OBJECTIVE: To investigate the liver T1rho values for detecting fibrosis, and the potential impact of fatty liver on T1rho measurements. MATERIALS AND METHODS: This study included 18 healthy subjects, 18 patients with fatty liver, and 18 patients with liver fibrosis, who underwent T1rho MRI and mDIXON collections. Liver T1rho, proton density fat fraction (PDFF) and T2* values were measured and compared among the three groups. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the T1rho values for detecting liver fibrosis. Liver T1rho values were correlated with PDFF, T2* values and clinical data. RESULTS: Liver T1rho and PDFF values were significantly different (p < 0.001), whereas the T2* (p = 0.766) values were similar, among the three groups. Mean liver T1rho values in the fibrotic group (52.6 ± 6.8 ms) were significantly higher than those of healthy subjects (44.9 ± 2.8 ms, p < 0.001) and fatty liver group (45.0 ± 3.5 ms, p < 0.001). Mean liver T1rho values were similar between healthy subjects and fatty liver group (p = 0.999). PDFF values in the fatty liver group (16.07 ± 10.59%) were significantly higher than those of healthy subjects (1.43 ± 1.36%, p < 0.001) and fibrosis group (1.07 ± 1.06%, p < 0.001). PDFF values were similar in healthy subjects and fibrosis group (p = 0.984). Mean T1rho values performed well to detect fibrosis at a threshold of 49.5 ms (area under the ROC curve, 0.855), had a moderate correlation with liver stiffness (r = 0.671, p = 0.012), and no correlation with PDFF, T2* values, subject age, or body mass index (p > 0.05). CONCLUSION: T1rho MRI is useful for noninvasive detection of liver fibrosis, and may not be affected with the presence of fatty liver.
Body Mass Index ; Fatty Liver* ; Fibrosis ; Healthy Volunteers ; Humans ; Liver Cirrhosis* ; Liver* ; Magnetic Resonance Imaging ; Prospective Studies* ; Protons ; ROC Curve

Objective:To identify the potential intracranial inflammation in neuromyelitis optica spectrum disorders(NMOSD) patients without supratentorial MRI lesions using quantitative susceptibility mapping (QSM).Methods:Seventy NMOSD patients and 35 age- and gender-matched healthy controls (NC) underwent QSM, 3D-T 1, diffusion MRI from Beijing Tiantan Hospital during June 2019 to June 2021. Susceptibility was compared among NMOSD patients with acute attack (ANMOSD), NMOSD patients in chronic phase (CNMOSD) and NC. The correlation between susceptibility in several brain regions and the cerebrospinal fluid levels of inflammatory makers were analyzed. Results:NMOSD patients showed different susceptibility in several brain regions including bilateral hippocampus, precuneus, right cuneus, putamen, superior parietal and inferior temporal ( P<0.001) and the posr-hoc showed it is higher than normal. Compared to CNMOSD patients, the ANMOSD patients showed increased susceptibility in the cuneus (0.009 ± 0.004 vs. 0.005 ± 0.004, P<0.05). There was significant positive correlations between susceptibility and CSF levels of sTREM2 which reflect the active of microglial cells ( r = 0.494, P<0.05). Conclusions:Despite the absence of supratentorial lesions on MRI, increased susceptibility suggests underlying inflammation in the cerebral cortex in both patients with ANMOSD and CNMOSD, and some of them are obviously related to inflammatory markers in CSF. QSM sequence can be used to explore the potential inflammation in NMOSD patients without obvious supratentorial lesions.

Background::Few evidence is available in the early prediction models of behavioral and psychological symptoms of dementia (BPSD) in Alzheimer’s disease (AD). This study aimed to develop and validate a novel genetic-clinical-radiological nomogram for evaluating BPSD in patients with AD and explore its underlying nutritional mechanism.Methods::This retrospective study included 165 patients with AD from the Chinese Imaging, Biomarkers, and Lifestyle (CIBL) cohort between June 1, 2021, and March 31, 2022. Data on demographics, neuropsychological assessments, single-nucleotide polymorphisms of AD risk genes, and regional brain volumes were collected. A multivariate logistic regression model identified BPSD-associated factors, for subsequently constructing a diagnostic nomogram. This nomogram was internally validated through 1000-bootstrap resampling and externally validated using a time-series split based on the CIBL cohort data between June 1, 2022, and February 1, 2023. Area under receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical applicability of the nomogram.Results::Factors independently associated with BPSD were: CETP rs1800775 (odds ratio [OR] = 4.137, 95% confidence interval [CI]: 1.276-13.415, P = 0.018), decreased Mini Nutritional Assessment score (OR = 0.187, 95% CI: 0.086-0.405, P <0.001), increased caregiver burden inventory score (OR = 8.993, 95% CI: 3.830-21.119, P <0.001), and decreased brain stem volume (OR = 0.006, 95% CI: 0.001-0.191, P = 0.004). These variables were incorporated into the nomogram. The area under the ROC curve was 0.925 (95% CI: 0.884-0.967, P <0.001) in the internal validation and 0.791 (95% CI: 0.686-0.895, P <0.001) in the external validation. The calibration plots showed favorable consistency between the prediction of nomogram and actual observations, and the DCA showed that the model was clinically useful in both validations. Conclusion::A novel nomogram was established and validated based on lipid metabolism-related genes, nutritional status, and brain stem volumes, which may allow patients with AD to benefit from early triage and more intensive monitoring of BPSD.Registration::Chictr.org.cn, ChiCTR2100049131.

Objective:To investigate the feasibility and clinical diagnostic value of rapid arterial spin labeling (ASL) imaging in brain glioma.Methods:Patients with glioma admitted to Beijing Tiantan Hospital, Capital Medical University from May 2021 to December 2022 were prospectively enrolled. All patients received MR rapid ASL (scan time: 1 min) and conventional ASL (scan time: 4 min 30 s), where the cerebral blood flow (CBF) perfusion maps were obtained. The qualitative analysis of CBF signal intensity and quantitative analysis of average CBF values from both tumor solid and edema regions were conducted by two radiologists independently. Kappa test and intraclass correlation coefficient ( ICC) were used to analyze the consistency of qualitative and quantitative results, respectively. Results:A total of 30 patients with brain glioma were included. The 2 physicians used rapid ASL to determine low perfusion, isoperfusion, and hyperperfusion in the tumor area in 1, 6, 23 cases and 0, 5, and 25 cases, respectively; and used conventional ASL to determine low perfusion, isoperfusion, and hyperperfusion in the tumor area in 0, 9, and 21 cases, respectively. The results of qualitative analysis of rapid ASL and conventional ASL were highly consistent within and between groups ( Kappa was 0.830 and 0.850 respectively). The results of quantitative analysis of rapid ASL and conventional ASL were highly consistent within and between groups ( ICC 0.940—0.994). Conclusion:Rapid ASL with shorter scanning time could be applied in assessing tissue perfusion in brain glioma and contribute to the clinical diagnosis of gliomas.

Objective:To construct a spatial radiomics model based on the spatial distribution characteristics of supratentorial pilocytic astrocytoma (PA) and ganglioglioma (GG) and to evaluate its differential diagnosis efficiency.Methods:The study was a cross-sectional study. A retrospective collection of 244 patients with episodic PA and GG who attended Beijing Tiantan Hospital of Capital Medical University (Center 1) from June 2016 to June 2022 and 116 patients with episodic PA and GG who attended General Hospital of Eastern Theater Command (Center 2) from March 2019 to October 2022 was performed. The patients in Center 1 were divided into a training set (171 patients) and a validation set (73 patients) in a 7∶3 ratio according to the random number table method, and the patients in Center 2 as a whole were regarded as test sets. All patients underwent MRI. Segmentation of tumor based on enhanced T 1WI and T 2WI images, alignment to standard space to generate a statistical parametric mapping of tumor locations and intergroup comparison was conducted. The Johns Hopkins University template was used to extract 189 tumor location features to construct a spatial model of tumor location; PyRadiomic 3.0.1 software was used to extract tumor radiomics features to construct a radiomics model; and the two models were fused to construct a spatial radiomics model. The efficacy of spatial radiomics model, spatial model, and radiomics model to discriminate PA from GG was analyzed using receiver operating characteristic curves and area under the curve (AUC). The generalization ability of the model was assessed by the difference in accuracy between the test sets and the validation sets (ΔACC). The clinical utility of the model was compared using clinical decision curves and calibration curves. Results:The statistical parametric mapping of lesions showed that supratentorial PA was vulnerable to medial structure areas such as suprasellar region, thalamus, basal ganglia and frontal lobe, temporal lobe, parietal lobe. GG was mainly distributed in bilateral temporal lobes, as well as frontal lobe, occipital lobe and parietal lobe. The AUCs of spatial radiomics model, radiomics model and spatial model to identify PA and GG in the test set were 0.876, 0.785, and 0.819, with accuracies of 77.59%, 72.41%, and 77.14%, respectively, and ΔACCs in the test set and validation set were 11.6%, 15.43%, and 6.94%, respectively. The clinical decision curves showed an overall greater clinical benefit of the spatial radiomics model compared with the conventional radiomics model and spatial model.Conclusion:Spatial radiomics model containing spatial information on lesion location can improve the diagnostic efficacy of supratentorial PA and GG, and enhance the generalization of the prediction model.

Objective:To investigate the efficacy of a machine learning model based on radiomics of brain lesions on T 2WI in differentiating multiple sclerosis (MS) from neuromyelitis optica spectrum disorders (NMOSD). Methods:Totally 223 MS and NMOSD patients who were treated from January 2009 to September 2018 in Beijing Tiantan Hospital Affiliated to Capital Medical University, Donghu Branch of the First Affiliated Hospital of Nanchang University, Tianjin Medical University General Hospital, and Xuanwu Hospital of Capital Medical University were analyzed retrospectively, and according to the proportion of 7∶3, 223 patients were completely randomly divided into training set (156 cases) and test set (67 cases). A total of 74 patients with MS and NMOSD who were treated in Huashan Hospital Affiliated to Fudan University and China-Japan Friendship Hospital of Jilin University from January 2009 to September 2018 and in Xianghu Branch of the First Affiliated Hospital of Nanchang University from March 2020 to September 2021 were collected as an independent external validation set. All patients underwent brain cross-sectional MR T 2WI, radiomics features were extracted from T 2WI, and features were selected by max-relevance and min-redundancy and least absolute shrinkage and selection operator algorithms. Then various machine learning classifier models (logistic regression, decision tree, AdaBoost, random forest or support vector machine) were constructed to differentiate MS from NMOSD. The area under curve (AUC) of receiver operating characteristics was used to evaluate the performance of each classifier model in the training set, test set and external validation set. Results:Based on multi-center T 2WI, a total of 11 radiomics features related to the discrimination between MS and NMOSD were extracted and classifier models were constructed. Among them, the random forest model had the best efficiency in distinguishing MS from NMOSD, and its AUC values for distinguishing MS from NMOSD in the training set, test set and external validation set were 1.000, 0.944 and 0.902, with specificity of 100%, 76.9% and 86.0%, and sensitivity of 100%, 92.1% and 79.7%, respectively. Conclusion:The random forest model based on the radiomic features of T 2WI of brain lesions can effectively distinguish MS from NMOSD.