Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits.

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

Association between cortical gyrification and white matter integrity in spinocerebellar ataxia type 3.

Gray matter volume and thickness reductions have been reported in patients with spinocerebellar ataxia type 3 (SCA3), whereas cortical gyrification alterations of this disease remain largely unexplored. Using local gyrification index (LGI) and fractional anisotropy (FA) from structural and diffusion MRI data, this study investigated the cortical gyrification alterations as well as their relationship with white matter microstructural abnormalities in patients with SCA3 (n = 61) compared with healthy controls (n = 69). We found widespread reductions in cortical LGI and white matter FA in patients with SCA3 and that changes in these 2 features were also coupled. In the patient group, the LGI of the left middle frontal gyrus, bilateral insula, and superior temporal gyrus was negatively correlated with the severity of depressive symptoms, and the FA of a cluster in the left cerebellum was negatively correlated with the Scale for the Assessment and Rating of Ataxia scores. Our findings suggest that the gyrification abnormalities observed in this study may account for the clinical heterogeneity in SCA3 and are likely to be mediated by the underlying white matter microstructural abnormalities of this disease.

Clinic information, pathological, and imaging characteristics in 2 058 surgical patients with lung cancer from a single center. / 单中心2 058ä¾‹è‚ºç™Œæ‰‹æœ¯æ‚£è€ çš„ä¸´åºŠä¿¡æ¯ã€ç— ç†åŠå½±åƒå­¦ç‰¹å¾.

OBJECTIVES: Lung cancer is characterized by its high incidence and case fatality rate. Factors related to population composition and cancer prevention programme policy have an effect on the incidence and diagnosis of lung cancer. This study aims to provide scientific support for early diagnosis and treatment of lung cancer by investigating the clinic information, pathological, and imaging characteristics of surgical patients with lung cancer. METHODS: The data of 2 058 patients, who underwent surgery for lung cancer in the Department of Thoracic Surgery of Xiangya Hospital of Central South University from 2016 to 2019, were retrospectively collected to analyze changes in clinic information, pathological, and imaging characteristics. RESULTS: From 2016 to 2019, the number of patients per year was 280, 376, 524, and 878, respectively. Adenocarcinoma (68.1%) was the most common pathological type of surgical patients with lung cancer. From 2016 to 2019, the proportion of adenocarcinoma was increased from 55.5% to 74.1%. The proportion lung cancer patients in stage IA was increased from 38.9% to 62.3%, and the proportion of patients who underwent sublobar resection was increased from 1.8% to 8.6%. The proportion of lymph node sampling was increased in 2019. Compared with the rate in 2016, the detection rate of nodules with diameter≤1 cm detected by CT before surgery in 2019 was significantly improved (2.0% vs 18.2%), and the detection rate of nodules with diameter>3 cm was decreased (34.7% vs 18.3%). From 2016 to 2019, the proportion of lesions with pure ground-glass density and partial solid density detected by CT was increased from 2.0% and 16.6% to 20.0% and 37.3%, respectively. The proportion of solid density was decreased from 81.4% to 42.7%. CONCLUSIONS: The number of lung cancer surgery patients is rapidly increasing year by year, the proportion of CT-detected purely ground-glass density and partially solid density lesions are increasing, the proportion of patients with adenocarcinoma is rising, the proportion of early-stage lung cancer is increasing, smaller lung cancers are detected in earlier clinical stage leading to a more minimally invasive approach to the surgical methods.

Clinical features and imaging manifestations for intravascular large B-cell lymphoma. / è¡€ç®¡å† å¤§B细胞淋巴瘤的临床特点及影像学表现.

OBJECTIVES: Intravascular large B-cell lymphomas are a rare group of extranodal non-Hodgkin's lymphomas with no apparent specificity of symptoms and often a poor prognosis. This study aims to investigate the clinical features and imaging manifestations of intravascular large B-cell lymphoma, to improve the awareness of clinicians and imaging physicians and to reduce the underdiagnosis and misdiagnosis of this disease. METHODS: Five patients with pathologically confirmed intravascular large B-cell lymphoma diagnosed between December 2011 and February 2022 at Xiangya Hospital of Central South University were retrospectively analyzed for clinical features and imaging manifestations. RESULTS: The age range for the 5 patients in this paper was 44-72 years at an average of (55.4±11.2) years, 2 were male and 3 were female. There were 4 patients with fever, 3 patients with cough, 3 patients with fatigue, and 2 patients with neurological symptoms. The primary sites of lesions were bilateral adrenal glands (2 cases), liver (1 case), central nervous system (1 case), lung (1 case). Imaging of the corresponding site was performed in 5 cases. When the primary site is the adrenal gland, it appears as bilateral adrenal masses or bilateral adrenal thickening with clear contours. When the primary site is the liver, it appears as a hypodense lesion in the right lobe of the liver with "vascular floating sign". When the primary site is the central nervous system, it appears as multiple tumor-like lesions in the frontal-parietal-occipital lobe, right temporal lobe and bilateral cerebellar hemispheres with multiple hemorrhages within the lesions. When the primary site is the lung, it appears as multiple ground glass shadows and solid changes in both lungs with localized lobular septal thickening. The final pathological diagnosis was intravascular large B-cell lymphoma in all 5 patients, one of whom had a good prognosis after chemotherapy, 2 of whom died within 3 days of discharge, and two of whom were lost to follow-up. CONCLUSIONS: Intravascular large B-cell lymphoma can involve multiple tissues and organs, and its prognosis is often poor, and its imaging manifestations possess certain specificity. When this disease is highly suspected, a biopsy of the corresponding organ should be performed as soon as possible to clarify the diagnosis.

The Chinese Parkinson's Disease Registry (CPDR): Study Design and Baseline Patient Characteristics.

BACKGROUND: There is a lack of large multicenter Parkinson's disease (PD) cohort studies and limited data on the natural history of PD in China. OBJECTIVES: The objective of this study was to launch the Chinese Parkinson's Disease Registry (CPDR) and to report its protocol, cross-sectional baseline data, and prospects for a comprehensive observational, longitudinal, multicenter study. METHODS: The CPDR recruited PD patients from 19 clinical sites across China between January 2018 and December 2020. Clinical data were collected prospectively using at least 17 core assessment scales. Patients were followed up for clinical outcomes through face-to-face interviews biennially. RESULTS: We launched the CPDR in China based on the Parkinson's Disease & Movement Disorders Multicenter Database and Collaborative Network (PD-MDCNC). A total of 3148 PD patients were enrolled comprising 1623 men (51.6%) and 1525 women (48.4%). The proportions of early-onset PD (EOPD, age at onset ≤50 years) and late-onset PD (LOPD) were 897 (28.5%) and 2251 (71.5%), respectively. Stratification by age at onset showed that EOPD manifested milder motor and nonmotor phenotypes and was related to increased probability of dyskinesia. Comparison across genders suggested a slightly older average age at PD onset, milder motor symptoms, and a higher rate of developing levodopa-induced dyskinesias in women. CONCLUSIONS: The CPDR is one of the largest multicenter, observational, longitudinal, and natural history studies of PD in China. It offers an opportunity to expand the understanding of clinical features, genetic, imaging, and biological markers of PD progression. © 2022 International Parkinson and Movement Disorder Society.

Identification of the Largest SCA36 Pedigree in Asia: with Multimodel Neuroimaging Evaluation for the First Time.

Spinocerebellar ataxias (SCAs) are a large group of hereditary neurodegenerative diseases characterized by ataxia and dysarthria. Due to high clinical and genetic heterogeneity, many SCA families are undiagnosed. Herein, using linkage analysis, WES, and RP-PCR, we identified the largest SCA36 pedigree in Asia. This pedigree showed some distinct clinical characteristics. Cognitive impairment and gaze palsy are common and severe in SCA36 patients, especially long-course patients. Although no patients complained of hearing loss, most of them presented with hearing impairment in objective auxiliary examination. Voxel-based morphometry (VBM) demonstrated a reduction of volumes in cerebellum, brainstem, and thalamus (corrected P < 0.05). Reduced volumes in cerebellum were also found in presymptomatic carriers. Resting-state functional MRI (R-fMRI) found reduced ReHo values in left cerebellar posterior lobule (corrected P < 0.05). Diffusion tensor imaging (DTI) demonstrated a reduction of FA values in cerebellum, midbrain, superior and inferior cerebellar peduncle (corrected P < 0.05). MRS found reduced NAA/Cr values in cerebellar vermis and hemisphere (corrected P < 0.05). Our findings could provide new insights into management of SCA36 patients. Detailed auxiliary examination are recommended to assess hearing or peripheral nerve impairment, and we should pay more attention to eye movement and cognitive changes in patients. Furthermore, for the first time, our multimodel neuroimaging evaluation generate a full perspective of brain function and structure in SCA36 patients.

Abnormal functional connectivity profiles predict drug responsiveness in patients with temporal lobe epilepsy.

OBJECTIVE: This work was undertaken to study the functional connectivity differences between non-seizure-free and seizure-free patients with temporal lobe epilepsy (TLE) and to identify imaging predictors for drug responsiveness in TLE. METHODS: In this prospective study, 52 patients with TLE who presented undetermined antiseizure medication responsiveness and 55 demographically matched healthy controls were sequentially recruited from Xiangya Hospital. Functional magnetic resonance imaging data were acquired during a Chinese version of the verbal fluency task. The patients were followed up until the outcome could be classified. The subject groups were compared in terms of activation profile, task-residual functional connectivity (trFC), and generalized psychophysiological interaction (gPPI) analyses. Moreover, we extracted imaging characteristics for logistic regression and receiver operating characteristic evaluation. RESULTS: With a mean follow-up of 1.1 years, we identified 27 non-seizure-free patients and 19 seizure-free patients in the final analyses. The Chinese character verbal fluency task successfully activated the language network and cognitive control network (CCN) and deactivated the default mode network (DMN). In the non-seizure-freedom group, the trFC between the hippocampus and bilateral brain networks was attenuated (p < .05, familywise error corrected). For the gPPI analysis, group differences were mainly located in the precuneus, middle frontal gyrus, and inferior parietal lobule (p < .001, uncorrected; k ≥ 10). The regression model presented high accuracy when predicting non-seizure-free patients (area under the curve = .879, 95% confidence interval = .761-.998). SIGNIFICANCE: In patients with TLE who would not achieve seizure freedom with current antiseizure medications, the functional connectivity between the hippocampus and central nodes of the DMN, CCN, and language network was disrupted, leading to language decline. Independent of hippocampal sclerosis, abnormalities, especially the effective connectivity from the hippocampus to the DMN, were predictive biomarkers of drug responsiveness in patients with TLE.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Increased expression of NOP14 is associated with improved prognosis due to immune regulation in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is the third most common of cancer-related deaths. Nucleolar protein 14 (NOP14) is known to play different roles in diverse types of cancers. However, little is known about its roles in CRC. Here, we assessed the prognostic value and functions of NOP14 in CRC using the data from The Cancer Genome Atlas (TCGA) and validated them based on the data from Gene Expression Omnibus (GEO). METHODS: NOP14 mRNA and protein data in CRC was obtained from the TCGA, GEO, human protein atlas (HPA), and UALCAN databases. Survival and Cox regression analysis was performed to assess the prognostic value of NOP14 in CRC patients. Next, to evaluate the potential functions of NOP14, a protein-protein interaction (PPI) network was constructed and gene set enrichment analysis (GSEA) of differential expression genes (DEGs) associated with dysregulated NOP14 was performed. Finally, to investigate the immune response associated with NOP14 expression in CRC, we analyzed the correlations between immune cells infiltration and NOP14 expression level. Additionally, the correlations between immune molecule expression levels with NOP14 expression level were analyzed. RESULTS: High NOP14 mRNA expression was observed in CRC tissues based on the data from TCGA and GEO datasets. Similarly, high NOP14 protein levels were found in CRC tissues according to the immunohistochemical images from HPA. Interestingly, high NOP14 expression level was associated with an improved prognosis in CRC patients. Univariate and multivariate Cox regression analysis indicated that high NOP14 expression level was an independent protective factor for CRC patients. With the support of PPI network analysis, we found several risk genes interacted with NOP14. GSEA revealed that high NOP14 expression inhibited several signal pathways involved in tumor formation and development. Additionally, high NOP14 expression was positively associated with most kinds of immune cell infiltrations and the expression levels of some molecules related to immune activation. CONCLUSION: Altogether, these results indicated that high NOP14 expression leads to improved prognosis in CRC patients by inhibiting the signaling pathways involved in tumor growth and promoting the immune responses.

Regional Brain and Spinal Cord Volume Loss in Spinocerebellar Ataxia Type 3.

BACKGROUND: Given that new therapeutic options for spinocerebellar ataxias are on the horizon, there is a need for markers that reflect disease-related alterations, in particular, in the preataxic stage, in which clinical scales are lacking sensitivity. OBJECTIVE: The objective of this study was to quantify regional brain volumes and upper cervical spinal cord areas in spinocerebellar ataxia type 3 in vivo across the entire time course of the disease. METHODS: We applied a brain segmentation approach that included a lobular subsegmentation of the cerebellum to magnetic resonance images of 210 ataxic and 48 preataxic spinocerebellar ataxia type 3 mutation carriers and 63 healthy controls. In addition, cervical cord cross-sectional areas were determined at 2 levels. RESULTS: The metrics of cervical spinal cord segments C3 and C2, medulla oblongata, pons, and pallidum, and the cerebellar anterior lobe were reduced in preataxic mutation carriers compared with controls. Those of cervical spinal cord segments C2 and C3, medulla oblongata, pons, midbrain, cerebellar lobules crus II and X, cerebellar white matter, and pallidum were reduced in ataxic compared with nonataxic carriers. Of all metrics studied, pontine volume showed the steepest decline across the disease course. It covaried with ataxia severity, CAG repeat length, and age. The multivariate model derived from this analysis explained 46.33% of the variance of pontine volume. CONCLUSION: Regional brain and spinal cord tissue loss in spinocerebellar ataxia type 3 starts before ataxia onset. Pontine volume appears to be the most promising imaging biomarker candidate for interventional trials that aim at slowing the progression of spinocerebellar ataxia type 3. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research.

The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18-30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.

DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications.

In the era of data-driven medicine, rapid access and accurate interpretation of medical images are becoming increasingly important. The DICOM Image ANalysis and Archive (DIANA) system is an open-source, lightweight, and scalable Python interface that enables users to interact with hospital Picture Archiving and Communications Systems (PACS) to access such data. In this work, DIANA functionality was detailed and evaluated in the context of retrospective PACS data retrieval and two prospective clinical artificial intelligence (AI) pipelines: bone age (BA) estimation and intra-cranial hemorrhage (ICH) detection. DIANA orchestrates activity beginning with post-acquisition study discovery and ending with online notifications of findings. For AI applications, system latency (exam completion to system report time) was quantified and compared to that of clinicians (exam completion to initial report creation time). Mean DIANA latency was 9.04 ± 3.83 and 20.17 ± 10.16 min compared to clinician latency of 51.52 ± 58.9 and 65.62 ± 110.39 min for BA and ICH, respectively, with DIANA latencies being significantly lower (p < 0.001). DIANA's capabilities were also explored and found effective in retrieving and anonymizing protected health information for "big-data" medical imaging research and analysis. Mean per-image retrieval times were 1.12 ± 0.50 and 0.08 ± 0.01 s across x-ray and computed tomography studies, respectively. The data herein demonstrate that DIANA can flexibly integrate into existing hospital infrastructure and improve the process by which researchers/clinicians access imaging repository data. This results in a simplified workflow for large data retrieval and clinical integration of AI models.

Characteristics of high-resolution magnetic resonance vessel wall imaging of cervicocerebral artery dissection and the influential factors of vascular recanalization. / å¤´é¢ˆéƒ¨åŠ¨è„‰å¤¹å±‚é«˜åˆ†è¾¨çŽ‡ç£å ±æŒ¯è¡€ç®¡å£æˆåƒçš„ç‰¹å¾åŠç— å˜è¡€ç®¡å†é€šçš„å½±å“å› ç´ .

OBJECTIVES: Cervicocerebral artery dissection (CAD) is one of the important causes for ischemic stroke in young and middle-aged people. CAD is dangerous and untimely diagnosis and treatment are likely to result in severe disability. Early diagnosis and timely intervention can greatly improve the prognosis of patients. This study was to investigate the imaging features of CAD on high-resolution magnetic resonance vessel wall imaging (HRMR-VWI) and to analyze the influential factors of vascular recanalization. METHODS: A total of 19 CAD patients with both baseline HRMR-VWI and follow-up data of vascular imaging in the period from April 2017 to December 2019 in Department of Radiology, Xiangya Hospital, Central South University were retrospectively analyzed. The diseased vessels were divided into a recovery group and a unrecovered group. After treatment, diseased vessels with no residual arterial dissection and no residual stenosis in the lumen were included in the recovery group. Diseased vessels with stenosis, occlusion or residual dissection were included in the unrecovered group. Diseased vessels were divided into a ischemic stroke group and a non-ischemic stroke group according to the presence or absence of ischemic stroke in the area supplied by the diseased vessels. Differences in clinical data and HRMR-VWI imaging findings were compared between the groups. RESULTS: A total of 26 vessels were involved, including 14 (53.8%) internal carotid artery extracranial segment, 8 (30.8%) vertebral artery extracranial segment, 3 (11.5%) vertebral artery intracranial segment, and 1 (3.9%) basilar artery. Ischemic stroke occurred in 16 diseased vascular supply areas. Intramural hematoma was all observed in the baseline HMR-VWI of the affected vessels. There were 18 vessels (69.2%) in the recovery group and 8 vessels (30.8%) in the unrecovered group. Compared with the vessels in the recovery group, the vessels in the unrecovered group were mostly found in the intracranial segment (P<0.05). However, there were no significant differences in gender, age, imaging findings of dissection, length of lesion involvement, time between reexamination, vascular occlusion, and antiplatelet therapy between the recovery group and the unrecovered group (all P>0.05).There were nosignificant differences in age, gender, complication of hypertension, hyperlipidemia, diabetes mellitus, lesion location, vascular occlusion, lesion involvement length, double lumen sign, internal membrane, and lumen thrombosis between the ischemic stroke group and the non-ischemic stroke group (all P>0.05). CONCLUSIONS: Intramural hematoma is a common imaging manifestation of CAD and can be shown clearly and accurately on HRMR-VWI. Recanalization rate of CAD is high, and the recanalization of CAD in intracranial segment is slower than that of CAD in extracranial segment, which can prolong the review time.

A logistic regression model for prediction of glioma grading based on radiomics. / 基于影像组学的logistic回归模型预测胶质瘤分级.

OBJECTIVES: Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading. METHODS: Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T1-weighted imaging (T1WI+C) lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) was used to select the most-predictive radiomics features for pathological grading and to calculate radiomics score (Rad-score) of each patient. A logistic regression model was built to explore the correlation between giloma grading and Rad-score. Receiver operating characteristic (ROC) curve was performed to evaluate the model's predictive ability with area under the curve (AUC) for the evaluation index. Hosmer-Lemeshow test was used to measure the model's predictive accuracy. RESULTS: A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve (P=0.808), indicating high predictive accuracy of the model. CONCLUSIONS: The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.

A novel nonsense mutation in the L1CAM gene responsible for X-linked congenital hydrocephalus.

BACKGROUND: Congenital hydrocephalus is a descriptive diagnosis of symptoms, that are present for numerous reasons, including chromosomal disorders, genetic mutations, intrauterine infection and hemorrhage, amongst other factors. Mutation of L1CAM gene is the most frequent cause of congenital hydrocephalus, contributing to approximately 30% of X-linked congenital hydrocephalus. METHODS: In the present study, we used whole-exome sequencing and Sanger sequencing to investigate an aborted male fetus present with severe congenital hydrocephalus at 24 weeks of gestation, whose mother had a history of two previous voluntary terminations of pregnancies as a result of hydrocephalus. Magnetic resonance imaging, an autopsy and electron microscopy were performed and the phenotypic changes were described. RESULTS: Whole-exome sequencing in the fetus, as well as variant segregation analysis, revealed a novel maternally derived hemizygous nonsense mutation (c.2865G>A; p. Y955*) in exon 21 of the L1CAM gene (NM_000425.4). Severe hydrocephalus was observed along with marked dilatation of lateral ventricles. An electron micrograph of the surface of lateral ventricle walls revealed a lack of ependymal cilia. CONCLUSION: The present study suggests that L1CAM mutation screening should be considered for a male fetus with isolated hydrocephalus, especially with a family history, which could facilitate prenatal diagnosis in a subsequent pregnancy.

Sensorimotor and pain-related alterations of the gray matter and white matter in Type 2 diabetic patients with peripheral neuropathy.

Although diabetic peripheral neuropathy (DPN) has long been considered a disease of the peripheral nervous system, recent neuroimaging studies have shown that alterations in the central nervous system may play a crucial role in its pathogenesis. Here, we used surface-based morphometry (SBM) and tract-based spatial statistics (TBSS) to investigate gray matter (GM) and white matter (WM) differences between patients with DPN (n = 67, 44 painless and 23 painful) and healthy controls (HCs; n = 88). Compared with HCs, patients with DPN exhibited GM abnormalities in the pre- and postcentral gyrus and in several deep GM nuclei (caudate, putamen, medial pallidum, thalamus, and ventral nuclear). They also exhibited altered WM tracts (corticospinal tract, spinothalamic tract, and thalamocortical projecting fibers). These findings suggest impaired motor and somatosensory pathways in DPN. Further, patients with DPN (particularly painful DPN) exhibited morphological differences in the cingulate, insula, prefrontal cortex, and thalamus, as well as impaired WM integrity in periaqueductal WM and internal and external capsules. This suggests pain-perception/modulation pathways are altered in painful DPN. Intermodal correlation analyses found that the morphological indices of the brain regions identified by the SBM analysis were significantly correlated with the fractional anisotropy of brain regions identified by the TBSS analysis, suggesting that the GM and WM alterations were tightly coupled. Overall, our study showed sensorimotor and pain-related GM and WM alterations in patients with DPN, which might be involved in the development of DPN.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Clinical and imaging findings of discharged patients with SARS-CoV-2 positive anal swab samples: a descriptive study.

BACKGROUND: To explore the clinical features and CT findings of clinically cured coronavirus disease 2019 (COVID-19) patients with viral RNA positive anal swab results after discharge. METHODS: Forty-two patients with COVID-19 who were admitted to Yongzhou Central Hospital, Hunan, China, between January 20, 2020, and March 2, 2020, were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using anal swab viral RT-PCR. In this report, we present the clinical characteristics and chest CT features of six patients with positive anal swab results and compare the clinical, laboratory, and CT findings between the positive and negative groups. RESULTS: The anal swab positivity rate for SARS-CoV-2 RNA in discharged patients was 14.3% (6/42). All six patients were male. In the positive group, 40% of the patients (2/5) had a positive stool occult blood test (OBT), but none had diarrhea. The median duration of fever and major symptoms (except fever) in the positive patients was shorter than that of the negative patients (1 day vs. 6 days, 4.5 days vs. 10.5 days, respectively). The incidence of asymptomatic cases in the positive group (33.3%) was also higher than that of the negative group (5.6%). There were no significant differences in the CT manifestation or evolution of the pulmonary lesions between the two groups. CONCLUSION: In our case series, patients with viral RNA positive anal swabs did not exhibit gastrointestinal symptoms, and their main symptoms disappeared early. They had similar CT features to the negative patients, which may be easier to be ignored. A positive OBT may indicate gastrointestinal damage caused by SARS-CoV-2 infection.

Prognostic value of magnetic resonance imaging performed during the subacute phase in adult patients with hypoxic-ischemic encephalopathy for long-term neurological outcomes.

OBJECTIVE: To investigate the value of a model based on brain magnetic resonance imaging (MRI) performed in the subacute phase (between the 1st and 30th day) in predicting long-term neurological outcomes of adult hypoxic-ischemic encephalopathy (HIE) patients. METHODS: Ninety-six adult HIE patients who underwent conventional MRI and diffusion-weighted imaging (DWI) during the subacute phase were retrospectively analyzed. Favorable (Cerebral Performance Categories (CPC) 1-2) and unfavorable outcome (CPC 3-5) groups were created based on patient neurological status approximately three months after the onset of hypoxic-ischemic events. A multivariate stepwise regression model was applied after univariate analysis of MRI findings, and then the overall MRI score, Alberta Stroke Program Early Computed Tomography Score (ASPECTS), Bilateral ASPECTS (Bi-ASPECTS), modified ASPECTS (mASPECTS) and Bi-ASPECTS combined with posterior circulation ASPECTS (PC-ASPECTS) were calculated based on MRI findings. Receiver operating characteristic (ROC) curves were used to assess prognostic accuracy. RESULTS: Both univariate and multivariate analyses showed the cerebral cortex and cerebellum, neostriatum, hippocampus, brainstem and postanoxic leukoencephalopathy were independent prognostic factors for unfavorable outcomes. The multivariate regression analysis resulted in an overall classification accuracy of 84.4%, a sensitivity of 84.2% (95% CI, 71.6-92.1%), and a specificity of 92.3% (95% CI, 78.0-98.0%) for unfavorable outcomes. The model had an areas under the ROC curves (AUC) of 0.944 (95% CI, 0.901-0.987); the MRI overall scores were 0.918 (95% CI, 0.866, 0.971), ASPECTS 0.839 (95% CI, 0.755, 0.923), Bi-ASPECTS 0.837 (95% CI, 0.753, 0.922), mASPECTS 0.851(95% CI, 0.771, 0.931) and Bi-ASPECTS+PC-ASPECTS 0.876 (95% CI, 0.806, 0.946). CONCLUSIONS: The multivariate model based on conventional MRI combined with DWI performed in the subacute phase could help predict the prognosis of adult HIE with high performance.