Histogram Analysis Based on Neurite Orientation Dispersion and Density MR Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements.

BACKGROUND: Preoperative differentiation of glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) contributes to guide neurosurgical decision-making. PURPOSE: To explore the value of histogram analysis based on neurite orientation dispersion and density imaging (NODDI) in differentiating between GBM and SBM and comparison of the diagnostic performance of two region of interest (ROI) placements. STUDY TYPE: Retrospective. POPULATION: In all, 109 patients with GBM (n = 57) or SBM (n = 52) were enrolled. FIELD STRENGTH/SEQUENCE: A 3.0 T scanners. T2 -dark-fluid sequence, contrast-enhanced T1 magnetization-prepared rapid gradient echo sequence, and NODDI. ASSESSMENT: ROIs were placed on the peritumoral edema area (ROI1) and whole tumor area (ROI2, included the cystic, necrotic, and hemorrhagic areas). Histogram parameters of each isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and orientation dispersion index (ODI) from NODDI images for two ROIs were calculated, respectively. STATISTICAL TESTS: Mann-Whitney U test, independent t-test, chi-square test, multivariate logistic regression analysis, DeLong's test. RESULTS: For the ROI1 and ROI2, the ICVFmin and ODImean obtained the highest area under curve (AUC, AUC = 0.741 and 0.750, respectively) compared to other single parameters, and the AUC of the multivariate logistic regression model was 0.851 and 0.942, respectively. DeLong's test revealed significant difference in diagnostic performance between optimal single parameter and multivariate logistic regression model within the same ROI, and the multivariate logistic regression models between two different ROIs. DATA CONCLUSION: The performance of multivariate logistic regression model is superior to optimal single parameter in both ROIs based on NODDI histogram analysis to distinguish SBM from GBM, and the ROI placed on the whole tumor area exhibited better diagnostic performance. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Whole-Tumor Histogram Analysis of Multiple Diffusion Metrics for Glioma Genotyping.

Background The isocitrate dehydrogenase (IDH) genotype and 1p/19q codeletion status are key molecular markers included in glioma pathologic diagnosis. Advanced diffusion models provide additional microstructural information. Purpose To compare the diagnostic performance of histogram features of multiple diffusion metrics in predicting glioma IDH and 1p/19q genotyping. Materials and Methods In this prospective study, participants were enrolled from December 2018 to December 2020. Diffusion-weighted imaging was performed by using a spin-echo echo-planar imaging sequence with five b values (500, 1000, 1500, 2000, and 2500 sec/mm2) in 30 directions for every b value and one b value of 0. Diffusion metrics of diffusion-tensor imaging (DTI), diffusion-kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) were calculated, and their histogram features were analyzed in regions that included the entire tumor and peritumoral edema. Comparisons between groups were performed according to IDH genotype and 1p/19q codeletion status. Logistic regression analysis was used to predict the IDH and 1p/19q genotypes. Results A total of 215 participants (115 men, 100 women; mean age, 48 years ± 13 [standard deviation]) with grade II (n = 68), grade III (n = 35), and grade IV (n = 112) glioma were included. Among the DTI, DKI, NODDI, MAP, and total diffusion models, there were no significant differences in the areas under the receiver operating characteristic curve (AUCs) for predicting IDH mutations (AUC, 0.76, 0.82, 0.78, 0.81, and 0.82, respectively; P > .05) and 1p/19q codeletion in gliomas with IDH mutations (AUC, 0.83, 0.81, 0.82, 0.83, and 0.88, respectively; P > .05). A regression model with an R2 value of 0.84 was used for the Ki-67 labeling index and histogram features of the diffusion metrics. Conclusion Whole-tumor histogram analysis of multiple diffusion metrics is a promising approach for glioma isocitrate dehydrogenase and 1p/19q genotyping, and the performance of diffusion-tensor imaging is similar to that of advanced diffusion models. Clinical trial registration no. ChiCTR2100048119 © RSNA, 2021 Online supplemental material is available for this article. An earlier incorrect version appeared online. This article was corrected on December 14, 2021.

FGF18 Enhances Migration and the Epithelial-Mesenchymal Transition in Breast Cancer by Regulating Akt/GSK3ß/Β-Catenin Signaling.

BACKGROUND/AIMS: Fibroblast growth factors (FGFs) and their high-affinity receptors contribute to autocrine and paracrine growth stimulation in several human malignant tumors, including breast cancer. However, the mechanisms underlying the carcinogenic actions of FGF18 remain unclear. METHODS: The transcription level of FGF18 under the hypoxic condition was detected with quantitative PCR (qPCR). A wound-healing assay was performed to assess the role of FGF18 in cell migration. A clonogenicity assay was used to determine whether FGF18 silencing affected cell clonogenicity. Western blotting was performed to investigate Akt/GSK3ß/ß-catenin pathway protein expression. Binding of ß-catenin to the target gene promoter was determined by chromatin immunoprecipitation (ChIP) assays. RESULTS: FGF18 promoted the epithelial-mesenchymal transition (EMT) and migration in breast cancer cells through activation of the Akt/GSK3ß/ß-catenin pathway. FGF18 increased Akt-Ser473 and -Thr308 phosphorylation, as well as that of GSK3ß-Ser9. FGF18 also enhanced the transcription of proliferation-related genes (CDK2, CCND2, Ki67), metastasis-related genes (TGF-ß, MMP-2, MMP-9), and EMT markers (Snail-1, Snail-2, N-cadherin, vimentin, TIMP1). ß-catenin bound to the target gene promoter on the ChIP assay. CONCLUSION: FGF18 contributes to the migration and EMT of breast cancer cells following activation of the Akt/GSK3ß/ß-catenin pathway. FGF18 expression may be a potential prognostic therapeutic marker for breast cancer.

Radiomics Analysis of Diffusion Kurtosis Imaging: Distinguishing Between Glioblastoma and Single Brain Metastasis.

RATIONALE AND OBJECTIVES: This study aimed to assess the value of diffusion kurtosis imaging (DKI)-based radiomics models in differentiating glioblastoma (GB) from single brain metastasis (SBM) and compare their diagnostic performance with that of routine magnetic resonance imaging (MRI) models. MATERIALS AND METHODS: A total of 110 patients who underwent DKI and were pathologically diagnosed with GB (n = 58) or SBM (n = 52) were enrolled in this study. Radiomics features were extracted from the manually delineated region of interest of the lesion. A training set for model development was constructed from the images of 88 random patients, and 22 patients were reserved for independent validation. Seven single-DKI-parametric models and a multi-DKI-parametric model were constructed using six classifiers, whereas four single-routine-sequence models (based on T2 weighted imaging, apparent diffusion coefficient, T2-dark-fluid, and contrast-enhanced T1 magnetization prepared rapid gradient echo) and a multisequence routine MRI model were constructed for comparison. Receiver operating characteristic curve analysis was conducted to assess the diagnostic performance. The areas under the curve (AUCs) of different models were compared using the DeLong test. RESULTS: The AUCs of the single-DKI-parametric models ranged from 0.800 to 0.933 (mean kurtosis [MK] model). The multi-DKI-parametric model had a slightly higher AUC (0.958) than the MK model; however, the difference was not statistically significant (P = 0.688). In comparison, the AUCs of the routine MRI models ranged from 0.633 to 0.733 (multisequence routine MRI model). The AUC of the multi-DKI-parametric model was significantly higher than that of the multisequence routine MRI model (P = 0.042). CONCLUSION: The multi-DKI-parametric radiomics model exhibited better performance than that of the single-DKI-parametric models and routine MRI models in distinguishing GB from SBM.

Unlocking potential of oxytocin: improving intracranial lymphatic drainage for Alzheimer's disease treatment.

Background: The impediment to ß-amyloid (Aß) clearance caused by the invalid intracranial lymphatic drainage in Alzheimer's disease is pivotal to its pathogenesis, and finding reliable clinical available solutions to address this challenge remains elusive. Methods: The potential role and underlying mechanisms of intranasal oxytocin administration, an approved clinical intervention, in improving intracranial lymphatic drainage in middle-old-aged APP/PS1 mice were investigated by live mouse imaging, ASL/CEST-MRI scanning, in vivo two-photon imaging, immunofluorescence staining, ELISA, RT-qPCR, Western blotting, RNA-seq analysis, and cognitive behavioral tests. Results: Benefiting from multifaceted modulation of cerebral hemodynamics, aquaporin-4 polarization, meningeal lymphangiogenesis and transcriptional profiles, oxytocin administration normalized the structure and function of both the glymphatic and meningeal lymphatic systems severely impaired in middle-old-aged APP/PS1 mice. Consequently, this intervention facilitated the efficient drainage of Aß from the brain parenchyma to the cerebrospinal fluid and then to the deep cervical lymph nodes for efficient clearance, as well as improvements in cognitive deficits. Conclusion: This work broadens the underlying neuroprotective mechanisms and clinical applications of oxytocin medication, showcasing its promising therapeutic prospects in central nervous system diseases with intracranial lymphatic dysfunction.

TESC overexpression mitigates amyloid-ß-induced hippocampal atrophy and memory decline.

BACKGROUND AND OBJECTIVES: Genome-wide association studies (GWASs) have identified numerous candidate genes for human brain-imaging phenotypes; however, the biological relevance of many of these genes remains unconfirmed. This study aimed to investigate the causal relationships among tescalcin (TESC) (a GWAS-indicated gene), hippocampal volume, Alzheimer's disease (AD), and the underlying biological mechanisms. METHODS: Human transcriptional data were analyzed to confirm relative TESC expression in the hippocampus. In cell experiments, RNA-seq analysis was used to identify the potential biological pathways for TESC overexpression, and immunofluorescence imaging and cell viability assays were used to evaluate the effect of TESC overexpression on neuronal structure and survival. In animal experiments, the effects of TESC overexpression on hippocampal volume and cognitive function in normal mice and amyloid-ß (Aß)-induced AD mice were investigated by 9.4 T magnetic resonance imaging and behavioral tests. Underlying mechanisms were further assessed via western blotting and electrophysiological recordings. RESULTS: Human transcriptional data demonstrated that TESC is primarily expressed in the hippocampus and neurons. TESC overexpression enhanced the viability of HT22 cells and reduced Aß-induced cell death. In mouse models, Tesc-overexpressing mice revealed increased hippocampal volume, likely owing to enhanced cell viability and long-term potentiation (LTP), and reducing apoptotic- and oxidation-induced hippocampal damage. TESC overexpression could significantly mitigate Aß-induced hippocampal atrophy and memory impairment, potentially by reducing Aß-induced neuronal apoptosis and LTP weakening. CONCLUSION: This study exemplifies the translation of GWAS findings into actionable biological knowledge and suggests that upregulation of TESC may offer a promising therapeutic strategy for AD.

Value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging in differentiating parotid gland tumors.

PURPOSE: This study aimed to explore the value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging (RESOLVE-DWI) for the differential diagnosis of parotid gland tumors. METHODS: A total of 128 patients with histopathologically confirmed parotid gland tumors [86 benign tumors (BTs) and 42 malignant tumors (MTs)] were retrospectively recruited. BTs were further divided into pleomorphic adenomas (PAs, n = 57) and Warthin's tumors (WTs, n = 15). MRI examinations were performed before and after contrast injection to measure the longitudinal relaxation time (T1) value (T1p and T1e, respectively) and the apparent diffusion coefficient (ADC) value of the parotid gland tumors. The reduction in T1 (T1d) values and the percentage of T1 reduction (T1d%) were calculated. RESULTS: The T1d and ADC values of the BTs were considerably higher than those of the MTs (all P <.05). The area under the curve (AUC) of the T1d and ADC values for differentiating between BTs and MTs of the parotid was 0.618 and 0.804, respectively (all P <.05). The AUC of the T1p, T1d, T1d%, and ADC values for differentiating between PAs and WTs was 0.926, 0.945, 0.925, and 0.996, respectively (all P >.05). The ADC and T1d% + ADC values performed better in differentiating between PAs and MTs than the T1p, T1d, and T1d% (AUC values: 0.902, 0.909, 0.660, 0.726, and 0.736, respectively). The T1p, T1d, T1d%, and T1d% + T1p values all had high diagnosis efficacy in differentiating WTs from MTs (AUC values: 0.865, 0.890, 0.852, and 0.897, respectively, all P >.05). CONCLUSION: T1 mapping and RESOLVE-DWI can be used to differentiate parotid gland tumors quantitatively and can be complementary to each other.

Multiple diffusion metrics in differentiating solid glioma from brain inflammation.

Background and purpose: The differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models. Materials and methods: Participants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm2) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated. Results: 57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758). Conclusion: Multiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.

Quantitative analysis of mean apparent propagator-magnetic resonance imaging for distinguishing glioblastoma from solitary brain metastasis.

PURPOSE: Distinguishing glioblastoma (GBM) and solitary brain metastasis (SBM) is vital for determining the optimal treatment. GBM and SBM present similar imaging characteristics on conventional magnetic resonance imaging (MRI). The aim of this study was to evaluate the efficacy of quantitative analysis of mean apparent propagator (MAP)-MRI for distinguishing GBM and SBM. METHOD: Eighty-nine patients were enrolled. Regions of interest (ROIs), including the enhancing area (EA), peritumoural high signal intensity area (PHA), and maximum abnormal signal area (MASA), were manually delineated. The following MAP parameters for each region were measured: mean square displacement (MSD), non-Gaussianity (NG), NG axial (NGAx), NG vertical, Q-space inverse variance, return to origin probability (RTOP), return to axis probability (RTAP), and return to plane probability (RTPP). Normalised MAP values from each region were compared between the GBM and SBM groups, and their diagnostic efficiency was assessed. Multivariate logistic regression analysis was used to create the most accurate model. RESULTS: Compared with the SBM group, the MSD was significantly lower in the GBM group, whereas the RTAP, RTOP, and RTPP were significantly higher in each region, except for RTAPPHA (all P < 0.05). RTPPPHA, MSDEA, and RTPPMASA showed the most significant differences (all P < 0.01). The best logistic regression model combined RTPPPHA, MSDEA, and NGAxMASA (area under the curve, 0.840). CONCLUSIONS: Quantitative analysis of MAP-MRI is useful for distinguishing GBM from SBM. Multivariate analysis combined with multiple ROIs can improve diagnostic performance.

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram.

Objectives: We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs). Materials and Methods: This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong's test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models. Results: The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967). Conclusions: Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.

TCF4/ß-catenin complex is directly upstream of FGF21 in mouse stomach cancer cells.

Fibroblast growth factor 21 (FGF21) as a member of the FGFs serves a key role in glucose homeostasis and protection of the liver, heart, kidney and skin from damage as well as cancer cell development. In addition, transcription of FGF21 is sensitive to diverse damages; however, the role of the transcriptional regulator of FGF21 in cancer cells remains to be elucidated. FGFs were identified to have dominant expression in cancer cells; therefore, mouse forestomach carcinoma (MFC) cells were used in the present study, which is a mouse stomach cancer cell strain for identifying the FGF21 regulators. In promoter analysis of FGF21, the putative transcription factor 4 (TCF4) binding motifs (T/AC/GAAAG) were observed within 1.5 kb of the promoter region. Further chromatin immunoprecipitation and yeast-one hybrid assays identified that TCF4 directly bound to one of the two putative binding motifs observed. A co-immunoprecipitation assay identified that ß-catenin interacts with TCF4 in MFC cells, and the ß-catenin/TCF4 complex bound to the promoter of FGF21. In order to examine the function of TCF4 and ß-catenin in transcriptional regulation of FGF21, TCF4 and ß-catenin was transiently expressed in MFC cells. Reverse transcription-quantitative polymerase chain reaction results revealed that overexpression of TCF4 and ß-catenin activated FGF21 transcription. Besides, suppression of ß-catenin via a specific short interfering RNA resulted in reduction of FGF21 expression. Together these findings suggest that the ß-catenin/TCF complex directly activates FGF21 via promoter binding. The observations of the present study may help elucidate the regulatory mechanism of FGF21, which is a key pharmaceutical protein.

Sonic hedgehog-c-Jun N-terminal kinase-zinc finger protein Gli1 signaling protects against high glucose concentration-induced reactive oxygen species generation in human fibroblasts.

Diabetes mellitus (DM) complications affect patients and cause varying damage. Skin ulcers exhibit difficulties in wound healing, and the regulatory basis for this remains unclear. High glucose concentration (HG) was utilized to mimic DM in cultured cells. Reverse transcription-quantitative polymerase chain reaction, western blotting and fluorescence dye analyses were performed to analyze the effects of hedgehog signaling in regulation of HG or diabetes in fibroblasts. HG-stress suppressed hedgehog-signaling gene expression, whereas the apoptosis and inflammatory response markers, Caspase-3 and plasminogen activator inhibitor-1 (PAI1), respectively, were induced. In addition, HG-stress inhibited the fibroblast proliferation rate. In parallel, treatment with Sonic hedgehog (Shh), an activator of hedgehog signaling, together with HG eliminated effects of HG on expression of hedgehog-signaling genes, Caspase-3 and PAI1, and rescued the cell proliferation rate in fibroblasts. In addition, Shh application activated c-Jun N-terminal kinase (JNK), which was inhibited by HG stress. sp600125, a JNK specific inhibitor, treatment inhibited the effect of Shh on fibroblast proliferation and hedgehog-signaling marker gene expression. Furthermore, zinc finger protein Gli1 (Gli1) overexpression partially eliminated the effect of HG and sp600125 on fibroblast proliferation, and reduced HG-induced ROS generation in fibroblasts. Together, these results indicate that HG stress inhibits hedgehog signaling, and Shh-JNK-Gli1 pathway positively regulates HG-induced damage on fibroblasts.

Pigment epithelium derived factor play a positive role in bone mineralization of osteoblasts derived from diabetic patients.

Pigment epithelium-derived factor (PEDF) is a multifunctional secreted protein which plays important role in anti-angiogenic, anti-tumorigenic, as well as involves in the metabolism and regeneration of bone. In this study, our aim is to investigate the role of PEDF in regulating mineralization of osteoblasts from diabetic patients (DP). We isolated and cultured osteoblasts derived from DP and non-diabetic patients (NDP), in order to analyze the variable differences via gene expression and calcification assay in vitro. Gene expression analysis and alizarin red S staining revealed that osteoblasts from DP exhibited defective mineralization. PEDF and vascular endothelial growth factor (VEGF) levels were lower in osteoblasts from DP than those from NDP. Interestingly, exogenous PEDF could upregulate the gene expression levels of VEGF and osteoblast-related genes, further to restore mineralization ability in osteoblasts from DP. Our results demonstrated that PEDF played a positive role in maintaining bone development in diabetic osteoblasts, therefore, we confidently believe that PEDF may be a promising cytokine to consider in development of treatments for diabetic bone diseases.