α-Subunit Tyrosine Phosphorylation Is Required for Activation of the Large Conductance Ca2+-Activated Potassium Channel in the Rabbit Sphincter of Oddi.

Large conductance Ca2+-activated potassium (BKCa) channels are regulated by intracellular free Ca2+ concentrations ([Ca2+]i) and channel protein phosphorylation. In hypercholesterolemia (HC), motility impairment of the sphincter of Oddi (SO) is associated with abnormal [Ca2+]i accumulation in smooth muscle cells of the rabbit SO (RSOSMCs), which is closely related to BKCa channel activity. However, the underlying mechanisms regulating channel activity remain unclear. In this study, an HC rabbit model was generated and used to investigate BKCa channel activity of RSOSMCs via SO muscle tone measurement in vitro and manometry in vivo, electrophysiological recording, intracellular calcium measurement, and Western blot analyses. BKCa channel activity was decreased, which correlated with [Ca2+]i overload and reduced tyrosine phosphorylation of the BKCa α-subunit in the HC group. The abnormal [Ca2+]i accumulation and decreased BKCa channel activity were partially restored by Na3VO4 pretreatment but worsened by genistein in RSOSMCs in the HC group. This study suggests that α-subunit tyrosine phosphorylation is required for [Ca2+]i to activate BKCa channels, and there is a negative feedback between the BKCa channel and the L-type voltage-dependent Ca2+ channel that regulates [Ca2+]i. This study provides direct evidence that tyrosine phosphorylation of BKCa α-subunits is required for [Ca2+]i to activate BKCa channels in RSOSMCs, which may be the underlying physiological and pathologic mechanism regulating the activity of BKCa channels in SO cells.

Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging.

BACKGROUND: Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate the diagnostic performance of machine learning using radiomics modelfrom T1-weighted contrast enhanced imaging(T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM. METHODS: Seventy-sevenGBM patients, including 51 with true progression and 26 with pseudoprogression,who underwent standard treatment and T1CE, were retrospectively enrolled.Clinical information, including sex, age, KPS score, resection extent, neurological deficit and mean radiation dose, were also recorded collected for each patient. The whole tumor enhancementwas manually drawn on the T1CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity. RESULTS: No clinical features showed statistically significant differences between true progression and pseudoprogression.The radiomic classifier demonstrated ACC, sensitivity, and specificity of 72.78%(95% confidence interval [CI]: 0.45,0.91), 78.36%(95%CI: 0.56,1.00) and 61.33%(95%CI: 0.20,0.82).The accuracy, sensitivity and specificity of three radiologists' assessment were66.23%(95% CI: 0.55,0.76), 61.50%(95% CI: 0.43,0.78) and 68.62%(95% CI: 0.55,0.80); 55.84%(95% CI: 0.45,0.66),69.25%(95% CI: 0.50,0.84) and 49.13%(95% CI: 0.36,0.62); 55.84%(95% CI: 0.45,0.66), 69.23%(95% CI: 0.50,0.84) and 47.06%(95% CI: 0.34,0.61), respectively. CONCLUSION: T1CE-based radiomics showed better classification performance compared with radiologists' assessment.The radiomics modelwas promising in differentiating pseudoprogression from true progression.

Optimizing Texture Retrieving Model for Multimodal MR Image-Based Support Vector Machine for Classifying Glioma.

BACKGROUND: Accurate glioma grading plays an important role in patient treatment. PURPOSE: To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with support vector machine (SVM). STUDY TYPE: Retrospective. POPULATION: In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007. FIELD STRENGTH/SEQUENCE: 3.0T MRI/ T1 WI, T2 fluid-attenuated inversion recovery, contrast enhanced T1 , arterial spinal labeling, diffusion-weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm2 ), and dynamic contrast-enhanced. ASSESSMENT: Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size-zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF-SVM) combined with attribute selection using SVM-recursive feature elimination (SVM-RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10-fold crossvalidation. The model performance was further tested using the remaining 20% data. STATISTICAL TESTS: Ten-fold crossvalidation was used to validate the model performance. RESULTS: Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM-RFE was able to reduce attribute redundancy as well as improve RBF-SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray-level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM-RFE to provide comparable classifying efficacy. DATA CONCLUSION: When using image textures-based SVM classification of gliomas, the GLSZM model in combination with gray-level 64 and attribute selection may be an optimized solution. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1263-1274.

Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis.

BACKGROUND: The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma(GBM). Using pre-operative MRI techniques to predict MGMT promoter methylation status remains inconclusive. In this study, we investigated the value of features from structural and advanced imagings in predicting the methylation of MGMT promoter in primary glioblastoma patients. METHODS: Ninety-two pathologically confirmed primary glioblastoma patients underwent preoperative structural MR imagings and the efficacy of structural image features were qualitatively analyzed using Fisher's exact test. In addition, 77 of the 92 patients underwent additional advanced MRI scans including diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging. Apparent diffusion coefficient (ADC) and relative cerebral blood flow (rCBF) values within the manually drawn region-of-interest (ROI) were calculated and compared using independent sample t test for their efficacies in predicting MGMT promoter methylation. Receiver operating characteristic curve (ROC) analysis was used to investigate the predicting efficacy with the area under the curve (AUC) and cross validations. Multiple-variable logistic regression model was employed to evaluate the predicting performance of multiple variables. RESULTS: MGMT promoter methylation was associated with tumor location and necrosis (P <  0.05). Significantly increased ADC value (P <  0.001) and decreased rCBF (P <  0.001) were associated with MGMT promoter methylation in primary glioblastoma. The ADC achieved the better predicting efficacy than rCBF (ADC: AUC, 0.860; sensitivity, 81.1%; specificity, 82.5%; vs rCBF: AUC, 0.835; sensitivity, 75.0%; specificity, 78.4%; P = 0.032). The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914. CONCLUSION: ADC and rCBF are promising imaging biomarkers in clinical routine to predict the MGMT promoter methylation in primary glioblastoma patients.

Radiomics strategy for glioma grading using texture features from multiparametric MRI.

BACKGROUND: Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays. PURPOSE/HYPOTHESIS: To verify the superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps. STUDY TYPE: Retrospective; radiomics. POPULATION: A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively. FIELD STRENGTH/SEQUENCE: 3.0T MRI/T1 -weighted images before and after contrast-enhanced, T2 -weighted, multi-b-value diffusion-weighted and 3D arterial spin labeling images. ASSESSMENT: After multiparametric MRI preprocessing, high-throughput features were derived from patients' volumes of interests (VOIs). The support vector machine-based recursive feature elimination was adopted to find the optimal features for low-grade glioma (LGG) vs. high-grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency. STATISTICAL TESTS: Student's t-test or a chi-square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist. RESULTS: Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI. DATA CONCLUSION: Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision-making for patients with varied glioma grades. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1518-1528.

Structural and functional brain changes in perimenopausal women who are susceptible to migraine: a study protocol of multi-modal MRI trial.

BACKGROUND: As a common clinical symptom that often bothers midlife females, migraine is closely associated with perimenopause. Previous studies suggest that one of the most prominent triggers is the sudden decline of estrogen during perimenopausal period. Hormone replacement therapy (HRT) is widely used to prevent this suffering in perimenopausal women, but effective diagnostic system is lacked for quantifying the severity of the diseaase. To avoid the abuse and overuse of HRT, we propose to conduct a diagnostic trial using multimodal MRI techniques to quantify the severity of these perimenopausal migraineurs who are susceptible to the decline of estrogen. METHODS: Perimenopausal women suffering from migraine will be recruited from the pain clinic of our hospital. Perimenopausal women not suffering from any kind of headache will be recruited from the local community. Clinical assessment and multi-modal MR imaging examination will be conducted. A follow up will be conducted once half year within 3 years. Pain behavior, neuropsychology scores, fMRI analysis combined with suitable statistical software will be used to reveal the potential association between these above traits and the susceptibility of migraine. DISCUSSION: Multi-modal imaging features of both healthy controls and perimenopausal women who are susceptible to estrogen decline will be acquired. Imaging features will include volumetric characteristics, white matter integrity, functional characteristics, topological properties, and perfusion properties. Clinical information, such as basic information, blood estrogen level, information of migraine, and a bunch of neurological scale will also be used for statistic assessment. This clinical trial would help to build an effective screen system for quantifying the severity of illness of those susceptible women during the perimenopausal period. TRIAL REGISTRATION: This study has already been registered at Clinical Trials. gov (ID: NCT02820974 ). Registration date: September 28th, 2014.

Gender differences in functional connectivities between insular subdivisions and selective pain-related brain structures.

BACKGROUND: The incidence of pain disorders in women is higher than in men, making gender differences in pain a research focus. The human insular cortex is an important brain hub structure for pain processing and is divided into several subdivisions, serving different functions in pain perception. Here we aimed to examine the gender differences of the functional connectivities (FCs) between the twelve insular subdivisions and selected pain-related brain structures in healthy adults. METHODS: Twenty-six healthy males and 11 age-matched healthy females were recruited in this cross-sectional study. FCs between the 12 insular subdivisions (as 12 regions of interest (ROIs)) and the whole brain (ROI-whole brain level) or 64 selected pain-related brain regions (64 ROIs, ROI-ROI level) were measured between the males and females. RESULTS: Significant gender differences in the FCs of the insular subdivisions were revealed: (1) The FCs between the dorsal dysgranular insula (dId) and other brain regions were significantly increased in males using two different techniques (ROI-whole brain and ROI-ROI analyses); (2) Based on the ROI-whole brain analysis, the FC increases in 4 FC-pairs were observed in males, including the left dId - the right median cingulate and paracingulate/ right posterior cingulate gyrus/ right precuneus, the left dId - the right median cingulate and paracingulate, the left dId - the left angular as well as the left dId - the left middle frontal gyrus; (3) According to the ROI-ROI analysis, increased FC between the left dId and the right rostral anterior cingulate cortex was investigated in males. CONCLUSION: In summary, the gender differences in the FCs of the insular subdivisions with pain-related brain regions were revealed in the current study, offering neuroimaging evidence for gender differences in pain processing. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02820974 . Registered 28 June 2016.

Combination of IVIM-DWI and 3D-ASL for differentiating true progression from pseudoprogression of Glioblastoma multiforme after concurrent chemoradiotherapy: study protocol of a prospective diagnostic trial.

BACKGROUND: Standard therapy for Glioblastoma multiforme (GBM) involves maximal safe tumor resection followed with radiotherapy and concurrent adjuvant temozolomide. About 20 to 30% patients undergoing their first post-radiation MRI show increased contrast enhancement which eventually recovers without any new treatment. This phenomenon is referred to as pseudoprogression. Differentiating tumor progression from pseudoprogression is critical for determining tumor treatment, yet this capacity remains a challenge for conventional magnetic resonance imaging (MRI). Thus, a prospective diagnostic trial has been established that utilizes multimodal MRI techniques to detect tumor progression at its early stage. The purpose of this trial is to explore the potential role of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and three-dimensional arterial spin labeling imaging (3D-ASL) in differentiating true progression from pseudoprogression of GBM. In addition, the diagnostic performance of quantitative parameters obtained from IVIM-DWI and 3D-ASL, including apparent diffusion coefficient (ADC), slow diffusion coefficient (D), fast diffusion coefficient (D*), perfusion fraction (f), and cerebral blood flow (CBF), will be evaluated. METHODS: Patients that recently received a histopathological diagnosis of GBM at our hospital are eligible for enrollment. The patients selected will receive standard concurrent chemoradiotherapy and adjuvant temozolomide after surgery, and then will undergo conventional MRI, IVIM-DWI, 3D-ASL, and contrast-enhanced MRI. The quantitative parameters, ADC, D, D*, f, and CBF, will be estimated for newly developed enhanced lesions. Further comparisons will be made with unpaired t-tests to evaluate parameter performance in differentiating true progression from pseudoprogression, while receiver-operating characteristic (ROC) analyses will determine the optimal thresholds, as well as sensitivity and specificity. Finally, relationships between these parameters will be assessed with Pearson's correlation and partial correlation analyses. DISCUSSION: The results of this study may demonstrate the potential value of using multimodal MRI techniques to differentiate true progression from pseudoprogression in its early stages to help decision making in early intervention and improve the prognosis of GBM. TRIAL REGISTRATION: This study has been registered at ClinicalTrials.gov ( NCT02622620 ) on November 18, 2015 and published on March 28, 2016.

Multimodal MRI for early diabetic mild cognitive impairment: study protocol of a prospective diagnostic trial.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a risk factor for dementia. Mild cognitive impairment (MCI), an intermediary state between normal cognition and dementia, often occurs during the prodromal diabetic stage, making early diagnosis and intervention of MCI very important. Latest neuroimaging techniques revealed some underlying microstructure alterations for diabetic MCI, from certain aspects. But there still lacks an integrated multimodal MRI system to detect early neuroimaging changes in diabetic MCI patients. Thus, we intended to conduct a diagnostic trial using multimodal MRI techniques to detect early diabetic MCI that is determined by the Montreal Cognitive Assessment (MoCA). METHODS: In this study, healthy controls, prodromal diabetes and diabetes subjects (53 subjects/group) aged 40-60 years will be recruited from the physical examination center of Tangdu Hospital. The neuroimaging and psychometric measurements will be repeated at a 0.5 year-interval for 2.5 years' follow-up. The primary outcome measures are 1) Microstructural and functional alterations revealed with multimodal MRI scans including structure magnetic resonance imaging (sMRI), resting state functional magnetic resonance imaging (rs-fMRI), diffusion kurtosis imaging (DKI), and three-dimensional pseudo-continuous arterial spin labeling (3D-pCASL); 2) Cognition evaluation with MoCA. The second outcome measures are obesity, metabolic characteristics, lifestyle and quality of life. DISCUSSION: The study will provide evidence for the potential use of multimodal MRI techniques with psychometric evaluation in diagnosing MCI at prodromal diabetic stage so as to help decision making in early intervention and improve the prognosis of T2DM. TRIAL REGISTRATION: This study has been registered to ClinicalTrials.gov ( NCT02420470 ) on April 2, 2015 and published on July 29, 2015.

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.

PURPOSE: This study aimed to establish and compare the radiomics machine learning (ML) models based on non-contrast enhanced computed tomography (NECT) and clinical features for predicting the simplified risk categorization of thymic epithelial tumors (TETs). EXPERIMENTAL DESIGN: A total of 509 patients with pathologically confirmed TETs from January 2009 to May 2018 were retrospectively enrolled, consisting of 238 low-risk thymoma (LRT), 232 high-risk thymoma (HRT), and 39 thymic carcinoma (TC), and were divided into training (n = 433) and testing cohorts (n = 76) according to the admission time. Volumes of interest (VOIs) covering the whole tumor were manually segmented on preoperative NECT images. A total of 1218 radiomic features were extracted from the VOIs, and 4 clinical variables were collected from the hospital database. Fourteen ML models, along with varied feature selection strategies, were used to establish triple-classification models using the radiomic features (radiomic models), while clinical-radiomic models were built after combining with the clinical variables. The diagnostic accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of radiologist assessment, the radiomic and clinical-radiomic models were evaluated on the testing cohort. RESULTS: The Support Vector Machine (SVM) clinical-radiomic model demonstrated the highest AUC of 0.841 (95% CI 0.820 to 0.861) on the cross-validation result and reached an AUC of 0.844 (95% CI 0.793 to 0.894) in the testing cohort. For the one-vs-rest question of LRT vs HRT + TC, the sensitivity, specificity, and accuracy reached 80.00%, 63.41%, and 71.05%, respectively. For HRT vs LRT + TC, they reached 60.53%, 78.95%, and 69.74%. For TC vs LRT + HRT they reached 33.33%, 98.63%, and 96.05%, respectively. Compared with the radiomic models, superior diagnostic efficacy was demonstrated for most clinical-radiomics models, and the AUC of the Bernoulli Naive Bayes model was significantly improved. Radiologist2's assessment achieved a higher AUC of 0.813 (95% CI: 0.756-0.8761) than other radiologists, which was slightly lower than the SVM clinical-radiomic model. Combined with other evaluation indicators, SVM, as the best ML model, demonstrated the potential of predicting the simplified risk categorization of TETs with superior predictive performance to that of radiologists' assessment. CONCLUSION: Most of the ML models are promising in predicting the simplified TETs risk categorization with superior efficacy to that of radiologists' assessment, especially the SVM models, demonstrated the integration of ML with NECT may be valuable in aiding the diagnosis and treatment planning.

Pulmonary microvascular endothelial cells from bleomycin-induced rats promote the transformation and collagen synthesis of fibroblasts.

Accumulation and activation of myofibroblasts are the hallmark of progressive pulmonary fibrosis, and the resident fibroblasts are the major source of myofibroblasts. However, the key factors involved in the transformation of fibroblasts are unknown. Pulmonary microvascular endothelial cells (PMVECs), major effector cells against pathogenesis in early stages of the disease, can secrete cytokines to induce the differentiation of mesenchymal cells. We speculated that PMVECs could secrete pro-fibrotic cytokines and promote the transformation of fibroblasts into myofibroblasts. Accordingly, we established a co-culture system with PMVECs and fibroblasts to examine the specific transformation and collagen synthesis of the co-cultured fibroblasts by FACS and Western blot, prior to and after treatment with neutralizing antibodies against transforming growth factor-beta1 (TGF-ß1) and connective tissue growth factor (CTGF). We also analyzed expression of TGF-ß1 and CTGF in PMVECs. The synthesis and secretion of TGF-ß1 and CTGF protein were up-regulated in PMVECs isolated from bleomycin (BLM)-treated rats, most prominently at 7 days post-instillation. We showed that the PMVECs isolated from BLM-induced rats could induce the transformation of normal fibroblasts and their secretion of collagen I, which was inhibited by both neutralizing anti-TGF-ß1 and anti-CTGF antibodies. Therefore, up-regulation of TGF-ß1 and CTGF in PMVECs plays an important role in activation, transformation, and collagen synthesis of fibroblasts; in particular, these effects in PMVECs are likely to be the key factors for activation and stimulation of static fibroblasts in lung interstitium in early stages of pulmonary fibrosis disease.

Incidence of Cerebral Infarction in Northwest China From 2009 to 2018.

BACKGROUND: There is a lack of epidemiological analysis of patients with cerebral infarction in northwest China. In the present investigation, we conducted a retrospective analysis to collect information on epidemiological characteristics of patients with cerebral infarction in five provinces of northwest China and the Shanxi Province of patients who were hospitalized in the Tangdu Hospital. This project should provide a scientific basis for active prevention and treatment of cerebral infarction. MATERIAL AND METHODS: A retrospective analysis of patients with epidemic characteristics of cerebral infarction that were admitted to the Tangdu Hospital of northwest China from January 2009 to December 2018. RESULTS: A total of 18,302 patients (aged 1-97 years) with confirmed cerebral infarction, including 12,201 males and 6,101 females, were retrospectively enrolled in this study. The most common lesion site was the cerebellum (51.5%). The incidence of cerebral infarction was slightly higher in workers and laborers, favoring male patients and those aged 40-70 years. The difference between men and women gradually increased after the age of 30. CONCLUSIONS: In this study, 18,302 hospitalized patients with cerebral infarction from different occupations were included. Those engaged in physical labor were more likely to have a cerebral infarction. The incidence of cerebral infarction in males was higher than in females. Cerebellar and cerebral area infarctions were the most common.

Classification of Prostate Transitional Zone Cancer and Hyperplasia Using Deep Transfer Learning From Disease-Related Images.

Purpose The diagnosis of prostate transition zone cancer (PTZC) remains a clinical challenge due to their similarity to benign prostatic hyperplasia (BPH) on MRI. The Deep Convolutional Neural Networks (DCNNs) showed high efficacy in diagnosing PTZC on medical imaging but was limited by the small data size. A transfer learning (TL) method was combined with deep learning to overcome this challenge. Materials and methods A retrospective investigation was conducted on 217 patients enrolled from our hospital database (208 patients) and The Cancer Imaging Archive (nine patients). Using T2-weighted images (T2WIs) and apparent diffusion coefficient (ADC) maps, DCNN models were trained and compared between different TL databases (ImageNet vs. disease-related images) and protocols (from scratch, fine-tuning, or transductive transferring). Results PTZC and BPH can be classified through traditional DCNN. The efficacy of TL from natural images was limited but improved by transferring knowledge from the disease-related images. Furthermore, transductive TL from disease-related images had comparable efficacy to the fine-tuning method. Limitations include retrospective design and a relatively small sample size. Conclusion Deep TL from disease-related images is a powerful tool for an automated PTZC diagnostic system. In developing regions where only conventional MR scans are available, the accurate diagnosis of PTZC can be achieved via transductive deep TL from disease-related images.

Multi-modal MRI Reveals the Neurovascular Coupling Dysfunction in Chronic Migraine.

Previous studies reported that long-term nociceptive stimulation could result in neurovascular coupling (NVC) dysfunction in brain, but these studies were based mainly on unimodal imaging biomarkers, thus could not comprehensively reflect NVC dysfunction. We investigated the potential NVC dysfunction in chronic migraine by exploring the relationship between neuronal activity and cerebral perfusion maps. The Pearson correlation coefficients between these 2 maps were defined as the NVC biomarkers. NVC biomarkers in migraineurs were significantly lower in left inferior parietal gyrus (IPG), left superior marginal gyrus (SMG) and left angular gyrus (AG), but significantly higher in right superior occipital gyrus (SOG), right superior parietal gyrus (SPG), and precuneus. These brain regions were located mainly in parietal or occipital lobes and were related to visual or sensory information processing. ALFF-CBF in right SPG was positively correlated with disease history and that in right precuneus was negatively correlated with migraine persisting time. fALFF-CBF in left SMG and AG were negatively related to headache frequency and positively related to health condition and disease history. In conclusion, multi-modal MRI could be used to detect NVC dysfunction in chronic migraine patients, which is a new method to assess the impact of chronic pain on the brain.

Disturbed neurovascular coupling in type 2 diabetes mellitus patients: Evidence from a comprehensive fMRI analysis.

BACKGROUND: Previous studies presumed that the disturbed neurovascular coupling to be a critical risk factor of cognitive impairments in type 2 diabetes mellitus (T2DM), but distinct clinical manifestations were lacked. Consequently, we decided to investigate the neurovascular coupling in T2DM patients by exploring the MRI relationship between neuronal activity and the corresponding cerebral blood perfusion. METHODS: Degree centrality (DC) map and amplitude of low-frequency fluctuation (ALFF) map were used to represent neuronal activity. Cerebral blood flow (CBF) map was used to represent cerebral blood perfusion. Correlation coefficients were calculated to reflect the relationship between neuronal activity and cerebral blood perfusion. RESULTS: At the whole gray matter level, the manifestation of neurovascular coupling was investigated by using 4 neurovascular biomarkers. We compared these biomarkers and found no significant changes. However, at the brain region level, neurovascular biomarkers in T2DM patients were significantly decreased in 10 brain regions. ALFF-CBF in left hippocampus and fractional ALFF-CBF in left amygdala were positively associated with the executive function, while ALFF-CBF in right fusiform gyrus was negatively related to the executive function. The disease severity was negatively related to the memory and executive function. The longer duration of T2DM was related to the milder depression, which suggests T2DM-related depression may not be a physiological condition but be a psychological condition. CONCLUSION: Correlations between neuronal activity and cerebral perfusion maps may be a method for detecting neurovascular coupling abnormalities, which could be used for diagnosis in the future. Trial registry number: This study has been registered in ClinicalTrials.gov (NCT02420470) on April 2, 2015 and published on July 29, 2015.

Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best?

PURPOSE: This study aims to incorporate informative histogram indicator analyses and advanced multimodal MRI parameters to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to explore the features associated with patients' survival. PATIENTS AND METHODS: A total of 120 patients with pathologically confirmed LGGs or HGGs receiving conventional and advanced MRI such as three-dimensional arterial spin labeling (3D-ASL), intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) were included. The mean and histogram indicators from advanced MRI were calculated from the entire tumor. The efficacies of a single indicator or multiple parameters were tested in distinguishing HGGs from LGGs and predicting patients' survival. Receiver operating characteristic (ROC) curve and multivariable stepwise logistic regression were used to evaluate the diagnostic efficacies. Leave-one-out cross-validation was further used to validate the accuracy of the parameter sets in glioma grading. Log-rank test using the Kaplan-Meier curve was utilized to predict patients' survival. RESULTS: Overall, parameters from DCE-MRI performed better than those from 3D-ASL or IVIM-DWI in both glioma grading and survival prediction. The histogram metrics of Ve were demonstrated to have higher accuracies (the accuracies for Extended Tofts_Ve mean and Extended Tofts_Ve median were 68.33% and 71.67%, respectively, while those for the Incremental_Ve mean and Incremental_Ve 75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_Ve histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_Ktrans 95th (AUC = 0.9265) and Extended Tofts_Ve 95th (AUC = 0.9154) performed better than their corresponding means (Patlak_Ktrans mean: AUC = 0.9118 and Extended Tofts_Ve mean: AUC = 0.9044) in predicting patients' overall survival (OS) at 18-month follow-up. CONCLUSION: DCE-MRI-derived histogram features from the entire tumor were promising metrics for glioma grading and OS prediction. Combining single modal histogram features improved glioma grading. TRIAL REGISTRATION: NCT02622620.

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning.

Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas. Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split. Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model. Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.

Intravoxel incoherent motion diffusion-weighted MR imaging parameters predict pathological classification in thymic epithelial tumors.

We evaluated the performance of intravoxel incoherent motion (IVIM) parameters for preoperatively predicting the subtype and Masaoka stage of thymic epithelial tumors (TETs). Seventy-seven patients with pathologically confirmed TETs underwent a diffusion weighted imaging (DWI) sequence with 9 b values. Differences in the slow diffusion coefficient (D), fast perfusion coefficient (D*), and perfusion fraction (f) IVIM parameters, as well as the multi b-value fitted apparent diffusion coefficient (ADCmb), were compared among patients with low-risk (LRT) and high-risk thymomas (HRT) and thymic carcinomas (TC), and between early stage (stages I and II) and advanced stage (stages III and IV) TET patients. ADCmb, D, and D* values were higher in the LRT group than in the HRT or TC group, but did not differ between the HRT and TC groups. The mean ADCmb, D, and D* values were higher in the early stage TETs group than the advanced stage TETs group. The f values did not differ among the groups. These results suggest that IVIM DWI could be used to preoperatively predict subtype and Masaoka stage in TET patients.

The Close Exposure to Radiology Program: Educational Benefits to Medical Students.

BACKGROUND: Radiology clerkships during medical school provide a suboptimal training experience in the Chinese medical doctor training program. Staff radiologists are heavily occupied with clinic tasks which decreases teaching quality. The close exposure to radiology program (CERP) is a novel pathway designed to improve teaching quality, yet students' expectations of the potential benefits of such a program and their willingness to join CERP still have not been investigated among Chinese medical students. METHODS: A survey was conducted among medical students of both sexes with various majors and at different levels of training. The students were asked to identify the potential benefits of CERP as well as to indicate if they were willing to join CERP. RESULTS: Of the 1,600 surveys distributed to medical students, 1,394 were returned and analyzed. Most of the returned surveys were from males (1,268, 91%), and most respondents had not had a radiology clerkship experience (1,376, 99%). Most responding students were in a 5-year training program (94%) and in their third grade of training (41%). More than 60% of the surveyed students acknowledged each of the 5 benefits listed on the survey, although no statistically significant differences were seen between sexes, training grades, those with and without prior radiology experience, program length, or majors in how the questions were answered. Students most willing to participate in CERP were those enrolled in a 5-year training program (71%) and those who had previous radiology clerkship experience (89%). Students least willing to join CERP were majoring in somatology medicine (54%) and medical psychology (55%), and only 45% of students in 8-year programs indicated a willingness to join CERP. Chi-square tests indicated that the willingness to join CERP was not associated with sex (χ2(df = 1393) = 128.6, P=1.00), training program (χ2(df = 1393) = 111.3, P=1.00), training grade (χ2(df = 1393) = 266.1, P=1.00), major (χ2(df = 1393) = 456.1, P=1.00), or previous experience with radiology (χ2(df = 1393) = 142.2, P=1.00). CONCLUSION: Medical students enrolled at Fourth Military Medical University developed an awareness of the potential benefits of CERP; however, this awareness did not correlate with their willingness to join CERP.

Proliferative phenotype of pulmonary microvascular endothelial cells plays a critical role in the overexpression of CTGF in the bleomycin-injured rat.

The pathogenesis of idiopathic pulmonary fibrosis (IPF) is not very clear, with evidence for the involvement of both inflammation and aberrant vascular remodeling (associated with angiogenesis). Pulmonary microvascular endothelial cells (PMVECs), which play a major role in inflammation, secrete cytokines that promote the transformation and collagen synthesis of fibroblasts. Moreover, angiogenesis is characterized by PMVEC proliferation. The main aim of this study was to confirm the role of PMVECs in pulmonary fibrosis. Accordingly, we observed the functional changes in PMVECs in bleomycin (BLM)-treated rats (pulmonary fibrosis model) in vivo, and compared them with those of rats with pneumonia. The proliferation phenotype and intracellular ionized calcium concentration ([Ca(2+)]i) of PMVECs from BLM-treated rats were also investigated. The functioning of PMVECs was abnormal in BLM-injured rats, particularly with regard to their proliferation and secretion of connective tissue growth factor (CTGF). [Ca(2+)]i was increased in the proliferated PMVECs from BLM-treated rats. The findings suggest that dysfunction of PMVECs characterized by overexpression of CTGF is critical in rat pulmonary injury induced by BLM, and is probably related with the proliferative phenotype and [Ca(2+)]i overload. It can be concluded from the results that proliferation of PMVECs plays an important role in the pathogenesis of BLM-induced PF.