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Background@#Numerous studies have shown the effect of particulate matter exposure on brain imaging markers. However, little evidence exists about whether the effect differs by the level of low-grade chronic systemic inflammation. We investigated whether the level of c-reactive protein (CRP, a marker of systemic inflammation) modifies the associations of particulate matter exposures with brain cortical gray matter thickness and white matter hyperintensities (WMH). @*Methods@#We conducted a cross-sectional study of baseline data from a prospective cohort study including adults with no dementia or stroke. Long-term concentrations of particulate matter ≤ 10 µm in diameter (PM10) and ≤ 2.5 µm (PM2.5) at each participant’s home address were estimated. Global cortical thickness (n = 874) and WMH volumes (n = 397) were estimated from brain magnetic resonance images. We built linear and logistic regression models for cortical thickness and WMH volumes (higher versus lower than median), respectively. Significance of difference in the association between the CRP group (higher versus lower than median) was expressed as P for interaction. @*Results@#Particulate matter exposures were significantly associated with a reduced global cortical thickness only in the higher CRP group among men (P for interaction = 0.015 for PM10 and 0.006 for PM2.5). A 10 μg/m3 increase in PM10 was associated with the higher volumes of total WMH (odds ratio, 1.78; 95% confidence interval, 1.07–2.97) and periventricular WMH (2.00; 1.20–3.33). A 1 μg/m3 increase in PM2.5 was associated with the higher volume of periventricular WMH (odds ratio, 1.66; 95% confidence interval, 1.08–2.56). These associations did not significantly differ by the level of high sensitivity CRP. @*Conclusion@#Particulate matter exposures were associated with a reduced global cortical thickness in men with a high level of chronic inflammation. Men with a high level of chronic inflammation may be susceptible to cortical atrophy attributable to particulate matter exposures.
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Objective@#To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18 F-fluorodeoxyglucose ( 18 F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. @*Materials and Methods@#This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18 F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. @*Results@#In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46–1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. @*Conclusion@#Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18 F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.
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Objective@#Cyclin-dependent kinase inhibitor (CDKN)2A/B homozygous deletion is a key molecular marker of isocitrate dehydrogenase (IDH)-mutant astrocytomas in the 2021 World Health Organization. We aimed to investigate whether qualitative and quantitative MRI parameters can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytomas. @*Materials and Methods@#Preoperative MRI data of 88 patients (mean age ± standard deviation, 42.0 ± 11.9 years; 40 females and 48 males) with IDH-mutant astrocytomas (76 without and 12 with CDKN2A/B homozygous deletion) from two institutions were included. A qualitative imaging assessment was performed. Mean apparent diffusion coefficient (ADC), 5th percentile of ADC, mean normalized cerebral blood volume (nCBV), and 95th percentile of nCBV were assessed via automatic tumor segmentation.Logistic regression was performed to determine the factors associated with CDKN2A/B homozygous deletion in all 88 patients and a subgroup of 47 patients with histological grades 3 and 4. The discrimination performance of the logistic regression models was evaluated using the area under the receiver operating characteristic curve (AUC). @*Results@#In multivariable analysis of all patients, infiltrative pattern (odds ratio [OR] = 4.25, p = 0.034), maximal diameter (OR = 1.07, p = 0.013), and 95th percentile of nCBV (OR = 1.34, p = 0.049) were independent predictors of CDKN2A/B homozygous deletion. The AUC, accuracy, sensitivity, and specificity of the corresponding model were 0.83 (95% confidence interval [CI], 0.72–0.91), 90.4%, 83.3%, and 75.0%, respectively. On multivariable analysis of the subgroup with histological grades 3 and 4, infiltrative pattern (OR = 10.39, p = 0.012) and 95th percentile of nCBV (OR = 1.24, p = 0.047) were independent predictors of CDKN2A/B homozygous deletion, with an AUC accuracy, sensitivity, and specificity of the corresponding model of 0.76 (95% CI, 0.60–0.88), 87.8%, 80.0%, and 58.1%, respectively. @*Conclusion@#The presence of an infiltrative pattern, larger maximal diameter, and higher 95th percentile of the nCBV may be useful MRI biomarkers for CDKN2A/B homozygous deletion in IDH-mutant astrocytomas.
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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.
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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 and18F-fluorodeoxyglucose ( 18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients withOPSCC. @*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 reclassification 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.
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Objective@#Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. @*Materials and Methods@#PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. @*Results@#External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the “gold standard” (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. @*Conclusion@#The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.
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Objective@#Intracranial atherosclerotic stroke occurs through various mechanisms, mainly by artery-to-artery embolism (AA) or branch occlusive disease (BOD). This study evaluated the spatial relationship between middle cerebral artery (MCA) plaques and perforating arteries among different MCA territory infarction types using vessel wall magnetic resonance imaging (VW-MRI). @*Materials and Methods@#We retrospectively enrolled patients with acute MCA infarction who underwent VW-MRI. Thirty-four patients were divided into three groups according to infarction pattern: 1) BOD, 2) both BOD and AA (BOD-AA), and 3) AA.To determine the factors related to BOD, the BOD and BOD-AA groups were combined into one group (with striatocapsular infarction [BOD+]) and compared with the AA group. To determine the factors related to AA, the BOD-AA and AA groups were combined into another group (with cortical infarction [AA+]) and compared with the BOD group. Plaque morphology and the spatial relationship between the perforating artery orifice and plaque were evaluated both quantitatively and qualitatively. @*Results@#The plaque margin in the BOD+ group was closer to the perforating artery orifice than that in the AA group (p = 0.011), with less enhancing plaque (p = 0.030). In the BOD group, plaques were mainly located on the dorsal (41.2%) and superior (41.2%) sides where the perforating arteries mainly arose. No patient in the AA group had overlapping plaques with perforating arteries at the cross-section where the perforator arose. Perforating arteries associated with culprit plaques were most frequently located in the middle two-thirds of the M1 segment (41.4%). The AA+ group had more stenosis (%) than the BOD group (39.73 ± 24.52 vs. 14.42 ± 20.96; p,/i> = 0.003). @*Conclusion@#The spatial relationship between the perforating artery orifice and plaque varied among different types of MCA territory infarctions. In patients with BOD, the plaque margin was closer and blocked the perforating artery orifice, and stenosis degree and enhancement were less than those in patients with AA.
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Background@#Firefighters inevitably encounter emotionally and physically stressful situations at work. Even firefighters without diagnosed post-traumatic stress disorder receive clinical attention because the nature of the profession exposes them to repetitive trauma and high occupational stress. This study investigated gray matter abnormalities related to high occupational stress in firefighters using voxel-based morphometry (VBM) and surface-based morphometry (SBM). @*Methods@#We assessed 115 subjects (112 males and 3 females) using magnetic resonance imaging and evaluated occupational stress by the Korean Occupational Stress Scale-26 (KOSS-26). Subjects were classified into highly or lowly stressed groups based on the median value of the KOSS-26. @*Results@#In VBM analysis, we found that firefighters with high occupational stress had lower gray matter volume (GMV) in both sides of the insula, the left amygdala, the right medial prefrontal cortex (mPFC), and the anterior cingulate cortex than firefighters with low occupational stress. In SBM analysis based on regions of interest, the GMV of the bilateral insula and right mPFC were also lower in the highly stressed group. Within the highly stressed group, low GMV of the insula was significantly correlated with the length of service (left: r = −0.347, P = 0.009; right: r = −0.333, P = 0.012). @*Conclusion@#Our findings suggest that regional GMV abnormalities are related to occupational stress. Regional gray matter abnormalities and related emotional dysregulation may contribute to firefighter susceptibility to burnout.
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Advances in radiomics and deep learning (DL) hold great potential to be at the forefront of precision medicine for the treatment of patients with brain metastases.Radiomics and DL can aid clinical decision-making by enabling accurate diagnosis, facilitating the identification of molecular markers, providing accurate prognoses, and monitoring treatment response. In this review, we summarize the clinical background, unmet needs, and current state of research of radiomics and DL for the treatment of brain metastases. The promises, pitfalls, and future roadmap of radiomics and DL in brain metastases are addressed as well.
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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.
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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.
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40 years or new hires with less than 1 year of service.
Subject(s)
Humans , Brain , Cardiovascular Diseases , Cohort Studies , Firefighters , Follow-Up Studies , Health Surveys , Hospitals, University , Killer Cells, Natural , Korea , Magnetic Resonance Imaging , Mental Disorders , Mental Health , Neuropsychological Tests , Polycyclic Aromatic Hydrocarbons , Prospective Studies , Republic of Korea , Risk FactorsABSTRACT
Serrated polyposis syndrome (SPS) can transform to malignant lesions through the sessile serrated pathway and traditional serrated pathway. These pathways may cause rapid neoplastic progression compared to the adenoma-carcinoma sequence, which may cause interval colorectal cancer. The authors experienced a case of SPS with a synchronous colon adenocarcinoma that was treated with an endoscopic mucosal resection. In pathology reviews, other parts of the adenocarcinoma showed sessile serrated adenoma. Therefore, patients with SPS have a potential for malignant transformation, highlighting the need for strict colonoscopy surveillance starting at the time of SPS diagnosis.
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Background@#and Purpose: Hippocampal atrophy (HA) resulting from a central nervous system (CNS) infection might be a relevant lesion responsible for the clinical characteristics of medial temporal lobe epilepsy. @*Methods@#The clinical characteristics of 54 patients with CNS infection-related medial temporal lobe epilepsy (MTLE) with isolated HA (CNS infection group) and 155 patients with conventional MTLE with HA (conventional group) were compared retrospectively. CNS infection alone and bilateral involvement of the HA were analyzed as prognostic factors, in addition to the detailed clinical characteristics, such as limbic aura and the presence and proportion of each type of automatism, between the two groups, and both medical and surgical prognoses were separately considered. A logistic regression analysis was performed. @*Results@#A statistical analysis including all clinical factors, including CNS infection with bilateral HA, did not reveal significant differences between the two groups. An analysis comparing the prognosis of the two groups based on good or poor prognosis among patients who received medical treatment and good or poor outcomes among patients who received surgical treatment did not produce significant differences. @*Conclusions@#In addition to bilateral HA, CNS infection alone was not a poor prognostic factor for the CNS infection-related epilepsy with HA group compared with the conventional MTLE with HA group. Based on these negative results, HA is a plausible and relevant lesion with similar clinical characteristics to HA in patients with conventional MTLE. Therefore, CNS infection-related MTLE with isolated HA might represent another subtype of MTLE with HA with a different etiology.
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The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613–0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467–0.759) and 0.663 (95% CI, 0.531–0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.
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Purpose@#To analyze the altered brain regions and intrinsic brain activity patterns in trauma-exposed firefighters without posttraumatic stress disorder (PTSD). @*Materials and Methods@#Resting-state functional MRI (rsfMRI) was performed for all subjects. Thirty-one firefighters over 40 years of age without PTSD (31 men; mean age, 49.8 ± 4.7 years) were included. Twenty-six non-traumatized healthy controls (HCs) (26 men; mean age, 65.3 ± 7.84 years) were also included. Voxel-based morphometry was performed to investigate focal differences in the brain anatomy. Seed-based functional connectivity analysis was performed to investigate differences in spontaneous brain characteristics. @*Results@#The mean z-scores of the Seoul Verbal Learning Test for immediate and delayed recall, Controlled Oral Word Association Test (COWAT) score for animals, and COWAT phonemic fluency were significantly lower in the firefighter group than in the HCs, indicating decreased neurocognitive function. Compared to HCs, firefighters showed reduced gray matter volume in the left superior parietal gyrus and left inferior temporal gyrus. Further, in contrast to HCs, firefighters showed alterations in rsfMRI values in multiple regions, including the fusiform gyrus and cerebellum. @*Conclusion@#Structural and resting-state functional abnormalities in the brain may be useful imaging biomarkers for identifying alterations in trauma-exposed firefighters without PTSD.
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Background@#: To compare the diagnostic performance of two-dimensional (2D) and three-dimensional(3D) fractal dimension (FD) and lacunarity features from MRI for predicting the meningioma grade. @*Methods@#: This retrospective study included 123 meningioma patients [90 World Health Organization(WHO) grade I, 33 WHO grade II/III] with preoperative MRI including post-contrast T1-weightedimaging. The 2D and 3D FD and lacunarity parameters from the contrast-enhancing portion of the tumorwere calculated. Reproducibility was assessed with the intraclass correlation coefficient. Multivariablelogistic regression analysis using 2D or 3D fractal features was performed to predict the meningiomagrade. The diagnostic ability of the 2D and 3D fractal models were compared. @*Results@#: The reproducibility between observers was excellent, with intraclass correlation coefficientsof 0.97, 0.95, 0.98, and 0.96 for 2D FD, 2D lacunarity, 3D FD, and 3D lacunarity, respectively.WHO grade II/III meningiomas had a higher 2D and 3D FD (p=0.003 and p<0.001, respectively) andhigher 2D and 3D lacunarity (p=0.002 and p=0.006, respectively) than WHO grade I meningiomas.The 2D fractal model showed an area under the curve (AUC), accuracy, sensitivity, and specificity of0.690 [95% confidence interval (CI) 0.581-0.799], 72.4%, 75.8%, and 64.4%, respectively. The 3Dfractal model showed an AUC, accuracy, sensitivity, and specificity of 0.813 (95% CI 0.733-0.878),82.9%, 81.8%, and 70.0%, respectively. The 3D fractal model exhibited significantly better diagnosticperformance than the 2D fractal model (p<0.001). @*Conclusion@#: The 3D fractal analysis proved superiority in diagnostic performance to 2D fractalanalysis in grading meningioma.
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Objective@#Compressed sensing (CS) has gained wide interest since it accelerates MRI acquisition. We aimed to compare the 3D post-contrast T1-weighted volumetric isotropic turbo spin echo acquisition (VISTA) with CS (VISTA-CS) and without CS (VISTA-nonCS) in intracranial vessel wall MRIs (VW-MRI). @*Materials and Methods@#From April 2017 to July 2018, 72 patients who underwent VW-MRI, including both VISTA-CS and VISTAnonCS, were retrospectively enrolled. Wall and lumen volumes, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured from normal and lesion sites. Two neuroradiologists independently evaluated overall image quality and degree of normal and lesion wall delineation with a four-point scale (scores ≥ 3 defined as acceptable). @*Results@#Scan coverage was increased in VISTA-CS to cover both anterior and posterior circulations with a slightly shorter scan time compared to VISTA-nonCS (approximately 7 minutes vs. 8 minutes). Wall and lumen volumes were not significantly different with VISTA-CS or VISTA-nonCS (interclass correlation coefficient = 0.964–0.997). SNR was or trended towards significantly higher values in VISTA-CS than in VISTA-nonCS. At normal sites, CNR was not significantly different between two sequences (p = 0.907), whereas VISTA-CS provided lower CNR in lesion sites compared with VISTA-nonCS (p = 0.003). Subjective wall delineation was superior with VISTA-nonCS than with VISTA-CS (p = 0.019), although overall image quality did not differ (p = 0.297). The proportions of images with acceptable quality were not significantly different between VISTA-CS (83.3–97.8%) and VISTA-nonCS (75–100%). @*Conclusion@#CS may be useful for intracranial VW-MRI as it allows for larger scan coverage with slightly shorter scan time without compromising image quality.
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Objective@#To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use. @*Materials and Methods@#PubMed MEDLINE and EMBASE were searched using the terms ‘cognitive impairment’ or ‘Alzheimer’ or ‘dementia’ and ‘radiomic’ or ‘texture’ or ‘radiogenomic’ for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS.Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. @*Results@#The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer’s Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence. @*Conclusion@#The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.
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Objective@#Compressed sensing (CS) has gained wide interest since it accelerates MRI acquisition. We aimed to compare the 3D post-contrast T1-weighted volumetric isotropic turbo spin echo acquisition (VISTA) with CS (VISTA-CS) and without CS (VISTA-nonCS) in intracranial vessel wall MRIs (VW-MRI). @*Materials and Methods@#From April 2017 to July 2018, 72 patients who underwent VW-MRI, including both VISTA-CS and VISTAnonCS, were retrospectively enrolled. Wall and lumen volumes, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured from normal and lesion sites. Two neuroradiologists independently evaluated overall image quality and degree of normal and lesion wall delineation with a four-point scale (scores ≥ 3 defined as acceptable). @*Results@#Scan coverage was increased in VISTA-CS to cover both anterior and posterior circulations with a slightly shorter scan time compared to VISTA-nonCS (approximately 7 minutes vs. 8 minutes). Wall and lumen volumes were not significantly different with VISTA-CS or VISTA-nonCS (interclass correlation coefficient = 0.964–0.997). SNR was or trended towards significantly higher values in VISTA-CS than in VISTA-nonCS. At normal sites, CNR was not significantly different between two sequences (p = 0.907), whereas VISTA-CS provided lower CNR in lesion sites compared with VISTA-nonCS (p = 0.003). Subjective wall delineation was superior with VISTA-nonCS than with VISTA-CS (p = 0.019), although overall image quality did not differ (p = 0.297). The proportions of images with acceptable quality were not significantly different between VISTA-CS (83.3–97.8%) and VISTA-nonCS (75–100%). @*Conclusion@#CS may be useful for intracranial VW-MRI as it allows for larger scan coverage with slightly shorter scan time without compromising image quality.