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Abstract Although gliomatosis cerebri (GC) has been removed as an independent tumor type from the WHO classification, its extensive infiltrative pattern may harbor a unique biological behavior. However, the clinical implication of GC in the context of the 2021 WHO classification is yet to be unveiled. This study investigated the incidence, clinicopathologic and imaging correlations, and prognostic implications of GC in adult-type diffuse glioma patients. Retrospective chart and imaging review of 1,211 adult-type diffuse glioma patients from a single institution between 2005 and 2021 was performed. Among 1,211 adult-type diffuse glioma patients, there were 99 (8.2%) patients with GC. The proportion of molecular types significantly differed between patients with and without GC (P = 0.017); IDH-wildtype glioblastoma was more common (77.8% vs. 66.5%), while IDH-mutant astrocytoma (16.2% vs. 16.9%) and oligodendroglioma (6.1% vs. 16.5%) were less common in patients with GC than in those without GC. The presence of contrast enhancement, necrosis, cystic change, hemorrhage, and GC type 2 were independent risk factors for predicting IDH mutation status in GC patients. GC remained as an independent prognostic factor (HR = 1.25, P = 0.031) in IDH-wildtype glioblastoma patients on multivariable analysis, along with clinical, molecular, and surgical factors. Overall, our data suggests that although no longer included as a distinct pathological entity in the WHO classification, recognition of GC may be crucial considering its clinical significance. There is a relatively high incidence of GC in adult-type diffuse gliomas, with different proportion according to molecular types between patients with and without GC. Imaging may preoperatively predict the molecular type in GC patients and may assist clinical decision-making. The prognostic role of GC promotes its recognition in clinical settings.

Abstract Objectives To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting. Materials and methods In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having newly developed BM from two tertiary university hospitals, which performed black-blood imaging between January 2020 and April 2021. Four neuroradiologists independently evaluated BM either with segmented masks and BM counts provided (with DLS) or not provided (without DLS) on a clinical trial imaging management system (CTIMS). To assess reading reproducibility, BM count agreement between the readers and the reference standard were calculated using limits of agreement (LoA). Readers’ workload was assessed with reading time, which was automatically measured on CTIMS, and were compared between with and without DLS using linear mixed models considering the imaging center. Results In the validation cohort, the detection sensitivity and positive predictive value of the DLS were 90.2% (95% confidence interval [CI]: 88.1–92.2) and 88.2% (95% CI: 85.7–90.4), respectively. The difference between the readers and the reference counts was larger without DLS (LoA: −0.281, 95% CI: −2.888, 2.325) than with DLS (LoA: −0.163, 95% CI: −2.692, 2.367). The reading time was reduced from mean 66.9 s (interquartile range: 43.2–90.6) to 57.3 s (interquartile range: 33.6–81.0) (P

Abstract Cognitive impairment in Parkinson’s disease (PD) severely affects patients’ prognosis, and early detection of patients at high risk of dementia conversion is important for establishing treatment strategies. We aimed to investigate whether multiparametric MRI radiomics from basal ganglia can improve the prediction of dementia development in PD when integrated with clinical profiles. In this retrospective study, 262 patients with newly diagnosed PD (June 2008–July 2017, follow-up >5 years) were included. MRI radiomic features (n = 1284) were extracted from bilateral caudate and putamen. Two models were developed to predict dementia development: (1) a clinical model—age, disease duration, and cognitive composite scores, and (2) a combined clinical and radiomics model. The area under the receiver operating characteristic curve (AUC) were calculated for each model. The models’ interpretabilities were studied. Among total 262 PD patients (mean age, 68 years ± 8 [standard deviation]; 134 men), 51 (30.4%), and 24 (25.5%) patients developed dementia within 5 years of PD diagnosis in the training (n = 168) and test sets (n = 94), respectively. The combined model achieved superior predictive performance compared to the clinical model in training (AUCs 0.928 vs. 0.894, P = 0.284) and test set (AUCs 0.889 vs. 0.722, P = 0.016). The cognitive composite scores of the frontal/executive function domain contributed most to predicting dementia. Radiomics derived from the caudate were also highly associated with cognitive decline. Multiparametric MRI radiomics may have an incremental prognostic value when integrated with clinical profiles to predict future cognitive decline in PD.

Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.

Abstract Objectives This study investigated whether radiomic features can improve the prediction accuracy for tumor recurrence over clinicopathological features and if these features can be used to identify high-risk patients requiring adjuvant radiotherapy (ART) in WHO grade 2 meningiomas. Methods Preoperative magnetic resonance imaging (MRI) of 155 grade 2 meningioma patients with a median follow-up of 63.8 months were included and allocated to training (n = 92) and test sets (n = 63). After radiomic feature extraction (n = 200), least absolute shrinkage and selection operator feature selection with logistic regression classifier was performed to develop two models: (1) a clinicopathological model and (2) a combined clinicopathological and radiomic model. The probability of recurrence using the combined model was analyzed to identify candidates for ART. Results The combined clinicopathological and radiomics model exhibited superior performance for the prediction of recurrence compared with the clinicopathological model in the training set (area under the curve [AUC] 0.78 vs. 0.67, P = 0.042), which was also validated in the test set (AUC 0.77 vs. 0.61, P = 0.192). In patients with a high probability of recurrence by the combined model, the 5-year progression-free survival was significantly improved with ART (92% vs. 57%, P = 0.024), and the median time to recurrence was longer (54 vs. 17 months after surgery). Conclusions Radiomics significantly contributes added value in predicting recurrence when integrated with the clinicopathological features in patients with grade 2 meningiomas. Furthermore, the combined model can be applied to identify high-risk patients who require ART.

Abstract Background While numerous neuroimaging studies have demonstrated that glaucoma is associated with smaller volumes of the visual cortices in the brain, only a few studies have linked glaucoma with brain structures beyond the visual cortices. Therefore, the objective of this study was to compare brain imaging markers and neuropsychological performance between individuals with and without glaucoma. Methods We identified 64 individuals with glaucoma and randomly selected 128 age-, sex-, and education level-matched individuals without glaucoma from a community-based cohort. The study participants underwent 3 T brain magnetic resonance imaging and neuropsychological assessment battery. Regional cortical thickness and subcortical volume were estimated from the brain images of the participants. We used a linear mixed model after adjusting for potential confounding variables. Results Cortical thickness in the occipital lobe was significantly smaller in individuals with glaucoma than in the matched individuals (β = − 0.04 mm, P = 0.014). This did not remain significant after adjusting for cardiovascular risk factors (β = − 0.02 mm, P = 0.67). Individuals with glaucoma had smaller volumes of the thalamus (β = − 212.8 mm3, P = 0.028), caudate (β = − 170.0 mm3, P = 0.029), putamen (β = − 151.4 mm3, P = 0.051), pallidum (β = − 103.6 mm3, P = 0.007), hippocampus (β = − 141.4 mm3, P = 0.026), and amygdala (β = − 87.9 mm3, P = 0.018) compared with those without glaucoma. Among neuropsychological battery tests, only the Stroop color reading test score was significantly lower in individuals with glaucoma compared with those without glaucoma (β = − 0.44, P = 0.038). Conclusions We found that glaucoma was associated with smaller volumes of the thalamus, caudate, putamen, pallidum, amygdala, and hippocampus.

Abstract The interplay between schizophrenia liability and environmental influences has been considered to be responsible for the development of schizophrenia. Recent neuroimaging studies have linked aberrant functional connectivity (FC) between the default-mode network (DMN) and the frontoparietal network (FPN) in the resting-state to the underlying neural mechanism of schizophrenia. By using schizotypy as the proxy for genetic-based liability to schizophrenia and methylation of brain-derived neurotrophic factor (BDNF) to represent environmental exposure, this study investigated the impact of the interaction between vulnerability and the environment on the neurobiological substrates of schizophrenia. Participants in this study included 101 healthy adults (HC) and 46 individuals with ultra-high risk for psychosis (UHR). All participants were tested at resting-state by functional magnetic resonance imaging, and group-independent component analysis was used to identify the DMN and the FPN. The Perceptual Aberration Scale (PAS) was used to evaluate the schizotypy level. The methylation status of BDNF was measured by pyrosequencing. For moderation analysis, the final sample consisted of 83 HC and 32 UHR individuals. UHR individuals showed reduced DMN-FPN network FC compared to healthy controls. PAS scores significantly moderated the relationship between the percentage of BDNF methylation and DMN-FPN network FC. The strength of the positive relationship between BDNF methylation and the network FC was reduced when the schizotypy level increased. These findings support the moderating role of schizotypy on the neurobiological mechanism of schizophrenia in conjunction with epigenetic changes.

Abstract The heterogeneity of MRI is one of the major reasons for decreased performance of a radiomics model on external validation, limiting the model’s generalizability and clinical application. We aimed to establish a generalizable radiomics model to predict meningioma grade on external validation through leveraging Cycle-Consistent Adversarial Networks (CycleGAN). In this retrospective study, 257 patients with meningioma were included in the institutional training set. Radiomic features (n = 214) were extracted from T2-weighted (T2) and contrast-enhanced T1 (T1C) images. After radiomics feature selection, extreme gradient boosting classifiers were developed. The models were validated in the external validation set consisting of 61 patients with meningiomas. To reduce the gap in generalization associated with the inter-institutional heterogeneity of MRI, the smaller image set style of the external validation was translated into the larger image set style of the institutional training set using CycleGAN. On external validation before CycleGAN application, the performance of the combined T2 and T1C models showed an area under the curve (AUC), accuracy, and F1 score of 0.77 (95% confidence interval 0.63–0.91), 70.7%, and 0.54, respectively. After applying CycleGAN, the performance of the combined T2 and T1C models increased, with an AUC, accuracy, and F1 score of 0.83 (95% confidence interval 0.70–0.97), 73.2%, and 0.59, respectively. Quantitative metrics (by Fréchet Inception Distance) showed that CycleGAN can decrease inter-institutional image heterogeneity while preserving predictive information. In conclusion, leveraging CycleGAN may be helpful to increase the generalizability of a radiomics model in differentiating meningioma grade on external validation.

Little is known about the effect of air pollution on Alzheimer’s disease (AD)-specific brain structural pathologies. There is also a lack of evidence on whether this effect leads to poorer cognitive function. We investigated whether, and the extent to which, AD-like cortical atrophy mediated the association between air pollution exposures and cognitive function in dementia-free adults. We used cross-sectional data from 640 participants who underwent brain magnetic resonance imaging and the Montreal Cognitive Assessment (MoCA). Mean cortical thickness (as the measure of global cortical atrophy) and machine learning-based AD-like cortical atrophy score were estimated from brain images. Concentrations of particulate matter with diameters ≤ 10 μm (PM10) and ≤ 2.5 μm (PM2.5) and nitrogen dioxide (NO2) were estimated based on each participant’s residential address. Following the product method, a mediation effect was tested by conducting a series of three regression analyses (exposure to outcome; exposure to mediator; and exposure and mediator to outcome). A 10 μg/m3 increase in PM10 (β = -1.13; 95 % CI, −1.73 to −0.53) and a 10 ppb increase in NO2 (β = -1.09; 95 % CI, −1.40 to −0.78) were significantly associated with a lower MoCA score. PM10 (β = 0.27; 95 % CI, 0.06 to 0.48) and NO2 (β = 0.35; 95 % CI, 0.25 to 0.45) were significantly associated with an increased AD-like cortical atrophy score. Effects of PM10 and NO2 on MoCA scores were significantly mediated by mean cortical thickness (proportions mediated: 25 %–28 %) and AD-like cortical atrophy scores (13 %–16 %). The findings suggest that air pollution exposures may induce AD-like cortical atrophy, and that this effect may lead to poorer cognitive function in dementia-free adults.

Abstract There is a growing need to develop novel strategies for the diagnosis of schizophrenia using neuroimaging biomarkers. We investigated the robustness of the diagnostic model for schizophrenia using radiomic features from T1-weighted and diffusion tensor images of the corpus callosum (CC). A total of 165 participants [86 schizophrenia and 79 healthy controls (HCs)] were allocated to training (N = 115) and test (N = 50) sets. Radiomic features of the CC subregions were extracted from T1-weighted, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) images (N = 1605). Following feature selection, various combinations of classifiers were trained, and Bayesian optimization was adopted in the best performing classifier. Discrimination, calibration, and clinical utility of the model were assessed. An online calculator was constructed to offer the probability of having schizophrenia. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model. We identified 30 radiomic features to differentiate participants with schizophrenia from HCs. The Bayesian optimized model achieved the highest performance, with an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.81–0.98), 80.0, 83.3, and 76.9%, respectively, in the test set. The final model offers clinical probability in an online calculator. The model explanation by SHAP suggested that second-order features from the posterior CC were highly associated with the risk of schizophrenia. The multiparametric radiomics model focusing on the CC shows its robustness for the diagnosis of schizophrenia. Radiomic features could be a potential source of biomarkers that support the biomarker-based diagnosis of schizophrenia and improve the understanding of its neurobiology.

Abstract The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN. .

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.

Purpose The 2021 World Health Organization classification includes telomerase reverse transcriptase promoter (TERTp) mutation status as a factor for differentiating meningioma grades. Therefore, preoperative prediction of TERTp mutation may assist in clinical decision making. However, no previous study has applied fractal analysis for TERTp mutation status prediction in meningiomas. The purpose of this study was to assess the utility of three-dimensional (3D) fractal analysis for predicting the TERTp mutation status in grade 2 meningiomas. Methods Forty-eight patients with surgically confirmed grade 2 meningiomas (41 TERTp-wildtype and 7 TERTp-mutant) were included. 3D fractal dimension (FD) and lacunarity values were extracted from the fractal analysis. A predictive model combining clinical, conventional, and fractal parameters was built using logistic regression analysis. Receiver operating characteristic curve analysis was used to assess the ability of the model to predict TERTp mutation status. Results Patients with TERTp-mutant grade 2 meningiomas were older (P = 0.029) and had higher 3D FD (P = 0.026) and lacunarity (P = 0.004) values than patients with TERTp-wildtype grade 2 meningiomas. On multivariable logistic analysis, higher 3D FD values (odds ratio = 32.50, P = 0.039) and higher 3D lacunarity values (odds ratio = 20.54, P = 0.014) were significant predictors of TERTp mutation status. The area under the curve, accuracy, sensitivity, and specificity of the multivariable model were 0.84 (95% confidence interval 0.71–0.93), 83.3%, 71.4%, and 85.4%, respectively. Conclusion 3D FD and lacunarity may be useful imaging biomarkers for predicting TERTp mutation status in grade 2 meningiomas.

Purpose: The aim of this study was to evaluate whether perivascular space (PVS) severity and retinal ganglion cell layer (GCL) thickness differed based on the stage of diabetic retinopathy (DR) and the cognitive status in patients with DR.Methods: A total of 81 patients with DR (51 in the non-proliferative group and 30 in the proliferative group) were included in this retrospective, cross-sectional study. PVS severity was assessed in the basal ganglia (BG) and centrum semiovale using MRI. The total cerebral small vessel disease (SVD) score was determined based on the numbers of lacunes and microbleeds and the severity of white matter hyperintensity. Optical coherence tomography was used to measure foveal and perifoveal GCL thicknesses. Cerebral SVD markers and cognitive function were compared between the groups, and correlations between the BG-PVS severity and the Mini-Mental Status Examination (MMSE) scores and GCL parameters were evaluated.Results: Patients with proliferative DR had higher BG-PVS severity (P = 0.012), higher total cerebral SVD scores (P = 0.035), reduced GCL thicknesses in the inferior (P = 0.027), superior (P = 0.046), and temporal (P = 0.038) subfields compared to patients with non-proliferative DR. In addition, the BG-PVS severity was negatively correlated with the MMSE score (P = 0.007), and the GCL thickness was negatively correlated with the BG-PVS severity (P-values < 0.05 for inferior, superior, and temporal subfields).Conclusion: BG-PVS severity and retinal GCL thickness may represent novel imaging biomarkers reflecting the stage of DR and cognitive decline in diabetic patients. Furthermore, these results suggest a possible link between cerebral and retinal neurodegeneration at the clinical level.

The general population is exposed to numerous environmental pollutants, and it remains unclear which pollutants affect the brain, accelerating brain aging and increasing the risk of dementia. The Environmental-Pollution-Induced Neurological Effects study is a multi-city prospective cohort study aiming to comprehensively investigate the effect of different environmental pollutants on brain structures, neuropsychological function, and the development of dementia in adults. The baseline data of 3,775 healthy elderly people were collected from August 2014 to March 2018. The eligibility criteria were age ≥50 years and no self-reported history of dementia, movement disorders, or stroke. The assessment included demographics and anthropometrics, laboratory test results, and individual levels of exposure to air pollution. A neuroimaging sub-cohort was also recruited with 1,022 participants during the same period, and brain magnetic resonance imaging and neuropsychological tests were conducted. The first follow-up environmental pollutant measurements will start in 2022 and the follow-up for the sub-cohort will be conducted every 3-4 years. We have found that subtle structural changes in the brain may be induced by exposure to airborne pollutants such as particulate matter 10 μm or less in diameter (PM10), particulate matter 2.5 μm or less in diameter (PM2.5) and Mn10, manganese in PM10; Mn2.5, manganese in PM2.5. PM10, PM2.5, and nitrogen dioxide in healthy adults. This study provides a basis for research involving large-scale, long-term neuroimaging assessments in community-based populations.

When a stimulus is associated with an external reward, its chance of being consolidated into long-term memory is boosted via dopaminergic facilitation of long-term potentiation in the hippocampus. Given that higher temporal distance (TD) has been found to discount the subjective value of a reward, we hypothesized that memory performance associated with a more immediate reward will result in better memory performance. We tested this hypothesis by measuring both behavioral memory performance and brain activation using functional magnetic resonance imaging (fMRI) during memory encoding and retrieval tasks. Contrary to our hypothesis, both behavioral and fMRI results suggest that the TD of rewards might enhance the chance of the associated stimulus being remembered. The fMRI data demonstrate that the lateral prefrontal cortex, which shows encoding-related activation proportional to the TD, is reactivated when searching for regions that show activation proportional to the TD during retrieval. This is not surprising given that this region is not only activated to discriminate between future vs. immediate rewards, it is also a part of the retrieval-success network. These results provide support for the conclusion that the encoding-retrieval overlap provoked as the rewards are more delayed may lead to better memory performance of the items associated with the rewards.

This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks of different difficulty levels with magnetic resonance imaging (MRI) radiomics features: (1) "Simple" task, glioblastomas [n = 109] vs. brain metastasis [n = 58] and (2) "difficult" task, low- [n = 163] vs. high-grade [n = 95] meningiomas. Additionally, two undersampled datasets were created by randomly sampling 50% from these datasets. We performed random training-test set splitting for each dataset repeatedly to create 1,000 different training-test set pairs. For each dataset pair, the least absolute shrinkage and selection operator model was trained and evaluated using various validation methods in the training set, and tested in the test set, using the area under the curve (AUC) as an evaluation metric. The AUCs in training and testing varied among different training-test set pairs, especially with the undersampled datasets and the difficult task. The mean (±standard deviation) AUC difference between training and testing was 0.039 (±0.032) for the simple task without undersampling and 0.092 (±0.071) for the difficult task with undersampling. In a training-test set pair with the difficult task without undersampling, for example, the AUC was high in training but much lower in testing (0.882 and 0.667, respectively); in another dataset pair with the same task, however, the AUC was low in training but much higher in testing (0.709 and 0.911, respectively). When the AUC discrepancy between training and test, or generalization gap, was large, none of the validation methods helped sufficiently reduce the generalization gap. Our results suggest that machine learning after a single random training-test set split may lead to unreliable results in radiomics studies especially with small sample sizes.

Abstract Introduction For the diagnosis of Parkinson's disease (PD) and atypical parkinsonism (AP) using neuroimaging, structural measures have been largely employed since structural abnormalities are most noticeable in the diseases. Functional abnormalities have been known as well, though less clearly seen, and thus, the addition of functional measures to structural measures is expected to be more informative for the diagnosis. Here, we aimed to assess whether multimodal neuroimaging measures of structural and functional alterations could have potential for enhancing performance in diverse diagnostic classification problems. Methods For 77 patients with PD, 86 patients with AP comprising multiple system atrophy and progressive supranuclear palsy, and 53 healthy controls (HC), structural and functional MRI data were collected. Gray matter (GM) volume was acquired as a structural measure, and GM regional homogeneity and degree centrality were acquired as functional measures. The measures were used as predictors individually or in combination in support vector machine classifiers for different problems of distinguishing between HC and each diagnostic type and between different diagnostic types. Results In statistical comparisons of the measures, structural alterations were extensively seen in all diagnostic types, whereas functional alterations were limited to specific diagnostic types. The addition of functional measures to the structural measure generally yielded statistically significant improvements to classification accuracy, compared to the use of the structural measure alone. Conclusion We suggest the fusion of multimodal neuroimaging measures as an effective strategy that could generally cope with diverse prediction problems of clinical concerns.

Introduction: High resolution vessel wall MRI (VW-MRI) has enabled to characterize intracranial atherosclerosis (ICAS). We studied to identify the factors for enhancement of ICAS in VW-MRI in patients with acute ischemic stroke.Methods: Consecutive patients with acute ischemic stroke or TIA who underwent VW-MRI between January 2017 and December 2017 were included. Enhancement on VW-MRI was defined as an increase in intensity on contrast-enhanced T1-weighted sequence. We compared the clinical and the radiologic findings between patients with wall enhancement and those without wall enhancement.Results: Of the 48 patients with ICAS, 28 patients revealed enhancement on VW-MRI. Patients with enhancement were more likely to have severe stenotic lesions and higher levels of total cholesterol, triglycerides, low-density cholesterol, Apo (b), and Apo (b)/Apo (a) lipoprotein ratio (p < 0.05). Multivariable analysis demonstrated that total cholesterol (OR: 5.378, 95% CI, 1.779–16.263), triglycerides (OR: 3.362, 95% CI, 1.008–11.209), low density lipoprotein cholesterol (OR: 4.226, 95% CI, 1.264–14.126), Apo (b) lipoprotein (OR: 3639.641, 95% CI, 17.854–741954.943) levels, and Apo (b)/Apo (a) lipoprotein ratio (OR, 65.514; 95% CI, 1.131–3680.239) were independently associated with enhancement of ICAS. High-density lipoprotein cholesterol and Apo (a) lipoprotein levels were not significantly different between the patients with wall enhancement and those without wall enhancement.Conclusions: The presence and severity of enhancement of ICAS was significantly associated with dyslipidemic conditions. These results suggest that strict lipid modification should be achieved for the management of ICAS.

Functional neuroimaging data was collected while postpartum women and age-matched control women performed the Remember/Know judgment task in the functional magnetic resonance imaging scanner. This data provides information about functional connectivity patterns across the subjective recollection networks that were informative in differentiating the postpartum women from control women. Classification performances based on machine learning algorithms and descriptions of functional connectivity patterns that derived the peak classification accuracy are reported in this article. All other results from our study have been reported in Nah et al. (2018) [1]. Keywords: Episodic memory, Functional connectivity, Postpartum women, Subjective recollection effect

Abstract Degeneration of the substantia innominata (SI) is significantly correlated with cognitive performance in Parkinson’s disease (PD). We examined functional and structural patterns of SI degeneration in drug-naïve PD patients according to the duration of parkinsonism before mild cognitive impairment (MCI) diagnosis. Twenty PD patients with a shorter duration (PD-MCI-SD,

Purpose To describe imaging features of infratentorial desmoplastic infantile or non-infantile tumors (DIT/DNIT). Materials and Methods Four cases with infratentorial DIT/DNIT from our hospital and 5 cases from literature review were analyzed. Clinical data and MR imaging features were evaluated including location, size, shape, margin, composition, dural attachment, perilesional edema, and metastasis or multiplicity. Results The mean age was 9.2 years (range, 1–18 years). Most of the patients presented with headache or vomiting (4/9, 44.4%) and had no underlying disease (8/9, 88.9%). The major pathologic subtype was astrocytoma (6/9, 66.7%). On MR, majority of the tumors involved cerebellum and/or spinal cord (8/9, 88.9%) and the mean size of the tumors was 4.2 cm (range, 3.2–5 cm). The tumors were mainly solid (4/9, 44.4%) or mixed (4/9, 44.4%) in composition with lobulated shape (7/9, 77.8%) and well-defined margin (7/9, 77.8%). Two cases (2/7, 28.6%) showed dural attachment and all the cases had no or minimal perilesional edema (100%). Metastasis or multiplicity was frequently seen in 44.4% (4/9). Conclusion Infratentorial DIT/DNIT occurred in relatively older children and the major tumor type was astrocytoma. They also had atypical imaging features showing mainly solid or mixed in composition with frequent metastasis or multiplicity.

Background: The observer perspective causes patients with social anxiety disorder (SAD) to excessively inspect their performance and appearance. This study aimed to investigate the neural basis of distorted self-face recognition in non-social situations in patients with SAD. Methods: Twenty patients with SAD and 20 age- and gender-matched healthy controls participated in this fMRI study. Data were acquired while participants performed a Composite Face Evaluation Task, during which they had to press a button indicating how much they liked a series of self-faces, attractively transformed self-faces, and attractive others' faces. Results: Patients had a tendency to show more favorable responses to the self-face and unfavorable responses to the others' faces compared with controls, but the two groups' responses to the attractively transformed self-faces did not differ. Significant group differences in regional activity were observed in the middle frontal and supramarginal gyri in the self-face condition (patients controls); and the middle frontal, supramarginal, and angular gyri in the attractive others' face condition (patients > controls). Most fronto-parietal activities during observation of the self-face were negatively correlated with preference scores in patients but not in controls. Conclusion: Patients with SAD have a positive point of view of their own face and experience self-relevance for the attractively transformed self-faces. This distorted cognition may be based on dysfunctions in the frontal and inferior parietal regions. The abnormal engagement of the fronto-parietal attentional network during processing face stimuli in non-social situations may be linked to distorted self-recognition in SAD.

PurposeTo characterize resting-state brain activation patterns and investigate altered areas for cognitive decline in HIV patients.MethodsTwelve male HIV patients with intact cognition (HIV-IC), 10 with HIV-associated neurocognitive disorder (HAND), and 11 male healthy controls (HC) underwent resting-state functional MRI (rsfMRI). Three rsfMRI values, regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF) were calculated and compared between groups. Correlation analyses were performed between rsfMRI values and neuropsychological tests.ResultsrsfMRI analyses revealed decreased rsfMRI values in the frontal areas, and increases in the posterior brain regions for both HIV-IC and HAND compared to HC. When directly compared to HIV-IC, HAND showed lower fALFF in the orbitofrontal cortex and higher ReHo in the primary sensorimotor area. Additionally, decreased orbitofrontal fALFF, increased sensorimotor ReHo, and a larger difference between the two values were highly correlated with decreased verbal memory and executive function in HIV patients.ConclusionsRegardless of cognitive status, altered local intrinsic activities were found in HIV patients. The orbitofrontal cortex and primary sensorimotor area were more disrupted in HAND relative to HIV-IC and correlated with behavioral performance, suggesting these areas are relevant to cognitive impairment in HIV patients.

An early and reliable diagnosis of intracranial arterial dissection is important to reduce the risk of neurological complication. The purpose of this study was to assess the clinical usefulness of three-dimensional high-resolution MRI (3D-HR-MRI) including pre- and post-contrast T1-weighted volumetric isotropic turbo spin echo acquisition with improved motion-sensitized driven equilibrium preparation (3D-iMSDE-T1) and proton-density weighted image (3D-PD) in detecting dissection and to evaluate the added value of 3D-PD in diagnosing intracranial arterial dissection.We retrospectively recruited patients who underwent 3D-HR-MRI with clinical suspicion of arterial dissection. Among them, we selected patients who were diagnosed with definite dissection according to the Spontaneous Cervicocephalic Arterial Dissections Study criteria. For each patient, the presence of intimal flap, intramural hematoma, and vessel dilatation were evaluated independently by two neuroradiologists on each sequence. Interobserver agreement was assessed.Seventeen patients (mean age: 41 ± 10 [SD] years; 13 men) were diagnosed with definite dissection. The intimal flaps were more frequently detected on 3D-PD (88.2%, 15/17) than on 3D-iMSDE-T1 (29.4%, 5/17), and post-contrast 3D-iMSDE-T1 (35.3%, 6/17; P = 0.006 and P = 0.004, respectively). No significant difference was found in the detection rate of intramural hematomas (59-71%) and vascular dilatations (47%) on each sequence. Interobserver agreement for detection of dissection findings showed almost perfect agreement (k = 0.84-1.00), except for detection of intimal flaps on pre-contrast 3D-iMSDE-T1 (k = 0.62). After addition of 3D-PD to pre- and post-contrast 3D-iMSDE-T1, more patients were diagnosed with definite dissection with the initial MRI (88.2% vs. 47.1%; P = 0.039).The intimal flap might be better visualized on the 3D-PD sequence than the 3D-iMSDE-T1 sequences, allowing diagnosis of definite dissection without follow-up imaging.

It has been reported that reality evaluation and recognition are impaired in patients with schizophrenia and these impairments are related to the severity of psychotic symptoms. The current study aimed to investigate the neural basis of impairments in reality evaluation and recognition and their relationships with cognitive insight in schizophrenia. During functional magnetic resonance imaging, 20 patients with schizophrenia and 20 healthy controls performed a set of reality evaluation and recognition tasks, in which subjects judged whether scenes in a series of drawings were real or unreal and whether they were familiar or novel. During reality evaluation, patients showed decreased activity in various regions including the inferior parietal lobule, retrosplenial cortex and parahippocampal gyrus, compared with controls. Particularly, parahippocampal gyrus activity was correlated with the severity of positive symptoms in patients. During recognition, patients also exhibited decreased activity in various regions, including the dorsolateral prefrontal cortex, inferior parietal lobule and posterior cingulate cortex. Particularly, inferior parietal lobule activity and posterior cingulate cortex activity were correlated with cognitive insight in patients. These findings provide evidence that neural impairments in reality evaluation and recognition are related to psychotic symptoms. Anomalous appraisal of context by dysfunctions in the context network may contribute to impairments in the reality processing in schizophrenia, and abnormal declarative memory processes may be involved in cognitive insight in patients with schizophrenia.

The presence of good collaterals on CT angiography (CTA) is a well-known predictor for favorable outcome in acute ischemic stroke. Recently, multiphase CT has been introduced as a more accurate method in assessing collaterals. The aim of this study was to assess the ability of dual-phase CT to evaluate collateral status and predict clinical outcome.Forty-three patients who underwent both dual-phase CT and transfemoral cerebral angiography (TFCA) for occluded intracranial internal carotid artery (ICA) and/or middle cerebral artery (M1 segment) were recruited from a prospectively collected database. The collateral status on dual-phase CT was graded by using a 4-point scale: grade 0 = no collaterals; 1 = some collaterals with persistence of some defects; 2 = slow but complete collaterals; and 3 = fast and complete collaterals. Univariate and multivariate analysis were performed to define the independent predictors for favorable outcome at 3 months.Dual-phase CT collateral status (ρ = 0.744) showed higher correlation with TFCA collateral status than CTA collateral status (ρ = 0.596) and substantial interobserver agreement (weighted κ = 0.776). In the univariate analysis, age, history of hypertension, collateral scores on CTA, dual-phase CT, and TFCA, occlusion in intracranial ICA, final infarct volume, and symptomatic hemorrhage were significantly associated with outcome. Among them, only the dual-phase CT collateral score was an independent predictor for favorable outcome (OR = 26.342 (2.788-248.864); P = 0.004) in the multivariate analysis.The collateral status on dual-phase CT can be a useful predictor for clinical outcome in acute stroke patients, especially when advanced CT techniques are not available in emergent situations.

Apparent diffusion coefficients (ADC) can help differentiate between central nervous system (CNS) lymphoma and Glioblastoma (GBM). However, overlap between ADCs for GBM and lymphoma have been reported because of various region of interest (ROI) methods. Our aim is to explore ROI method to provide the most reproducible results for differentiation.We studied 25 CNS lymphomas and 62 GBMs with three ROI methods: (1) ROI1, whole tumor volume; (2) ROI2, multiple ROIs; and (3) ROI3, a single ROI. Interobserver variability of two readers for each method was analyzed by intraclass correlation(ICC). ADCs were compared between GBM and lymphoma, using two-sample t-test. The discriminative ability was determined by ROC analysis.ADCs from ROI1 showed most reproducible results (ICC >0.9). For ROI1, ADCmean for lymphoma showed significantly lower values than GBM (p = 0.03). The optimal cut-off value was 0.98×10(-3) mm2/s with 85% sensitivity and 90% specificity. For ROI2, ADCmin for lymphoma was significantly lower than GBM (p = 0.02). The cut-off value was 0.69×10(-3) mm2/s with 87% sensitivity and 88% specificity.ADC values were significantly dependent on ROI method. ADCs from the whole tumor volume had the most reproducible results. ADCmean from the whole tumor volume may aid in differentiating between lymphoma and GBM. However, multi-modal imaging approaches are recommended than ADC alone for differentiation.

Although Gadolinium enhanced bFFE is commonly used to evaluate cisternal tumors, banding artifact may interrupt interpretation and adjacent nerve and vessels differentiation is known to be difficult. We analyzed the qualities of Gd enhanced 3D PDDE in the evaluation of cisternal tumors, comparing with bFFE.Forty five cisternal tumors (33 schwannoma and 12 meningioma) on both bFFE and PDDE were retrospectively reviewed. For quantitative analysis, contrast ratios of CSF to tumor and tumor to parenchyma (CRC/T and CRT/P) on both sequences were compared by paired t-test. For qualitative analysis, the readers gauged the qualities of the two MR sequences with respect to the degree of demarcating cisternal structures (tumor, basilar artery, AICA, trigeminal nerve, facial nerve and vestibulocochlear nerve).In quantitative analysis, CRC/T and CRT/P on 3D PDDE was significantly lower than that of 3D bFFE (p < 0.01). In qualitative analysis, basilar artery, AICA, facial nerve and vestibulocochlear nerves were significantly better demarcated on 3D PDDE than on bFFE (p < 0.01). The degree of demarcation of tumor on 3D PDDE was not significantly different with that on 3D bFFE (p = 0.13).Although the contrast between tumor and the surrounding structures are reduced, Gd enhanced 3D PDDE provides better demarcation of cranial nerves and major vessels adjacent to cisternal tumors than Gd enhanced bFFE.

OBJECTIVES: Glucose metabolism, perfusion, and water diffusion may have a relationship or affect each other in the same tumor. The understanding of their relationship could expand the knowledge of tumor characteristics and contribute to the field of oncologic imaging. The purpose of this study was to evaluate the relationships between metabolism, vasculature and cellularity of advanced hepatocellular carcinoma (HCC), using multimodality imaging such as ¹⁸F-FDG positron emission tomography (PET), dynamic contrast enhanced (DCE)-MRI, and diffusion weighted imaging(DWI). MATERIALS AND METHODS: Twenty-one patients with advanced HCC underwent ¹⁸F-FDG PET, DCE-MRI, and DWI before treatment. Maximum standard uptake values (SUV(max)) from ¹⁸F-FDG-PET, variables of the volume transfer constant (K(trans)) from DCE-MRI and apparent diffusion coefficient (ADC) from DWI were obtained for the tumor and their relationships were examined by Spearman's correlation analysis. The influence of portal vein thrombosis on SUV(max) and variables of K(trans) and ADC was evaluated by Mann-Whitney test. RESULTS: SUV(max) showed significant negative correlation with K(trans)(max) (ρ = -0.622, p = 0.002). However, variables of ADC showed no relationship with variables of K(trans) or SUV(max) (p>0.05). Whether portal vein thrombosis was present or not did not influence the SUV max and variables of ADC and K(trans) (p>0.05). CONCLUSION: In this study, SUV was shown to be correlated with K(trans) in advanced HCCs; the higher the glucose metabolism a tumor had, the lower the perfusion it had, which might help in guiding target therapy.

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]