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Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test. PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion: Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to develop and validate a convolutional neural network (CNN) that automatically detects an aberrant right subclavian artery (ARSA) on preoperative computed tomography (CT) for thyroid cancer evaluation. Materials and Methods: A total of 556 CT with ARSA and 312 CT with normal aortic arch from one institution were used as the training set for model development. A deep learning model for the classification of patch images for ARSA was developed using two-dimension CNN from EfficientNet. The diagnostic performance of our model was evaluated using external test sets (112 and 126 CT) from two institutions. The performance of the model was compared with that of radiologists for detecting ARSA using an independent dataset of 1683 consecutive neck CT. Results: The performance of the model was achieved using two external datasets with an area under the curve of 0.97 and 0.99, and accuracy of 97% and 99%, respectively. In the temporal validation set, which included a total of 20 patients with ARSA and 1663 patients without ARSA, radiologists overlooked 13 ARSA cases. In contrast, the CNN model successfully detected all the 20 patients with ARSA. Conclusion: We developed a CNN-based deep learning model that detects ARSA using CT. Our model showed high performance in the multicenter validation. [ABSTRACT FROM AUTHOR]

Purpose: Lower-grade gliomas of histologic grades 2 and 3 follow heterogenous clinical outcomes, which necessitates risk stratification. This study aimed to evaluate whether diffusion-weighted and perfusion-weighted MRI radiomics allow overall survival (OS) prediction in patients with lower-grade gliomas and investigate its prognostic value. Materials and Methods: In this retrospective study, radiomic features were extracted from apparent diffusion coefficient, relative cerebral blood volume map, and Ktrans map in patients with pathologically confirmed lower-grade gliomas (January 2012–February 2019). The radiomics risk score (RRS) calculated from selected features constituted a radiomics model. Multivariable Cox regression analysis, including clinical features and RRS, was performed. The models’ integrated area under the receiver operating characteristic curves (iAUCs) were compared. The radiomics model combined with clinical features was presented as a nomogram. Results: The study included 129 patients (median age, 44 years; interquartile range, 37–57 years; 63 female): 90 patients for training set and 39 patients for test set. The RRS was an independent risk factor for OS with a hazard ratio of 6.01. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model in both training (iAUC, 0.82 vs. 0.72, p=0.002) and test sets (0.88 vs. 0.76, p=0.04). The radiomics nomogram combined with clinical features exhibited good agreement between the actual and predicted OS with C-index of 0.83 and 0.87 in the training and test sets, respectively. Conclusion: Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lowergrade glioma. [ABSTRACT FROM AUTHOR]

Purpose: Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients with OPSCC. Materials and Methods: Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. Results: A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564-0.959], 0.638 (95% CI, 0.404-0.871), and 0.823 (95% CI, 0.668-0.978) for Models 1, 2, and 3, respectively. The net reclassifica-tion improvement of Model 3, compared with that of Model 1, in the test set was 0.119. Conclusion: When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer brain metastases (BCBM) may involve subtypes that differ from the primary breast cancer lesion. This study aimed to develop a radiomics-based model that utilizes preoperative brain MRI for multiclass classification of BCBM subtypes and to investigate whether the model offers better prediction accuracy than the assumption that primary lesions and their BCBMs would be of the same subtype (non-conversion model) in an external validation set. Materials and Methods: The training and external validation sets each comprised 51 cases (102 cases total). Four machine learning classifiers combined with three feature selection methods were trained on radiomic features and primary lesion subtypes for prediction of the following four subtypes: 1) hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, 2) HR+/HER2+, 3) HR-/HER2+, and 4) triple-negative. After training, the performance of the radiomics-based model was compared to that of the non-conversion model in an external validation set using accuracy and F1-macro scores. Results: The rate of discrepant subtypes between primary lesions and their respective BCBMs were 25.5% (n=13 of 51) in the training set and 23.5% (n=12 of 51) in the external validation set. In the external validation set, the accuracy and F1-macro score of the radiomics-based model were significantly higher than those of the non-conversion model (0.902 vs. 0.765, p=0.004; 0.861 vs. 0.699, p=0.002). Conclusion: Our radiomics-based model represents an incremental advance in the classification of BCBM subtypes, thereby facilitating a more appropriate personalized therapy. [ABSTRACT FROM AUTHOR]

Purpose: We hypothesized that a radiomics approach could be employed to classify children with growth hormone deficiency (GHD) and idiopathic short stature (ISS) on sella magnetic resonance imaging (MRI). Accordingly, we aimed to develop a radiomics prediction model for differentiating GHD from ISS and to evaluate the diagnostic performance thereof. Materials and Methods: Short stature pediatric patients diagnosed with GHD or ISS from March 2011 to July 2020 at our institution were recruited. We enrolled 312 patients (GHD 210, ISS 102) with normal sella MRI and temporally split them into training and test sets (7:3). Pituitary glands were semi-automatically segmented, and 110 radiomic features were extracted from the coronal T2-weighted images. Feature selection and model development were conducted by applying mutual information (MI) and a light gradient boosting machine, respectively. After training, the model's performance was validated in the test set. We calculated mean absolute Shapley values for each of the selected input features using the Shapley additive explanations (SHAP) algorithm. Volumetric comparison was performed for GHD and ISS groups. Results: Ten radiomic features were selected by MI. The receiver operating characteristics curve of the developed model in the test set was 0.705, with an accuracy of 70.6%. When analyzing SHAP plots, root mean squared values had the highest impact in the model, followed by various texture features. In volumetric analysis, sagittal height showed a significant difference between GHD and ISS groups. Conclusion: Radiomic analysis of sella MRI may be able to differentiate between GHD and ISS in clinical practice for short-statured children. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. Materials and Methods: In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated. Results: The sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI: 89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall falsepositive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model. Conclusion: The present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm. [ABSTRACT FROM AUTHOR]

Firefighters have a high risk of developing cardiovascular and mental disorders due to their physical and chemical environments. However, in Korea, few studies have been conducted on environmental risk of firefighters. The Firefighter Research on the Enhancement of Safety and Health (FRESH) study aimed to discover the risk factors for cardiovascular disease and mental disorders among firefighters. Former and current firefighters were recruited from three university hospitals. A total of 1022 participants completed baseline health examinations from 2016 to 2017. All participants were scheduled for follow-ups every 2 years. Baseline health survey, laboratory testing of blood and urine samples, blood heavy metal concentration, urine polycyclic aromatic hydrocarbons (PAHs) metabolites, stress-related hormone test, natural killer cell activity, as well as physical and mental health examinations that focused on cardiovascular and mental disorders, were conducted. In addition, 3 Tesla (3T) brain magnetic resonance imaging (MRI) and neuropsychological tests were also performed to investigate structural and functional changes in the brains of 352 firefighters aged >40 years or new hires with less than 1 year of service. [ABSTRACT FROM AUTHOR]

Purpose: To investigate horizontal image disparity in three-dimensional (3-D) perception using 3-D animations in normal control patients and patients with intermittent exotropia, anisometropic amblyopia, and partially accommodative esotropia. Materials and Methods: A total of 133 subjects were included. Stereopsis was measured using the Titmus Stereo test (Stereo Optical Inc., Chicago, IL, USA) and a 3-D stereopsis test with a 15 inch 3-D display laptop, adjusting 3-D parameters of 0 mm horizontal disparity to 15 mm horizontal disparity. Results: When compared with normal controls, the average threshold of the 3-D stereopsis test was significantly reduced for esotropia patients (p<0.001) and for anisometric amblyopia patients (p<0.001), compared to normal controls. No significant difference was observed between normal controls and intermittent exotropia patients (p=0.082). The 3-D stereopsis test was correlated with the Titmus Stereo test (Spearman's rho=0.690, p<0.001). Mean difference in stereoacuity was 1.323 log seconds of arc (95% limits of agreement: 0.486 to 2.112), and 125 (92.5%) patients were within the limits of agreement. Conclusion: This study demonstrated that a 3-D stereopsis test with animation is highly correlated with the Titmus Stereo test; nevertheless, 3-D stereopsis with animations generates more image disparities than the conventional Titmus Stereo test. The 3-D stereopsis test is highly predictive for estimating real stereopsis in a 3-D movie theater. [ABSTRACT FROM AUTHOR]

Purpose: To achieve maximal safe resection during brain tumor surgery, functional image-merged neuronavigation is widely used. We retrospectively reviewed our cases in which diffusion tensor image (DTT)-merged functional neuronavigation was performed during surgery. Materials and Methods: Between November 2008 and May 2010, 123 patients underwent surgery utilizing DTI-merged neuronavigation. Anatomical magnetic resonance images (MRI) were obtained preoperatively and fused with DTI of major white matter tracts, such as the corticospinal tract, optic radiation, or arcuate fasciculus. We used this fused image for functional neuronavigation during brain tumor surgery of eloquent areas. We checked the DTI images together with postoperative MRI images and evaluated the integrity of white matter tracts. Results: A single white matter tract was inspected in 78 patients, and two or more white matter tracts were checked in 45 patients. Among the 123 patients, a grossly total resection was achieved in 90 patients (73.2%), subtotal resection in 29 patients (23.6%), and partial resection in 4 patients (3.3%). Postoperative neurologic outcomes, compared with preoperative function, included the following: 100 patients (81.3%) displayed improvement of neurologic symptoms or no change, 7 patients (5.7%) experienced postoperative permanent neurologic deterioration (additional or aggravated neurologic symptoms), and 16 patients (13.0%) demonstrated transient worsening. Conclusion: DTI-merged functional neuronavigation could be a useful tool in brain tumor surgery for maximal safe resection. However, there are still limitations, including white matter tract shift, during surgery and in DTI itself. Further studies should be conducted to overcome these limitations. [ABSTRACT FROM AUTHOR]

Purpose: Single dose gadolinium (Gd) enhanced fluid-attenuated inversion recovery (FLAIR) is helpful for visualizing superficial parenchymal metastases. However, the usefulness of FLAIR with a higher dose of Gd is uncertain. The aim of our study was two-folds: first, to prove that the signal to noise ratio (SNR) of small brain metastases is higher than large brain metastases on double-dose (DD) enhanced FLAIR and, second, to explore the added value of DD Gd enhanced FLAIR in relation to T1 GRE for evaluating small brain metastases. Materials and Methods: For the first purpose, 50 pairs of small (2 mm5 mm) were included. The difference in the SNR and contrast ratio (CR) between small and large metastases on DD Gd-enhanced 3D T2 FLAIR was compared by Wilcoxon signed-rank tests. For the second purpose, a total of 404 small metastases were included. The diagnostic sensitivities between 3D T1 gradient echo (GRE) alone and combined results of 3D T1 GRE and 3D T2 FLAIR were compared with McNemar test. Results: The SNR and CR of small brain metastases were significantly higher than those of large brain metastases (p<0.001). In qualitative analysis, the diagnostic sensitivities for small brain metas- tases were significantly higher for 3D T1 GRE plus 3D T2 FLAIR than 3D T1 GRE alone regardless of scan time (p><0.001). Conclusion: Small brain metastases showed higher signal intensity than large brain metastases on the DD Gd enhanced 3D T2 FLAIR images. DD Gd enhanced 3D T2 FLAIR imaging may have a complementary role to 3D T1 GRE for evaluating small brain metastases. [ABSTRACT FROM AUTHOR]

Purpose: This study was conducted to assess the clinical relevance of diffusion tensor tractography (DTT) in pre- and post-operative evaluations of childhood epilepsy surgery. Materials and Methods: Seventy-two patients who received epilepsy surgery between March 2004 and July 2008 were retrospectively analyzed (M : F=40 : 32, ages of 3 months to 24 years, mean age=8.9 years). DTT was performed using a 3.0 T scanner and single-shot spin-echo echo-planar imaging with 32-different diffusion gradient directions. We reviewed the data focusing on the type of surgery, final pathological diagnosis, and how the DTT data were clinically used. Results: The most common form of childhood epilepsy surgery was complete resection of an epileptogenic lesion (n=52, 72.2%). The reported etiologies included cortical dysplasia (n=32, 44.4%), hippocampal sclerosis (n=9, 12.5%), brain tumors (n=7, 9.7%), and non-pathologic lesions (n=4, 5.6%) in the final diagnoses. Twenty-one dysplastic cortexes and four brain tumors involved an approximal relationship with the corticospinal tract (n=18), optic radiation (n=2), and arcuate fasciculus (n=5). Additionally, although DTT demonstrated white matter tracts clearly, DTT in the hippocampal sclerosis did not provide any additional information. In cases of callosotomy (n=18, 25%), post-operative DTT was utilized for the evaluation of complete resection in all patients. DTT information was not used in functional hemispherectomy (n=2, 2.8%). Conclusion: Preoperatively, DTT was a useful technique in cases of cortical dysplasia and brain tumors, and in cases with callosotomy, postoperatively. DTT should be included among the routine procedures performed in management of epilepsy. [ABSTRACT FROM AUTHOR]

Purpose: Mitochondrial encephalopathy (ME) is a rare disorder of energy metabolism. The disease course can roughly be evaluated by clinical findings. The purpose of this study was to evaluate metabolic spectral changes using proton MR spectroscopy (MRS), and to establish a way to monitor ME by neuroimaging. Materials and Methods: Proton MRS data were retrospectively reviewed in 12 patients with muscle biopsy-confirmed ME (M : F = 7 : 5, Mean age = 4.8 years). All received 1H-MRS initially and also after a ketogenic diet and mitochondrial disease treatment cocktail (follow up average was 10.2 months). Changes of Nacetylaspartate/ creatine (NAA/Cr) ratio, choline/creatine (Cho/Cr) ratio, and lactate peak in basal ganglia at 1.2 ppm were evaluated before and after treatment. Findings on conventional T2 weighted MR images were also evaluated. Results: On conventional MRI, increased basal ganglia T2 signal intensity was the most common finding with ME (n = 9, 75%), followed by diffuse cerebral atrophy (n = 8, 67%), T2 hyperintense lesions at pons and midbrain (n = 4, 33%), and brain atrophy (n = 2, 17%). Lactate peak was found in 4 patients; 2 had disappearance of the peak on follow up MRS. Quantitative analysis showed relative decrease of Cho/Cr ratio on follow up MRS (p = 0.0058, paired t-test, two-tailed). There was no significant change in NAA/Cr ratio. Conclusion: MRS is a useful tool for monitoring disease progression or impro-vement in ME, and decrease or disappearance of lactate peak and reduction of Cho/Cr fraction were correlated well with improvement of clinical symptoms. [ABSTRACT FROM AUTHOR]

Purpose: Diffusion tensor imaging provides better understanding of pathophysiology of congenital anomalies, involving central nervous system. This study was aimed to specify the pathogenetic mechanism of heterotopia, proved by diffusion tensor imaging, and establish new findings of heterotopia on fractional anisotropy maps. Materials and Methods: Diffusion-weighted imaging data from 11 patients (M : F = 7 : 4, aged from 1 to 22 years, mean = 12.3 years) who visited the epilepsy clinic and received a routine seizure protocol MRI exam were retrospectively analyzed. Fractional anisotropy (FA) maps were generated from diffusion tensor imaging of 11 patients with heterotopia. Regions of interests (ROI) were placed in cerebral cortex, heterotopic gray matter and deep gray matter, including putamen. ANOVA analysis was performed for comparison of different gray matter tissues. Results: Heterotopic gray matter showed signal intensities similar to normal gray matter on T1 and T2 weighted MRI. The measured FA of heterotopic gray matter was higher than that of cortical gray matter (0.236 ± 0.011 vs. 0.169 ± 0.015, p < 0.01, one way ANOVA), and slightly lower than that of deep gray matter (0.236 ± 0.011 vs. 0.259 ± 0.016, p < 0.01). Conclusion: Increased FA of heterotopic gray matter suggests arrested neuron during radial migration and provides better understanding of neurodevelopment. [ABSTRACT FROM AUTHOR]