response strategies for mitigating them. To predict potential damages, we conducted quantitative assessments using the ALOHA program to calculate the toxic effects (ppm) and damage distances resulting from chlorine leaks, taking into account potential negligence of drivers during transportation. The extent of damage from toxic gas leaks is influenced by various factors, including the amount of the leaked hazardous material and the meteorological conditions at the time of the leak. Therefore, a comprehensive analysis of damage distances was conducted by examining various scenarios that involved variations in the amount of leakage and weather conditions. Under intermediate conditions (leakage quantity: 5 tons, wind speed: 3 m/s, atmospheric stability: D), the estimated distance for exceeding the AEGL-2 level of 2 ppm was calculated to be 9 km. This concentration poses a high risk of respiratory disturbance and potential human casualties, comparable to the toxicity of hydrogen chloride. In particular, leaks in urban areas can lead to significant loss of life. In the event of a leakage incident, we proposed a plan to minimize damage by implementing appropriate response strategies based on the location and amount of the leak when an accident occurs.