Glymphatic dysfunction in patients with early-stage amyotrophic lateral sclerosis.

Recently, an astrocytic aquaporin 4-dependent drainage system, that is, the glymphatic system, has been identified in the live murine and human brain. Growing evidence suggests that glymphatic function is impaired in patients with several neurodegenerative diseases, including Alzheimer's and Parkinson's disease. As the third most common neurodegenerative disease, although animal studies have indicated that early glymphatic dysfunction is likely an important pathological mechanism underpinning amyotrophic lateral sclerosis (ALS), no available study has been conducted to thoroughly assess glymphatic function in vivo in ALS patients to date, particularly in patients with early-stage ALS. Thus, using diffusion tensor imaging analysis along the perivascular space (ALPS) index, an approximate measure of glymphatic function in vivo, we aimed to explore whether glymphatic function is impaired in patients with patients with early-stage ALS, and the diagnostic performance of the ALPS index in distinguishing between patients with early-stage ALS and healthy subjects. We also aimed to identify the relationships between glymphatic dysfunction and clinical disabilities and sleep problems in patients with early-stage ALS. In this retrospective study, King's Stage 1 ALS patients were defined as patients with early-stage ALS. We enrolled 56 patients with early-stage ALS and 32 age- and sex-matched healthy control subjects. All participants completed clinical screening, sleep assessment and ALPS index analysis. For the sleep assessment, the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale and polysomnography were used. Compared with healthy control subjects, patients with early-stage ALS had a significantly lower ALPS index after family-wise error correction (P < 0.05). Moreover, receiver operating characteristic analysis showed that the area under the curve for the ALPS index was 0.792 (95% confidence interval 0.700-0.884). Partial correlation analyses showed that the ALPS index was significantly correlated with clinical disability and sleep disturbances in patients with early-stage ALS. Multivariate analysis showed that sleep efficiency (r = 0.419, P = 0.002) and periodic limb movements in sleep index (r = -0.294, P = 0.017) were significant predictive factors of the ALPS index in patients with early-stage ALS. In conclusion, our study continues to support an important role for glymphatic dysfunction in ALS pathology, and we provide additional insights into the early diagnostic value of glymphatic dysfunction and its correlation with sleep disturbances in vivo in patients with early-stage ALS. Moreover, we suggest that early improvement of glymphatic function may be a promising strategy for slowing the neurodegenerative process in ALS. Future studies are needed to explore the diagnostic and therapeutic value of glymphatic dysfunction in individuals with presymptomatic-stage neurodegenerative diseases.

LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images.

BACKGROUND: The accurate identification and evaluation of lymph nodes by CT images is of great significance for disease diagnosis, treatment, and prognosis. PURPOSE: To assess the lymph nodes' segmentation, size, and station by artificial intelligence (AI) for unenhanced chest CT images and evaluate its value in clinical scenarios. MATERIAL AND METHODS: This retrospective study proposed an end-to-end Lymph Nodes Analysis System (LNAS) consisting of three models: the Lymph Node Segmentation model (LNS), the Mediastinal Organ Segmentation model (MOS), and the Lymph Node Station Registration model (LNR). We selected a healthy chest CT image as the template image and annotated 14 lymph node station masks according to the IASLC to build the lymph node station mapping template. The exact contours and stations of the lymph nodes were annotated by two junior radiologists and reviewed by a senior radiologist. Patients aged 18 and above, who had undergone unenhanced chest CT and had at least one suspicious enlarged mediastinal lymph node in imaging reports, were included. Exclusions were patients who had thoracic surgeries in the past 2 weeks or artifacts on CT images affecting lymph node observation by radiologists. The system was trained on 6725 consecutive chest CTs that from Tianjin Medical University General Hospital, among which 6249 patients had suspicious enlarged mediastinal lymph nodes. A total of 519 consecutive chest CTs from Qilu Hospital of Shandong University (Qingdao) were used for external validation. The gold standard for each CT was determined by two radiologists and reviewed by one senior radiologist. RESULTS: The patient-level sensitivity of the LNAS system reached of 93.94% and 92.89% in internal and external test dataset, respectively. And the lesion-level sensitivity (recall) reached 89.48% and 85.97% in internal and external test dataset. For man-machine comparison, AI significantly apparently shortened the average reading time (p < 0.001) and had better lesion-level and patient-level sensitivities. CONCLUSION: AI improved the sensitivity lymph node segmentation by radiologists with an advantage in reading time.

Amygdala abnormalities across disease stages in patients with sporadic amyotrophic lateral sclerosis.

To examine selective atrophy patterns and resting-state functional connectivity (FC) alterations in the amygdala at different stages of amyotrophic lateral sclerosis (ALS), and to explore any correlations between amygdala abnormalities and neuropsychiatric symptoms. We used the King's clinical staging system for ALS to divide 83 consecutive patients with ALS into comparable subgroups at different disease stages. We explored the pattern of selective amygdala subnucleus atrophy and amygdala-based whole-brain FC alteration in these patients and 94 healthy controls (HCs). Cognitive and emotional functions were also evaluated using a neuropsychological test battery. There were no significant differences between ALS patients at King's stage 1 and HCs for any amygdala subnucleus volumes. Compared with HCs, ALS patients at King's stage 2 had significantly lower left accessory basal nucleus and cortico-amygdaloid transition volumes. Furthermore, ALS patients at King's stage 3 demonstrated significant reductions in most amygdala subnucleus volumes and global amygdala volumes compared with HCs. Notably, amygdala-cuneus FC was increased in ALS patients at King's stage 3. Specific subnucleus volumes were significantly associated with Mini-Mental State Examination scores and Hamilton Anxiety Rating Scale scores in ALS patients. In conclusions, our study provides a comprehensive profile of amygdala abnormalities in ALS patients. The pattern of amygdala abnormalities in ALS patients differed greatly across King's clinical disease stages, and amygdala abnormalities are an important feature of patients with ALS at relatively advanced stages. Moreover, our findings suggest that amygdala volume may play an important role in anxiety and cognitive dysfunction in ALS patients.

Spatio-Temporal Graph Hubness Propagation Model for Dynamic Brain Network Classification.

Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting features from independent brain networks divided by sliding windows, making them hard to reveal the high-order dynamic evolution laws of functional brain networks. Additionally, they cannot effectively extract the spatio-temporal topology features in dynamic brain networks. In this paper, we propose to use optimal transport (OT) theory to capture the topology evolution of the dynamic brain networks, and develop a multi-channel spatio-temporal graph convolutional network that collaboratively extracts the temporal and spatial features from the evolution networks. Specifically, we first adaptively evaluate the graph hubness of brain regions in the brain network of each time window, which comprehensively models information transmission among multiple brain regions. Second, the hubness propagation information across adjacent time windows is captured by optimal transport, describing high-order topology evolution of dynamic brain networks. Moreover, we develop a spatio-temporal graph convolutional network with attention mechanism to collaboratively extract the intrinsic temporal and spatial topology information from the above networks. Finally, the multi-layer perceptron is adopted for classifying the dynamic brain network. The extensive experiment on the collected epilepsy dataset and the public ADNI dataset show that our proposed method not only outperforms several state-of-the-art methods in brain disease diagnosis, but also reveals the key dynamic alterations of brain connectivities between patients and healthy controls.

Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study.

PURPOSE: To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data. MATERIALS AND METHODS: In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve. RESULTS: A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People's Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model. CONCLUSION: The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.

Choroid plexus enlargement in amyotrophic lateral sclerosis patients and its correlation with clinical disability and blood-CSF barrier permeability.

BACKGROUND: Using in vivo neuroimaging techniques, growing evidence has demonstrated that the choroid plexus (CP) volume is enlarged in patients with several neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease. However, although animal and postmortem findings suggest that CP abnormalities are likely important pathological mechanisms underlying amyotrophic lateral sclerosis (ALS), the third most common neurodegenerative disease, no available study has been conducted to thoroughly assess CP abnormalities and their clinical relevance in vivo in ALS patients to date. Thus, we aimed to determine whether in vivo CP enlargement may occur in ALS patients. We also aimed to identify the relationships of CP volume with clinical disabilities and blood-CSF barrier (BCSFB) permeability in ALS patients. METHODS: In this retrospective study, based on structural MRI data, CP volume was assessed using a Gaussian mixture model and underwent further manual correction in 155 ALS patients and 105 age- and sex-matched HCs from October 2021 to April 2023. The ALS Functional Rating Scale-Revised (ALSFRS-R) was used to assess clinical disability. The CSF/serum albumin quotient (Qalb) was used to assess BCSFB permeability. Moreover, all the ALS patients completed genetic testing, and according to genetic testing, the ALS patients were further divided into genetic ALS subgroup and sporadic ALS subgroup. RESULTS: We found that compared with HCs, ALS patients had a significantly higher CP volume (p < 0.001). Moreover, compared with HCs, CP volume was significantly increased in both ALS patients with and without known genetic mutations after family-wise error correction (p = 0.006 and p < 0.001, respectively), while there were no significant differences between the two ALS groups. Furthermore, the CP volume was significantly correlated with the ALSFRS-r score (r = -0.226; p = 0.005) and the Qalb (r = 0.479; p < 0.001) in ALS patients. CONCLUSION: Our study first demonstrates CP enlargement in vivo in ALS patients, and continues to suggest an important pathogenetic role for CP abnormalities in ALS. Moreover, assessing CP volume is likely a noninvasive and easy-to-implement approach for screening BCSFB dysfunction in ALS patients.

Diffusion tensor imaging of normal-appearing white matter in unilateral cerebral arterial occlusive disease.

PURPOSE: To use MR with diffusion tensor imaging (DTI) and conventional and high b value to assess diffusion changes in normal-appearing white matter (NAWM) in patients with unilateral, severe stenosis, or occlusion of the middle cerebral artery (MCA). MATERIALS AND METHODS: In total, 28 patients with NAWM and unilateral, severe stenosis, or occlusion of the MCA underwent DTI with b values 1000 and 2200 s/mm(2) at 3.0T MR. Fractional anisotropy (FA), apparent diffusion coefficient (ADC), radial diffusivity (eigenvalues λ1 , λ2 ), and axial diffusivity (eigenvalue λ3 ) were measured for the ipsilateral and contralateral corona radiata. RESULTS: Mean FA was significantly lower for the ipsilateral than contralateral corona radiata with high b value, 2200 s/mm(2) , and ipsilateral corona radiata with conventional low b value, 1000 s/mm(2) (all P < 0.01). Mean ADC, λ1 , λ2 , and λ3 were significantly higher for the ipsilateral than contralateral corona radiata with high b value (all P < 0.05) but not for ipsilateral than contralateral corona radiata with low b value (P > 0.05). CONCLUSION: DTI with a high b value detects diffusion changes in NAWM in patients with unilateral, severe stenosis, or occlusion of the MCA not seen with conventional b value or conventional MRI contrasts.

Neuroimaging anomalies in asymptomatic middle cerebral artery steno-occlusive disease with normal-appearing white matter.

Background: Asymptomatic chronic cerebrovascular steno-occlusive disease is common, but the cognitive function and alterations in the brain's structural and functional profiles have not been well studied. This study aimed to reveal whether and how patients with asymptomatic middle cerebral artery (MCA) steno-occlusive disease and normal-appearing white matter differ in brain structural and functional profiles from normal controls and their correlations with cognitive function. Methods: In all, 26 patients with asymptomatic MCA steno-occlusive disease and 22 healthy controls were compared for neurobehavioral assessments, brain volume, cortical thickness, fiber connectivity density (FiCD) value, and resting-state functional connectivity (FC) using multimodal MRI. We also investigated the associations between abnormal cortical thicknesses, FiCD values, and functional connectivities with the neurobehavioral assessments. Results: Patients performed worse on memory tasks (Auditory Verbal Learning Test-Huashan version) compared with healthy controls. Patients were divided into two groups: the right group (patients with right MCA steno-occlusive disease) and the left group (patients with left MCA steno-occlusive disease). The left group showed significant cortical thinning in the left superior parietal lobule, while the right group showed significant cortical thinning in the right superior parietal lobule and caudal portion of the right middle frontal gyrus. Increased FiCD values in the superior frontal region of the left hemisphere were observed in the left group. In addition, a set of interhemispheric and intrahemispheric FC showed a significant decrease or increase in both the left and right groups. Many functional connectivity profiles were positively correlated with cognitive scores. No correlation was found between cortical thickness, FiCD values, and cognitive scores. Conclusion: Even if the patients with MCA steno-occlusive disease were asymptomatic and had normal-appearing white matter, their cognitive function and structural and functional profiles had changed, especially the FC. Alterations in FC may be an important mechanism underlying the neurodegenerative process in patients with asymptomatic MCA steno-occlusive disease before structural changes occur, so FC assessment may promote the detection of network alterations, which may be used as a biomarker of disease progression and therapeutic efficacy evaluation in these patients.

Difference of mean Hounsfield units (dHU) between follow-up and initial noncontrast CT scan predicts 90-day poor outcome in spontaneous supratentorial acute intracerebral hemorrhage with deep convolutional neural networks.

OBJECTIVES: This study aimed to investigate the usefulness of a new non-contrast CT scan (NCCT) sign called the dHU, which represented the difference in mean Hounsfield unit values between follow-up and the initial NCCT for predicting 90-day poor functional outcomes in acute supratentorial spontaneous intracerebral hemorrhage(sICH) using deep convolutional neural networks. METHODS: A total of 377 consecutive patients with sICH from center 1 and 91 patients from center 2 (external validation set) were included. A receiver operating characteristic (ROC) analysis was performed to determine the critical value of dHU for predicting poor outcome at 90 days. Modified Rankin score (mRS) >3 or >2 was defined as the primary and secondary poor outcome, respectively. Two multivariate models were developed to test whether dHU was an independent predictor of the two unfavorable functional outcomes. RESULTS: The ROC analysis showed that a dHU >2.5 was a critical value to predict the poor outcomes (mRS >3) in sICH. The sensitivity, specificity, and accuracy of dHU >2.5 for poor outcome prediction were 37.5%, 86.0%, and 70.6%, respectively. In multivariate models developed after adjusting for all elements of the ICH score and hematoma expansion, dHU >2.5 was an independent predictor of both primary and secondary poor outcomes (OR = 2.61, 95% CI [1.32,5.13], P = 0.006; OR = 2.63, 95% CI [1.36,5.10], P = 0.004, respectively). After adjustment for all possible significant predictors (p < 0.05) by univariate analysis, dHU >2.5 had a positive association with primary and secondary poor outcomes (OR = 3.25, 95% CI [1.52,6.98], P = 0.002; OR = 3.42, 95% CI [1.64,7.15], P = 0.001). CONCLUSIONS: The dHU of hematoma based on serial CT scans is independently associated with poor outcomes after acute sICH, which may help predict clinical evolution and guide therapy for sICH patients.

Differentiation of Parkinson's disease and Parkinsonism predominant multiple system atrophy in early stage by morphometrics in susceptibility weighted imaging.

Background and purpose: We previously established a radiological protocol to discriminate multiple system atrophy-parkinsonian subtype (MSA-P) from Parkinson's disease (PD). However, we do not know if it can differentiate early stage disease. This study aimed to investigate whether the morphological and intensity changes in susceptibility weighted imaging (SWI) of the lentiform nucleus (LN) could discriminate MSA-P from PD at early stages. Methods: We retrospectively enrolled patients with MSA-P, PD and sex- and age-matched controls whose brain MRI included SWI, between January 2015 and July 2020 at the Movement Disorder Center. Two specialists at the center reviewed the medical records and made the final diagnosis, and two experienced neuroradiologists performed MRI analysis, based on a defined and revised protocol for conducting morphological measurements of the LN and signal intensity. Results: Nineteen patients with MSA-P and 19 patients with PD, with less than 2 years of disease duration, and 19 control individuals were enrolled in this study. We found that patients with MSA- P presented significantly decreased size in the short line (SL) and corrected short line (cSL), ratio of the SL to the long line (SLLr) and corrected SLLr (cSLLr) of the LN, increased standard deviation of signal intensity (SIsd_LN, cSIsd_LN) compared to patients with PD and controls (P < 0.05). With receiver operating characteristic (ROC) analysis, this finding had a sensitivity of 89.5% and a specificity of 73.7% to distinguish MSA- P from PD. Conclusion: Compared to PD and controls, patients with MSA-P are characterized by a narrowing morphology of the posterior region of the LN. Quantitative morphological changes provide a reference for clinical auxiliary diagnosis.

A Pilot Study of Radiomic Based on Routine CT Reflecting Difference of Cerebral Hemispheric Perfusion.

Background: To explore the effectiveness of radiomics features based on routine CT to reflect the difference of cerebral hemispheric perfusion. Methods: We retrospectively recruited 52 patients with severe stenosis or occlusion in the unilateral middle cerebral artery (MCA), and brain CT perfusion showed an MCA area with deficit perfusion. Radiomics features were extracted from the stenosis side and contralateral of the MCA area based on precontrast CT. Two different region of interest drawing methods were applied. Then the patients were randomly grouped into training and testing sets by the ratio of 8:2. In the training set, ANOVA and the Elastic Net Regression with fivefold cross-validation were conducted to filter and choose the optimized features. Moreover, different machine learning models were built. In the testing set, the area under the receiver operating characteristic (AUC) curve, calibration, and clinical utility were applied to evaluate the predictive performance of the models. Results: The logistic regression (LR) for the triangle-contour method and artificial neural network (ANN) for the semiautomatic-contour method were chosen as radiomics models for their good prediction efficacy in the training phase (AUC = 0.869, 0.873) and the validation phase (AUC = 0.793, 0.799). The radiomics algorithms of the triangle-contour and semiautomatic-contour method were implemented in the whole training set (AUC = 0.870, 0.867) and were evaluated in the testing set (AUC = 0.760, 0.802). According to the optimal cutoff value, these two methods can classify the vascular stenosis side class and normal side class. Conclusion: Radiomic predictive feature based on precontrast CT image could reflect the difference of cerebral hemispheric perfusion to some extent.

Radiomics for predicting revised hematoma expansion with the inclusion of intraventricular hemorrhage growth in patients with supratentorial spontaneous intraparenchymal hematomas.

BACKGROUND: Previous radiomics analyses of hematoma expansion have been based on the traditional definition, which only focused on changes in intraparenchymal volume. However, the ability of radiomics-related models to predict revised hematoma expansion (RHE) with the inclusion of intraventricular hemorrhage expansion remains unclear. To develop and validate a noncontrast computed tomography (NCCT)-based clinical- semantic-radiomics nomogram to identify supratentorial spontaneous intracerebral hemorrhage (sICH) patients with RHE on admission. METHODS: In this double-center retrospective study, data from 376 patients with sICH (training set: n=299; test set: n=77; external validation cohort: n=91) were reviewed. A radiomics model, a clinical-semantic model, and a combined model were then constructed based on the logistic regression machine learning approach. Radiomics features were extracted and selected by least absolute shrinkage and selection operator (LASSO) with 5-fold cross validation. Furthermore, the classical BRAIN scoring system was also constructed to predict RHE. Discriminative performance of the models was evaluated on the training and test set with area under the curve (AUC) and decision curve analysis (DCA). RESULTS: The addition of radiomics to clinical-semantic factors significantly improved the prediction performance of RHE compared with the clinical-semantic model alone in the training (AUC, 0.94 vs. 0.81, P<0.05) and test (AUC, 0.84 vs. 0.71, P<0.05) sets, with similar results in the validation set (AUC, 0.83 vs. 0.69, P<0.05). Moreover, the discrimination efficacy of the BRAIN score was significantly lower than the other 3 models (AUC of 0.71 in the training set, P<0.05). CONCLUSIONS: The clinical-semantic-radiomics combined model had the greatest potential for discriminating RHE, and significantly outperformed the classical BRAIN scoring system.

Hypothalamic subregion abnormalities are related to body mass index in patients with sporadic amyotrophic lateral sclerosis.

OBJECTIVE: To investigate atrophy patterns in hypothalamic subunits at different stages of ALS and examine correlations between hypothalamic subunit volume and clinical information. METHODS: We used the King's clinical staging system to divide 91 consecutive ALS patients into the different disease stages. We investigated patterns of hypothalamic atrophy using a recently published automated segmentation method in ALS patients and in 97 healthy controls. We recorded all subjects' demographic and clinical information. RESULTS: Compared with healthy controls, we found significant atrophy in the bilateral anterior-superior subunit and the superior tubular subunit, as well as a reduction in global hypothalamic volume in ALS patients. When we used the King's clinical staging system to divide patients into the different disease stages, we found neither global nor specific subunit atrophy until King's stage 3 in the hypothalamus. Moreover, specific subunit volumes were significantly associated with body mass index. CONCLUSIONS: In a relatively large sample of Chinese patients with ALS, using a recently published automated segmentation method for the hypothalamus, we found the pattern of hypothalamic atrophy in ALS patients differed greatly across King's clinical disease stages. Moreover, specific hypothalamic subunit atrophy may play an important role in energy metabolism in ALS patients. Thus, our findings suggest that hypothalamic atrophy may have potential phenotypic associations, and improved energy metabolism may become an important component of individualised therapy for ALS.

Neuroimaging Anomalies in Community-Dwelling Asymptomatic Adults With Very Early-Stage White Matter Hyperintensity.

White matter hyperintensity (WMH) is common in healthy adults in their 60s and can be seen as early as in their 30s and 40s. Alterations in the brain structural and functional profiles in adults with WMH have been repeatedly studied but with a focus on late-stage WMH. To date, structural and functional MRI profiles during the very early stage of WMH remain largely unexplored. To address this, we investigated multimodal MRI (structural, diffusion, and resting-state functional MRI) profiles of community-dwelling asymptomatic adults with very early-stage WMH relative to age-, sex-, and education-matched non-WMH controls. The comparative results showed significant age-related and age-independent changes in structural MRI-based morphometric measures and resting-state fMRI-based measures in a set of specific gray matter (GM) regions but no global white matter changes. The observed structural and functional anomalies in specific GM regions in community-dwelling asymptomatic adults with very early-stage WMH provide novel data regarding very early-stage WMH and enhance understanding of the pathogenesis of WMH.

Automated detection of 3D midline shift in spontaneous supratentorial intracerebral haemorrhage with non-contrast computed tomography using deep convolutional neural networks.

Deep learning (DL)-based convolutional neural networks facilitate more accurate detection and rapid analysis of MLS. Our objective was to assess the feasibility of applying a DL-based convolutional neural network to non-contrast computed tomography (CT) for automated 2D/3D brain midline shift measurement and outcome prediction after spontaneous intracerebral haemorrhage. In this retrospective study, 140 consecutive patients were referred for CT assessment of sICH from January 2014 to April 2019. The level of consciousness of patients was evaluated using the Glasgow Coma Scale (GCS) score, and the Glasgow Outcome Scale (GOS) score was calculated to classify the outcome. The distance of midline shift (MLS-D) and volume of midline shift (MLS-V) were automatically measured via DL methods. Patients were divided into three groups based on GCS scores: mild degree (GCS score: 13-15), moderate degree (GCS score: 9-12), and severe degree (GCS score: 3-8). Spearman's correlation analysis revealed statistically significant (P<0.01) positive correlation between GCS and MLS-D (r=0.709) and MLS-V (r=0.754). The AUC of MLS-V was slightly larger than that of MLS-D (0.831 vs 0.799, P=0.318) in the midline shifting group. The AUC of MLS-V was significantly larger than that of MLS-D (0.854 vs 0.736, P=0.03) in patients with severe degree GCS scores. The DL-based measurements of both MLS-D and MLS-V enable the assessment of consciousness and the prediction of the outcome of sICH. Compared to MLS-D, MLS-V measurement can better indicate mass effect and predict outcomes, particularly in severe cases.

An MSCT-based radiomics nomogram combined with clinical factors can identify Crohn's disease and ulcerative colitis.

BACKGROUND: We established and evaluated a radiomics nomogram based on multislice computed tomography (MSCT) arterial phase contrast-enhanced images to distinguish between Crohn's disease (CD) and ulcerative colitis (UC) objectively, quantitatively, and reproducibly. METHODS: MSCT arterial phase-enhancement images of 165 lesions (99 CD, 66 UC) in 87 patients with inflammatory bowel disease (IBD) confirmed by endoscopy or surgical pathology were retrospectively analyzed. A total of 132 lesions (80%) were selected as the training cohort and 33 lesions (20%) as the test cohort. A total of 1648 radiomic features were extracted from each region of interest (ROI), and the Pearson correlation coefficient and tree-based method were used for feature selection. Five machine learning classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), and linear discriminative analysis (LDA), were trained. The best classifier was evaluated and obtained, and the results were transformed into the Rscore. Three clinical factors were screened out from 8 factors by univariate analysis. The logistic regression method was used to synthesize the significant clinical factors and the Rscore to generate the nomogram, which was compared with the clinical model and LR model. RESULTS: Among all machine learning classifiers, LR performed the best (AUC =0.8077, accuracy =0.697, sensitivity =0.8, specificity =0.5385), SGD model had the second best performance (AUC =0.8, accuracy =0.6667, sensitivity =0.75, specificity =0.5385), and the DeLong test results showed that there was no significant difference between LR and SGD (P=0.465>0.05), while the other models performed poorly. Texture features had the greatest impact on classification results among all imaging features. The significant features of the LR model were used to calculate the Rscore. The 3 significant clinical factors were perienteric edema or inflammation, CT value of arterial phase-enhancement (AP-CT value), and lesion location. Finally, a nomogram was constructed based on the 3 significant clinical factors and the Rscore, whose AUC (0.8846) was much higher than that of the clinical model (0.6154) and the LR model (0.8077). CONCLUSIONS: The nomogram is expected to provide a new auxiliary tool for radiologists to quickly identify CD and UC.

Substantia Nigra Radiomics Feature Extraction of Parkinson's Disease Based on Magnitude Images of Susceptibility-Weighted Imaging.

BACKGROUND: It is reported that radiomic features extracted from quantitative susceptibility mapping (QSM) had promising clinical value for the diagnosis of Parkinson's disease (PD). We aimed to explore the usefulness of radiomics features based on magnitude images to distinguish PD from non-PD controls. METHODS: We retrospectively recruited PD patients and controls who underwent brain 3.0T MR including susceptibility-weighted imaging (SWI). A total of 396 radiomics features were extracted from the SN of 95 PD patients and 95 non-PD controls based on SWI. Intra-/inter-observer correlation coefficients (ICCs) were applied to measure the observer agreement for the radiomic feature extraction. Then the patients were randomly grouped into training and validation sets in a ratio of 7:3. In the training set, the maximum correlation minimum redundancy algorithm (mRMR) and the least absolute shrinkage and selection operator (LASSO) were conducted to filter and choose the optimized subset of features, and a radiomics signature was constructed. Moreover, radiomics signatures were constructed by different machine learning models. Area under the ROC curves (AUCs) were applied to evaluate the predictive performance of the models. Then correlation analysis was performed to evaluate the correlation between the optimized features and clinical factors. RESULTS: The intro-observer CC ranged from 0.82 to 1.0, and the inter-observer CC ranged from 0.77 to 0.99. The LASSO logistic regression model showed good prediction efficacy in the training set [AUC = 0.82, 95% confidence interval (CI, 0.74-0.88)] and the validation set [AUC = 0.81, 95% CI (0.68-0.91)]. One radiomic feature showed a moderate negative correlation with Hoehn-Yahr stage (r = -0.49, P = 0.012). CONCLUSION: Radiomic predictive features based on SWI magnitude images could reflect the Hoehn-Yahr stage of PD to some extent.

Hippocampal subfield and anterior-posterior segment volumes in patients with sporadic amyotrophic lateral sclerosis.

Neuroimaging studies of hippocampal volumes in patients with amyotrophic lateral sclerosis (ALS) have reported inconsistent results. Our aims were to demonstrate that such discrepancies are largely due to atrophy of different regions of the hippocampus that emerge in different disease stages of ALS and to explore the existence of co-pathology in ALS patients. We used the well-validated King's clinical staging system for ALS to classify patients into different disease stages. We investigated in vivo hippocampal atrophy patterns across subfields and anterior-posterior segments in different King's stages using structural MRI in 76 ALS patients and 94 health controls (HCs). The thalamus, corticostriatal tract and perforant path were used as structural controls to compare the sequence of alterations between these structures and the hippocampal subfields. Compared with HCs, ALS patients at King's stage 1 had lower volumes in the bilateral posterior subiculum and presubiculum; ALS patients at King's stage 2 exhibited lower volumes in the bilateral posterior subiculum, left anterior presubiculum and left global hippocampus; ALS patients at King's stage 3 showed significantly lower volumes in the bilateral posterior subiculum, dentate gyrus and global hippocampus. Thalamic atrophy emerged at King's stage 3. White matter tracts remained normal in a subset of ALS patients. Our study demonstrated that the pattern of hippocampal atrophy in ALS patients varies greatly across King's stages. Future studies in ALS patients that focus on the hippocampus may help to further clarify possible co-pathologies in ALS.

[Diffusion tensor imaging of elderly leukoaraiosis and its correlation with cognitive function].

OBJECTIVE: To apply diffusion tensor imaging (DTI) for investigating the correlation between leukoaraiosis (LA) lesion's fraction anisotropy (FA) as well as average diffusion coefficient (DCavg) and LA severity, so as to explore DTI changes in microstructure of white marrow with normal ordinary MRI and its correlation with cognitive function. METHODS: Sixty LA patients and 30 healthy elderly people accepted DTI examination to detect the value of DCavg and FA of LA lesion and normal white marrow. The Mini-Mental State Examination (MMSE) was used for assessing cognitive function. RESULTS: LA severity (0 grade to 3 grade) was positively associated with DCavg, i.e. the more severe was LA, the higher DCavg was (0.66 +/- 0.05 to 1.09 +/- 0.06, P < 0.05); and it was negatively associated with FA, i.e. the more severe was LA, the lower FA was (0.42 +/- 0.04 to 0.26 +/- 0.03, P < 0.05). Neuropsychology tests (Mini-Mental State Examination, MMSE) had a significant relationship with DCavg and FA of normal appearing white matter (NAWM) in LA patients (P < 0.05), especially in anterior horn (Pearson Correlation Coefficient 0.422, P < 0.05) and in centrum semiovale (Pearson Correlation Coefficient -0.495, P < 0.01). CONCLUSIONS: In DTI examination, DCavg and FA of LA displays characteristic changes. Therefore, DTI can detect the macrostructaral changes of white marrow with normal MRI and these changes are related to cognitive function.

Morphology and signal changes of the lentiform nucleus based on susceptibility weighted imaging in parkinsonism-predominant multiple system atrophy.

INTRODUCTION: It remains challenging to make a differential diagnosis between atypical parkinsonism and Parkinson's disease (PD) from routine neuroimaging. This case-control study aimed to quantitatively investigate both morphological and signal intensity changes in susceptibility weighted imaging (SWI) of the lentiform nucleus (LN) for discriminating parkinsonism-predominant multiple system atrophy (MSA-P) from PD. METHODS: We retrospectively enrolled patients with MSA-P, PD, and sex- and age-matched controls between January 2016 and November 2019 at the Movement Disorder Center who underwent 3T MR imaging of brain with SWI sequence. Two specialists at the center reviewed the medical records and made the final diagnosis, and two experienced neuroradiologists performed MRI image analysis based on a defined radiological protocol to conduct the ROI-based morphological measurements of the LN and the signal intensity. RESULTS: A total of 19 patients with MSA-P, 19 patients with PD and 19 controls were enrolled in this study. We found that patients with MSA-P had significant decreases size in the short line (SL) and the ratio of the SL and the long line (SLLr) and increased value in the signal intensity standard deviation of the LN (SIsd_LN) compared with the patients with PD and with the controls (P < 0.05). Combining these three indexes, this finding had a sensitivity of 94.7% and a specificity of 63.2% to distinguish MSA-P from PD. CONCLUSION: As compared to PD and control subjects, the SA-P patients are characterized by narrowing morphology and the inhomogeneous signal intensity of the posterior region of LN. The quantitative morphological change is a possible potential marker to differentiate MSA-P from PD on SWI.