HE-Mind: A model for automatically predicting hematoma expansion after spontaneous intracerebral hemorrhage.

PURPOSE: To develop and validate an end-to-end model for automatically predicting hematoma expansion (HE) after spontaneous intracerebral hemorrhage (sICH) using a novel deep learning framework. METHODS: This multicenter retrospective study collected cranial noncontrast computed tomography (NCCT) images of 490 patients with sICH at admission for model training (n = 236), internal testing (n = 60), and external testing (n = 194). A HE-Mind model was designed to predict HE, which consists of a densely connected U-net for segmentation process, a multi-instance learning strategy for resolving label ambiguity and a Siamese network for classification process. Two radiomics models based on support vector machine or logistic regression and two deep learning models based on residual network or Swin transformer were developed for performance comparison. Reader experiments including physician diagnosis mode and artificial intelligence mode were conducted for efficiency comparison. RESULTS: The HE-Mind model showed better performance compared to the comparative models in predicting HE, with areas under the curve of 0.849 and 0.809 in the internal and external test sets respectively. With the assistance of the HE-Mind model, the predictive accuracy and work efficiency of the emergency physician, junior radiologist, and senior radiologist were significantly improved, with accuracies of 0.768, 0.789, and 0.809 respectively, and reporting times of 7.26 s, 5.08 s, and 3.99 s respectively. CONCLUSIONS: The HE-Mind model could rapidly and automatically process the NCCT data and predict HE after sICH within three seconds, indicating its potential to assist physicians in the clinical diagnosis workflow of HE.

Swirl sign score system: a novel and practical tool for predicting hematoma expansion risk after spontaneous intracerebral haemorrhage.

OBJECTIVE: To methodically analyse the swirl sign and construct a scoring system to predict the risk of hematoma expansion (HE) after spontaneous intracerebral haemorrhage (sICH). METHODS: We analysed 231 of 683 sICH patients with swirl signs on baseline noncontrast CT (NCCT) images. The characteristics of the swirl sign were analysed, including the number, maximum diameter, shape, boundary, minimum CT value of the swirl sign, and the minimum distance from the swirl sign to the edge of the hematoma. In the development cohort, univariate and multivariate analyses were used to identify independent predictors of HE, and logistic regression analysis was used to construct the swirl sign score system. The swirl sign score system was verified in the validation cohort. RESULTS: The number and the minimum CT value of the swirl sign were independent predictors of HE. The swirl sign score system was constructed (2 points for the number of swirl signs >1 and 1 point for the minimum CT value ≤41 Hounsfield units). The area under the curve of the swirl sign score system in predicting HE was 0.773 and 0.770 in the development and validation groups, respectively. CONCLUSIONS: The swirl sign score system is an easy-to-use radiological grading scale that requires only baseline NCCT images to effectively identify subjects at high risk of HE. ADVANCES IN KNOWLEDGE: Our newly developed semiquantitative swirl sign score system greatly improves the ability of swirl sign to predict HE.

The relationship between perihematomal edema and hematoma expansion in acute spontaneous intracerebral hemorrhage: an exploratory radiomics analysis study.

Background: The relationship between early perihematomal edema (PHE) and hematoma expansion (HE) is unclear. We investigated this relationship in patients with acute spontaneous intracerebral hemorrhage (ICH), using radiomics. Methods: In this multicenter retrospective study, we analyzed 490 patients with spontaneous ICH who underwent non-contrast computed tomography within 6 h of symptom onset, with follow-up imaging at 24 h. We performed HE and PHE image segmentation, and feature extraction and selection to identify HE-associated optimal radiomics features. We calculated radiomics scores of hematoma (Radscores_HEA) and PHE (Radscores_PHE) and constructed a combined model (Radscore_HEA_PHE). Relationships of the PHE radiomics features or Radscores_PHE with clinical variables, hematoma imaging signs, Radscores_HEA, and HE were assessed by univariate, correlation, and multivariate analyses. We compared predictive performances in the training (n = 296) and validation (n = 194) cohorts. Results: Shape_VoxelVolume and Shape_MinorAxisLength of PHE were identified as optimal radiomics features associated with HE. Radscore_PHE (odds ratio = 1.039, p = 0.032) was an independent HE risk factor after adjusting for the ICH onset time, Glasgow Coma Scale score, baseline hematoma volume, hematoma shape, hematoma density, midline shift, and Radscore_HEA. The areas under the receiver operating characteristic curve of Radscore_PHE in the training and validation cohorts were 0.808 and 0.739, respectively. After incorporating Radscore_PHE, the integrated discrimination improvements of Radscore_HEA_PHE in the training and validation cohorts were 0.009 (p = 0.086) and -0.011 (p < 0.001), respectively. Conclusion: Radscore_PHE, based on Shape_VoxelVolume and Shape_MinorAxisLength of PHE, independently predicts HE, while Radscore_PHE did not add significant incremental value to Radscore_HEA.

Differences of individual gray matter networks between MCI patients who converted to AD within 3 Years and nonconverters.

Objective: Here we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC). Materials and methods: Data from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback-Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline. Results: The gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001). Conclusion: The whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline.

Identification of differentially expressed miRNAs in plasma exosomes from patients with early-onset pre-eclampsia using next generation sequencing.

Pre-eclampsia (PE), a major cause of perinatal morbidity and mortality, accounts for up to 14 % mortality of maternal and 18 % of fetal or infant mortalities. However, the pathogenesis process of PE remains unclear. The aim of this study was to identify differentially expressed microRNAs (miRNAs) in the peripheral blood exosomes of early-onset PE patients versus healthy pregnant women using high-throughput sequencing, and to find candidate miRNAs as molecular markers. Methods: Peripheral blood samples were collected from five preeclamptic patients and five healthy women. Exosomal miRNAs were sequenced using the Illumina HiSeq4000 sequencing platform. The target gene prediction, biological function enrichment, and signaling pathway prediction of the miRNAs with significant differences were carried out using the Starbase database software, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, respectively. Our results showed 65 significantly differentially expressed miRNAs in the exosomes of early-onset PE patients compared to control group, with 17 up-regulated and 48 down-regulated (P < 0.05). A total of 2231 target genes were predicted for all differentially expressed miRNAs. Biological functions enriched by these target genes were mainly associated with Ras protein signal transduction, GTPase-mediated signal transduction regulation, histone modification, and ß-transforming growth factor regulatory process. Key regulatory signaling pathways included TGF-ß signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, tumor necrosis factor signaling pathway and EGFR tyrosine kinase inhibition signaling pathways. QPCR validation in 40 independent samples for 10 miRNAs, identified three miRNAs were confirmed in the second population. MIR7151 was a most significant differentially expressed miRNAs, and predicted its downstream regulatory gene, KCNQ10T1, using Starbase software. There were significant differences in miRNA expression profiles between peripheral blood exosomes of early-onset PE patients and normal pregnant women, suggesting that these miRNAs may contribute to the pathophysiology of early-onset PE by regulating various biological functions and signaling pathways.

Studies on the embryonic development and larval infection potential of the stomach bot flies, Gasterophilus pecorum.

Endangered Przewalski's horses have faced severe infections from Gasterophilus pecorum (Diptera, Gastrophilidae) in Xinjiang's Kalamaili Nature Reserve (KNR). This study examines G. pecorum's development and infection patterns in embryonic and larval stages, crucial for understanding horse botfly disease in desert grasslands. For the incubation of G. pecorum fertilized eggs, we established the six distinct temperature gradients: 16 °C, 20 °C, 24 °C, 28 °C, 30 °C, and 32 °C. Using the least squares method, we calculated the correlation between the developmental threshold temperature of the eggs and their cumulative effective temperature. Furthermore, we meticulously recorded the survival duration of the larvae across a spectrum of temperature gradients (-20 °C, -10 °C, 4 °C, 10 °C, 20 °C, and 30 °C) and under varying conditions (dark and light). This method allows us to analyze and interpret the impact of these environmental factors on larval survival durations. 1) The formula for predicting the embryonic development period of G. pecorum was N = (182.7 ± 12.03)/[T-(3.191 ± 1.48)], where the developmental threshold temperature was 3.191 ± 1.48 °C, and the effective accumulated temperature was 182.7 ± 12.03 d°C 2) The model describing the relationship between the embryonic development rate and temperature was: y = 0.0001x2+0.0007x+0.0378, demonstrating a positive correlation between the development rate and temperature (R-sq = 0.989, p < 0.001). 3) Larvae in the dark group exhibited a longer survival time, with the longest being 9 months at 4 °C. The adaptation of G. pecorum's embryonic development to cold temperature, combined with the extended survival period of larvae in the egg state, significantly increases the infection potential of G. pecorum in colder climates. This discovery offers essential insights into the predominance of G. pecorum in the KNR region and provides a crucial biological basis for the prevention of myiasis and the conservation of vulnerable species, such as Przewalski's horses.

A novel clinical-radscore nomogram for predicting ruptured intracranial aneurysm.

Objectives: Our study aims to find the more practical and powerful method to predict intracranial aneurysm (IA) rupture through verification of predictive power of different models. Methods: Clinical and imaging data of 576 patients with IAs including 192 ruptured IAs and matched 384 unruptured IAs was retrospectively analyzed. Radiomics features derived from computed tomography angiography (CTA) images were selected by t-test and Elastic-Net regression. A radiomics score (radscore) was developed based on the optimal radiomics features. Inflammatory markers were selected by multivariate regression. And then 4 models including the radscore, inflammatory, clinical and clinical-radscore models (C-R model) were built. The receiver operating characteristic curve (ROC) was performed to evaluate the performance of each model, PHASES and ELAPSS. The nomogram visualizing the C-R model was constructed to predict the risk of IA rupture. Results: Five inflammatory features, 2 radiological characteristics and 7 radiomics features were significantly associated with IA rupture. The areas under ROCs of the radscore, inflammatory, clinical and C-R models were 0.814, 0.935, 0.970 and 0.975 in the training cohort and 0.805, 0.927, 0.952 and 0.962 in the validation cohort, respectively. Conclusion: The inflammatory model performs particularly well in predicting the risk of IA rupture, and its predictive power is further improved by combining with radiological and radiomics features and the C-R model performs the best. The C-R nomogram is a more stable and effective tool than PHASES and ELAPSS for individually predicting the risk of rupture for patients with IA.

Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics.

Different machine learning algorithms have different characteristics and applicability. This study aims to predict ruptured intracranial aneurysms by radiomics models based on different machine learning algorithms and evaluate their differences in the same data condition. A total of 576 patients with intracranial aneurysms (192 ruptured and 384 unruptured intracranial aneurysms) from two institutions are included and randomly divided into training and validation cohorts in a ratio of 7:3. Of the 107 radiomics features extracted from computed tomography angiography images, seven features stood out. Then, radiomics features and 12 common machine learning algorithms, including the decision-making tree, support vector machine, logistic regression, Gaussian Naive Bayes, k-nearest neighbor, random forest, extreme gradient boosting, bagging classifier, AdaBoost, gradient boosting, light gradient boosting machine, and CatBoost were applied to construct models for predicting ruptured intracranial aneurysms, and the predictive performance of all models was compared. In the validation cohort, the area under curve (AUC) values of models based on AdaBoost, gradient boosting, and CatBoost for predicting ruptured intracranial aneurysms were 0.889, 0.883, and 0.864, respectively, with no significant differences among them. Of note, the performance of these models was significantly superior to that of the other nine models. The AUC of the AdaBoost model in the cross-validation was within the range of 0.842 to 0.918. Radiomics models based on the machine learning algorithms can be used to predict ruptured intracranial aneurysms, and the prediction efficacy differs among machine learning algorithms. The boosting algorithms might be superior in the application of radiomics combined with the machine learning algorithm to predict aneurysm ruptures.

The translational landscape of bread wheat during grain development.

The dynamics of gene expression in crop grains has typically been investigated at the transcriptional level. However, this approach neglects translational regulation, a widespread mechanism that rapidly modulates gene expression to increase the plasticity of organisms. Here, we performed ribosome profiling and polysome profiling to obtain a comprehensive translatome data set of developing bread wheat (Triticum aestivum) grains. We further investigated the genome-wide translational dynamics during grain development, revealing that the translation of many functional genes is modulated in a stage-specific manner. The unbalanced translation between subgenomes is pervasive, which increases the expression flexibility of allohexaploid wheat. In addition, we uncovered widespread previously unannotated translation events, including upstream open reading frames (uORFs), downstream open reading frames (dORFs), and open reading frames (ORFs) in long noncoding RNAs, and characterized the temporal expression dynamics of small ORFs. We demonstrated that uORFs act as cis-regulatory elements that can repress or even enhance the translation of mRNAs. Gene translation may be combinatorially modulated by uORFs, dORFs, and microRNAs. In summary, our study presents a translatomic resource that provides a comprehensive and detailed overview of the translational regulation in developing bread wheat grains. This resource will facilitate future crop improvements for optimal yield and quality.

T2 mapping and fat quantification of lumbar paraspinal muscle in ankylosing spondylitis: a case control study.

BACKGROUND: To compare changes in the composition of paraspinal muscles of patients with ankylosing spondylitis (AS) and matched healthy controls using T2 mapping and T2 IDEAL and correlate the quantitative magnetic resonance imaging (qMRI) results with clinical assessments of AS patients. METHOD: In total, 37 AS patients and 37 healthy controls were enrolled in the case control study. T2 mapping with and without fat saturation and IDEAL imaging were used to assess the multifidus (MF) and erector spinae (ES) at the levels of L3/L4 and L4/L5 for all subjects. Mean T2non-fatsat, T2fat, T2fatsat, cross-sectional area (CSA), and fat fraction (FF) were compared between AS and healthy controls. Correlations of qMRI results with clinical assessments were analyzed in AS. RESULTS: Significantly elevated mean T2non-fatsat values and the FF of the MF and ES at both levels were observed in AS and compared to the controls (p < 0.05). The mean T2fatsat values of ES and MF were significantly higher only at the level of L3/L4 in AS compared to healthy controls (p < 0.05). A loss of muscle CSA compatible with atrophy was present in MF and ES at both levels in AS compared to the controls (p < 0.05). Weak to moderate positive correlations were found between FF and age and disease duration in AS (r = 0.318-0.415, p < 0.05). However, such positive correlation was not observed between FF and disease duration after adjusting for age (p > 0.05). CONCLUSIONS: Our findings indicate that using a combination of IDEAL and T2 mapping may provide deeper insights into the pathophysiological degeneration of paraspinal muscles in AS.

Zengye Decoction Attenuated Severe Acute Pancreatitis Complicated with Acute Kidney Injury by Modulating the Gut Microbiome and Serum Amino Acid Metabolome.

Objective: To explore the effect and underlying mechanism of Zengye decoction (ZYD), a traditional formula from China, on the severe acute pancreatitis (SAP) rat model with acute kidney injury (AKI). Methods: The SAP-AKI model was induced by 3.5% sodium taurocholate. Rats were treated with normal saline or ZYD twice and sacrificed at 36 h after modeling. Amylase, lipase, creatinine, blood urea nitrogen, kidney injury molecule 1(KIM-1), and multiple organs' pathological examinations were used to assess the protective effect of ZYD. Gut microbiome detected by 16S rRNA sequencing analysis and serum amino acid metabolome analyzed by liquid chromatography-mass spectrometry explained the underlying mechanism. The Spearman correlation analysis presented the relationship between microflora and metabolites. Results: ZYD significantly decreased KIM-1(P < 0.05) and the pathological score of the pancreas (P < 0.05), colon (P < 0.05), and kidney (P < 0.05). Meanwhile, ZYD shifted the overall gut microbial structure (ß-diversity, ANOSIM R = 0.14, P=0.025) and altered the microbial compositions. Notably, ZYD reduced the potentially pathogenic bacteria-Bacteroidetes, Clostridiales vadin BB60 group, and uncultured_Clostridiales_bacterium, but promoted the short-chain fatty acid (SCFA) producers-Erysipelotrichaceae, Bifidobacterium, Lactobacillus, and Moryella (all P < 0.05). Moreover, principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA) presented a remarkable change in amino acid metabolome after SAP-AKI induction and an apparent regulation by ZYD treatment (R2Y 0.878, P=0.01; Q2 0.531, P=0.01). Spearman's correlation analysis suggested that gut bacteria likely influenced serum metabolites levels (absolute r > 0.4 and FDR P < 0.02). Conclusions: ZYD attenuated SAP-AKI by modulating the gut microbiome and serum amino acid metabolome, which may be a promising adjuvant treatment.

Diagnosis of Subcortical Ischemic Vascular Cognitive Impairment With No Dementia Using Radiomics of Cerebral Cortex and Subcortical Nuclei in High-Resolution T1-Weighted MR Imaging.

Purpose: To investigate whether the combination of radiomics derived from brain high-resolution T1-weighted imaging and automatic machine learning could diagnose subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) accurately. Methods: A total of 116 right-handed participants involving 40 SIVCIND patients and 76 gender-, age-, and educational experience-matched normal controls (NM) were recruited. A total of 7,106 quantitative features from the bilateral thalamus, hippocampus, globus pallidus, amygdala, nucleus accumbens, putamen, caudate nucleus, and 148 areas of the cerebral cortex were automatically calculated from each subject. Six methods including least absolute shrinkage and selection operator (LASSO) were utilized to lessen the redundancy of features. Three supervised machine learning approaches of logistic regression (LR), random forest (RF), and support vector machine (SVM) employing 5-fold cross-validation were used to train and establish diagnosis models, and 10 times 10-fold cross-validation was used to evaluate the generalization performance of each model. Correlation analysis was performed between the optimal features and the neuropsychological scores of the SIVCIND patients. Results: Thirteen features from the right amygdala, right hippocampus, left caudate nucleus, left putamen, left thalamus, and bilateral nucleus accumbens were included in the optimal subset. Among all the three models, the RF produced the highest diagnostic performance with an area under the receiver operator characteristic curve (AUC) of 0.990 and an accuracy of 0.948. According to the correlation analysis, the radiomics features of the right amygdala, left caudate nucleus, left putamen, and left thalamus were found to be significantly correlated with the neuropsychological scores of the SIVCIND patients. Conclusions: The combination of radiomics derived from brain high-resolution T1-weighted imaging and machine learning could diagnose SIVCIND accurately and automatically. The optimal radiomics features are mostly located in the right amygdala, left caudate nucleus, left putamen, and left thalamus, which might be new biomarkers of SIVCIND.

Dynamics of the Brain Functional Network Associated With Subjective Cognitive Decline and Its Relationship to Apolipoprotein E €4 Alleles.

The aim of our study was to explore the dynamic functional alterations in the brain in patients with subjective cognitive decline (SCD) and their relationship to apolipoprotein E (APOE) €4 alleles. In total, 95 SCD patients and 49 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Then, the mean time series of 90 cortical or subcortical regions were extracted based on anatomical automatic labeling (AAL) atlas from the preprocessed rs-fMRI data. The static functional connectome (SFC) and dynamic functional connectome (DFC) were constructed and compared using graph theory methods and leading eigenvector dynamics analysis (LEiDA), respectively. The SCD group displayed a shorter lifetime (p = 0.003, false discovery rate corrected) and lower probability (p = 0.009, false discovery rate corrected) than the HC group in a characteristic dynamic functional network mainly involving the bilateral insular and temporal neocortex. No significant differences in the SFC were detected between the two groups. Moreover, the lower probability in the SCD group was found to be negatively correlated with the number of APOE ε4 alleles (r = -0.225, p = 0.041) in a partial correlation analysis with years of education as a covariate. Our results suggest that the DFC may be a more sensitive parameter than the SFC and can be used as a potential biomarker for the early detection of SCD.

Neuroradiologic Findings and Clinical Features of Meningiomas With Spontaneous Hemorrhagic Onset: A Single-center 10-year Experience.

OBJECTIVE: This study aimed to elucidate the clinicoradiologic features of spontaneous hemorrhagic meningiomas (HMs) and examine risk factors associated with meningioma hemorrhage. METHODS: We retrospectively reviewed 651 consecutive meningioma patients who underwent surgical resection in our hospital between January 2011 and January 2021. After exclusions, 169 patients were included for analysis. Patients were grouped according to presence of hemorrhage in the meningioma: the HM group (n = 19) and non-HM group (n = 150). Clinicoradiologic patient data were examined and compared using univariate and multivariate analysis. RESULTS: HMs accounted for 2.9% of the entire series of meningiomas. HMs were mainly located at the convexity (63.2%). Mean diameter of HMs was 4.8 cm. On computed tomography, most HMs appeared as mixed isodensity and hyperdensity (84.2%). On magnetic resonance imaging, most appeared as mixed isointensity and hyperintensity on T1-weighted imaging and mixed hypointesity and hyperintensity on T2-weighted imaging (52.6%). Seventeen tumors exhibited heterogeneous enhancement, a dural tail, and peritumoral brain edema. Thirteen showed intratumoral cystic change. The misdiagnosis rate was significantly higher in HMs than non-HMs (31.6% vs. 7.3%; P = 0.005). Intratumoral cystic change was the only independent predictor of meningioma hemorrhage in multivariate analysis (odds ratio 4.116; 95% confidence interval 1.138-14.894; P = 0.031). CONCLUSIONS: Mixed isodensity/intensity and hyperdensity/intensity on computed tomography/magnetic resonance imaging in conjunction with heterogenous enhancement, a dural tail, and varying degrees of peritumoral brain edema suggest a high possibility of HM. Presence of intratumoral cystic change was an independent risk factor associated with meningioma hemorrhage.

Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study.

OBJECTIVE: To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). MATERIALS AND METHODS: A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer's disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. RESULTS: The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aß42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer's continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer's disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. CONCLUSION: We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.

Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography.

OBJECTIVE: The aim of this study was to investigate the effects of plaque-related factors on the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system (AI-CADS). METHODS: Patients who underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) were retrospectively included in this study. The degree of stenosis in each vessel was collected from CCTA and ICA, and the information on plaque-related factors (plaque length, plaque type, and coronary artery calcium score (CAC)) of the vessels with plaques was collected from CCTA. RESULTS: In total, 1224 vessels in 306 patients (166 men; 65.7 ± 10.1 years) were analyzed. Of these, 391 vessels in 249 patients showed significant stenosis using ICA as the gold standard. Using per-vessel as the unit, the area under the curves of coronary stenosis ≥ 50% for AI-CADS, doctor, and AI-CADS + doctor was 0.764, 0.837, and 0.853, respectively. The accuracies in interpreting the degree of coronary stenosis were 56.0%, 68.1%, and 71.2%, respectively. Seven hundred fifty vessels showed plaques on CCTA; plaque type did not affect the interpretation results by AI-CADS (chi-square test: p = 0.0093; multiple logistic regression: p = 0.4937). However, the interpretation results for plaque length (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0061) and CACs (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0001) were significantly different. CONCLUSION: AI-CADS has an ability to distinguish ≥ 50% coronary stenosis, but additional manual interpretation based on AI-CADS is necessary. The plaque length and CACs will affect the diagnostic performance of AI-CADS. KEY POINTS: • AI-CADS can help radiologists quickly assess CCTA and improve diagnostic confidence. • Additional manual interpretation on the basis of AI-CADS is necessary. • The plaque length and CACs will affect the diagnostic performance of AI-CADS.

Abnormal corpus callosum induced by overt hepatic encephalopathy impairs interhemispheric functional coordination in cirrhosis patients.

BACKGROUND: Although overt hepatic encephalopathy (OHE) patients were shown to have bilaterally symmetrical structural and functional abnormalities in the whole brain, few studies have focused on the bilateral cerebral hemisphere commissural fibers and measured functional coordination between bilateral hemispheres. This study aimed to investigate the structural changes of the corpus callosum (CC) and interhemispheric functional coordination in patients with OHE and to test the hypothesis that abnormal CC induced by OHE impairs interhemispheric functional coordination in cirrhosis patients. METHODS: The microstructural integrity and the volumes of each subregion of the CC were analyzed by diffusion tensor imaging (DTI) and three-dimensional T1-weighted imaging. Voxel-mirrored homotopic connectivity (VMHC) was derived from resting-state functional magnetic resonance imaging (MRI). RESULTS: Compared with the healthy controls (HCs) and patients without hepatic encephalopathy (noHE), the OHE group showed decreased volumes in all subregions of the CC. In OHE patients, the decreased fractional anisotropy (FA) of CC-5 correlated with decreased VMHC in the middle occipital gyrus (MOG) and precuneus. The value of FA in CC-5 and the volumes of CC-3, CC-4, and CC-5 showed correlations with neuropsychological performance in patients with OHE. CONCLUSIONS: These findings suggest that impairment of interhemispheric white matter pathways may disturb the functional connectivity associated with coordination and neurocognitive performance.

Misdiagnosis and Delay of Diagnosis in Hemorrhagic Meningioma: A Case Series and Review of the Literature.

OBJECTIVE: To evaluate the clinicoradiologic characteristics of hemorrhagic meningiomas (HMs) that are missed or misdiagnosed on radiologic imaging studies. METHODS: Clinical and radiologic data from 6 patients with HM who were initially misdiagnosed were collected and recorded respectively. In addition, we performed a literature review for misdiagnosed HM and summarized the results. RESULTS: Five of the 6 patients with misdiagnosed HM were female, and 1 was male. Both computed tomography (CT) and magnetic resonance imaging were performed in 4 patients, and CT alone was performed in 2. On CT, the HM was heterogeneously hyperdense in 5 patients and isodense in 1 patient. In all 4 patients who underwent magnetic resonance imaging, the HM was mixed iso- and hypointense on T1-weighted imaging and heterogeneously hyperintense on T2-weighted imaging. Marked heterogeneous contrast enhancement was observed in 2 patients, strong rim enhancement in 1, and peripheral enhancement in 1. The dural tail sign was seen in only 1 patient. The initial radiologic misdiagnoses were subdural hematoma (n = 1), malignant glioma (n = 1), ruptured arterial aneurysm (n = 1), metastasis (n = 2), and uncertain (n = 1). In the literature review, 22 cases of HM diagnostic error were collected. The main misdiagnoses were subdural hematoma (27.3%), traumatic hematoma (13.6%), vascular anomaly (13.6%), malignant glioma (4.5%), and metastasis (4.5%). CONCLUSIONS: Our study showed that in patients with HM with inadequate imaging evaluation, a small tumor associated with massive hematoma and atypical imaging features was more likely to be misdiagnosed.

An individualized prediction of time to cognitive impairment in Parkinson's disease: A combined multi-predictor study.

BACKGROUND: Cognitive impairment (CI) is important for the prognosis of Parkinson's disease (PD). Early prediction whether and when cognitive decline from normal cognition (NC) will occur is crucial for preventing or delaying the progression timely. The current study aimed to provide a personalized risk assessment of CI by using baseline information and establishing a multi-predictor nomogram. METHODS: 108 patients with PD were collected from the Parkinson's Progression Markers Initiative (PPMI), of whom 58 had progressed to CI and 50 remained NC during 5-year follow up. Radiomics signatures were obtained by using least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. Clinical factors and laboratory biomarkers were selected by multivariate Cox regression analysis. The combined model of radiomics signatures and clinical risk factors was developed by a multivariate Cox proportional hazard model. A multi-predictor nomogram derived from the combined model was established for individualized estimation of time to progress (TTP) of CI. We analyzed the risk of two subgroups of the combined model by Kaplan-Meier (KM) analysis. RESULTS: The combined model showed the best performance with a C-index of 0.988 and 0.926 in the training and validation datasets. KM analysis verified significant TTP of CI (P＜0.05) between two subgroups stratified by the cutoff value (-0.058). CONCLUSION: The combined model and its multi-predictor nomogram can be used to perfectly and individually predict the TTP of CI for patients with PD. Stratification of PD will benefit its timely clinical intervention and the delay and prevention of CI.

Differentiating Parkinson's disease motor subtypes: A radiomics analysis based on deep gray nuclear lesion and white matter.

OBJECTIVE: To investigate the feasibility of radiomics analysis of brain MR images to differentiate Parkinson's disease motor subtypes. METHODS: 42 postural instability gait difficulty (PIGD) patients, 92 tremor-dominant (TD) patients and 96 healthy controls were included from the Parkinson's Progressive Marker Initiative public database. For each subject, 4850 radiomic features from 148 cortical and 14 subcortical brain regions were extracted. The variance threshold and the least absolute shrinkage and selection operator were used to select the optimal features. Classification models based on Support Vector Machine, Logistic Regrcession, and Multi-Layer Perceptron were constructed to assess the performance of optimal features in the discrimination of the two subtypes. Correlations between radiomic features and clinical scores of the two subtypes were estimated. RESULTS: The Support Vector Machine demonstrated the best performance in discriminating between the two subtypes, and the mean area under the curve was 0.833 (specificity = 83.3%, sensitivity = 75.0%, and accuracy = 80.7%). For the postural instability gait difficulty patients, these optimal features in the hippocampal showed closed correlations with the Montreal Cognitive Assessment scores (P < 0.05). CONCLUSION: The results of our study provide preliminary evidence that radiomics analysis of brain MR images could allow discrimination between patients with TD, PIGD and control subjects and has great potential value in the clinical practice.