Diffuse low-grade glioma (DLGG) is a well-differentiated, slow-growing tumour with an inherent tendency to progress to high-grade glioma. The potential roles of genetic alterations in DLGG development have not yet been fully delineated. Therefore, the current study performed an integrated gene expression meta-analysis of eight independent, publicly available microarray datasets including 291 DLGGs and 83 non-glioma (NG) samples to identify gene expression signatures associated with DLGG. Using INMEX, 708 differentially expressed genes (DEGs) (385 upregulated and 323 downregulated genes) were identified in DLGG compared to NG. Furthermore, 497 DEGs (222 upregulated and 275 downregulated genes) corresponding to two histological types were identified. Of these, high expression of HIP1R significantly correlated with increased overall survival, whereas high expression of TBXAS1 significantly correlated with decreased overall survival. Additionally, network-based meta-analysis identified FN1 and APP as the key hub genes in DLGG compared with NG. PTPN6 and CUL3 were the key hub genes identified in the astrocytoma relative to the oligodendroglioma. Further immunohistochemical validation revealed that MTHFD2 and SPARC were positively expressed in DLGG, whereas RBP4 was positively expressed in NG. These findings reveal potential molecular biomarkers for diagnosis and therapy in patients with DLGG and provide a rich and novel candidate reservoir for future studies.
Graphene oxide is well known as a new kind of functional materials because of its super-high specie surface area, mechanical strength, as well as excellent amphipathicity. In this article, graphene oxide was further sulfonated via substitution reaction with diazo salt of sulfanilic in order to endow graphene oxide with better ion exchange capacity and proton conductivity. The microstructure and morphology of the obtained sulfonated graphene oxide were characterized by X-ray diffraction (XRD), Raman spectra, and TEM, while the sulfonation of graphene oxide was confirmed by energydispersive X-ray (EDX) and Fourier transform infrared spectroscopy (FTIR) spectra. As expected, ion exchange capacity and proton conductivity of sulfonated graphene oxide increased by about 0.589 and 33.6 times than those of graphene oxide, respectively.
Purpose To construct multivariate radiomics models using hybrid 18 F-FDG PET/MRI for distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA).Methods Ninety patients (60 with PD and 30 with MSA) were randomised to training and validation sets in a 7:3 ratio. All patients underwent 18 F-Fluorodeoxyglucose ( 18 F-FDG) PET/MRI to simultaneously obtain metabolic images ( 18 F-FDG), structural MRI images (T1-weighted imaging [T1WI], T2-weighted imaging [T2WI] and T2-weighted uid-attenuated inversion recovery [T2/ air]) and functional MRI images (susceptibilityweighted imaging [SWI] and apparent diffusion coe cient). Using PET and ve MRI sequences, we extracted 1172 radiomics features from the putamina and caudate nuclei. The radiomics signatures were constructed with the least absolute shrinkage and selection operator algorithm in the training set, with progressive optimization through single-sequence and double-sequence radiomics models. Multivariable logistic regression analysis was used to develop a clinical-radiomics model, combining the optimal multi-sequence radiomics signature with clinical characteristics and SUV values. The diagnostic performance of the models was assessed by receiver operating characteristic and decision curve analysis (DCA).Results The radiomics signatures showed favourable diagnostic e cacy. The optimal model comprised structural (T1WI), functional (SWI), and metabolic ( 18 F-FDG) sequences (Radscore FDG_T1WI_SWI ) with the area under curves (AUCs) of the training and validation sets of 0.971 and 0.957, respectively. The integrated model, incorporating Radscore FDG_T1WI_SWI , three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUV max , demonstrated satisfactory calibration and discrimination in the training and validation sets (0.993 and 0.994, respectively). DCA indicated the highest clinical bene t of the clinical-radiomics integrated model. ConclusionsThe radiomics signature with metabolic, structural, and functional information provided by hybrid 18 F-FDG PET/MRI may achieve promising diagnostic e cacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best.
We aimed to investigate the role of the quantitative parameters of dual-energy computed tomography (DECT) in evaluating patients with hepatocellular carcinoma (HCC) treated by transarterial chemoembolization (TACE). We retrospectively identified 80 HCC patients (mean age, 56 years; 61 men) treated by TACE who received contrast-enhanced DECT and were retreated by TACE within 7 days between November 2018 and December 2019. Taking digital subtraction angiography (DSA) and CT images as reference standard, two readers measured and calculated the values of normalized iodine concentration at arterial phase (NICAP), normalized iodine concentration at portal venous phase (NICPP), iodine concentration difference (ICD), arterial iodine fraction (AIF) and slope of the spectral Hounsfield unit curve (λHu) by placing matched regions of interests (ROIs) within the tumor active area (TAA), adjacent normal hepatic parenchyma (ANHP) and tumor necrotic area (TNA). Differences between the parameters were analyzed by the Kruskal–Wallis H test. Receiver operating characteristic analysis of the parameters performance in differentiating the three tissues types was performed. AIF exhibited a good performance in distinguishing TAA (0.93 ± 0.31) and ANHP (0.18 ± 0.14), the areas under the receiver operating characteristic curve (AUC) was 0.989, while the λHu exhibited an excellent performance in distinguishing TAA (3.32 ± 1.24) and TNA (0.29 ± 0.27), with an AUC of 1.000. In conclusion, quantitative DECT can be effectively used to evaluate the tumor viability in HCC patients treated by TACE.
Background: Quantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation usually registered subjects' images onto an atlas template (defined as RSIAT here) for group analysis, which changed the individuals' images and probably affected regional PET segmentation. In contrast, we could register atlas template to subjects' images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD), and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference. Results: The DC of RATSI increased, and the HD decreased significantly (P < 0.05) compared with the RSIAT in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUV mean and SUV max among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUV mean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P < 0.05), which is consistent with previous reports. Conclusion: The RATSI was more accurate for the caudate nucleus and putamen automatic segmentation and can be used for regional PET analysis in hybrid PET/MR.
This study investigated the accuracy of MRI features in differentiating the pathological grades of pancreatic neuroendocrine neoplasms (PNENs). A total of 31 PNENs patients were retrospectively evaluated, including 19 cases in grade 1, 5 in grade 2, and 7 in grade 3. Plain and contrastenhanced MRI was performed on all patients. MRI features including tumor size, margin, signal intensity, enhancement patterns, degenerative changes, duct dilatation and metastasis were analyzed. Chi square tests, Fisher's exact tests, one-way ANOVA and ROC analysis were conducted to assess the associations between MRI features and different tumor grades. It was found that patients with older age, tumors with higher TNM stage and without hormonal syndrome had higher grade of PNETs (all P<0.05). Tumor size, shape, margin and growth pattern, tumor pattern, pancreatic and bile duct dilatation and presence of lymphatic and distant metastasis as well as MR enhancement pattern and tumor-topancreas contrast during arterial phase were the key features differentiating tumors of all grades (all P<0.05). ROC analysis revealed that the tumor size with threshold of 2.8 cm, irregular shape, pancreatic duct dilatation and lymphadenopathy showed satisfactory sensitivity and specificity in distinguishing grade 3 from grade 1 and grade 2 tumors. Features of peripancreatic tissue or vascular invasion, and distant metastasis showed high specificity but relatively low sensitivity. In conclusion, larger size, poorlydefined margin, heterogeneous enhanced pattern during arterial phase, duct dilatation and the presence of metastases are common features of higher grade PNENs. Plain and contrast-enhanced MRI provides the ability to differentiate tumors with different pathological grades.
Background:Quantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation usually registered subjects’ images onto an atlas template (defined as RSIAT here) for group analysis, which changed the individuals’ images and probably affected regional PET segmentation. In contrast, we could register atlas template to subjects’ images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD). and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference. Results:The DC of RATSI increased and the HD decreased significantly (P < 0.05) compared with the RSIAT in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUVmean and SUVmax among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUVmean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P<0.05), which is consistent with previous reports. Conclusion:The RATSI was more accurate for the caudate nucleus and putamen automatic segmentation, and can be used for regional PET analysis in hybrid PET/MR.
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