Neurovascular decoupling measured with quantitative susceptibility mapping is associated with cognitive decline in patients with type 2 diabetes.

Disturbance of neurovascular coupling (NVC) is suggested to be one potential mechanism in type 2 diabetes mellitus (T2DM) associated mild cognitive impairment (MCI). However, NVC evidence derived from functional magnetic resonance imaging ignores the relationship of neuronal activity with vascular injury. Twenty-seven T2DM patients without MCI and thirty healthy controls were prospectively enrolled. Brain regions with changed susceptibility detected by quantitative susceptibility mapping (QSM) were used as seeds for functional connectivity (FC) analysis. NVC coefficients were estimated using combined degree centrality (DC) with susceptibility or cerebral blood flow (CBF). Partial correlations between neuroimaging indicators and cognitive decline were investigated. In T2DM group, higher susceptibility values in right hippocampal gyrus (R.PHG) were found and were negatively correlated with Naming Ability of Montreal Cognitive Assessment. FC increased remarkably between R.PHG and right middle temporal gyrus (R.MTG), right calcarine gyrus (R.CAL). Both NVC coefficients (DC-QSM and DC-CBF) reduced in R.PHG and increased in R.MTG and R.CAL. Both NVC coefficients in R.PHG and R.MTG increased with the improvement of cognitive ability, especially for executive function. These demonstrated that QSM and DC-QSM coefficients can be promising biomarkers for early evaluation of cognitive decline in T2DM patients and help to better understand the mechanism of NVC.

Functional to structural plasticity in unilateral sudden sensorineural hearing loss: neuroimaging evidence.

A cortical plasticity after long-duration single side deafness (SSD) is advocated with neuroimaging evidence while little is known about the short-duration SSDs. In this case-cohort study, we recruited unilateral sudden sensorineural hearing loss (SSNHL) patients and age-, gender-matched health controls (HC), followed by comprehensive neuroimaging analyses. The primary outcome measures were temporal alterations of varied dynamic functional network connectivity (dFNC) states, neurovascular coupling (NVC) and brain region volume at different stages of SSNHL. The secondary outcome measures were pure-tone audiograms of SSNHL patients before and after treatment. A total of 38 SSNHL patients (21 [55%] male; mean [standard deviation] age, 45.05 [15.83] years) and 44 HC (28 [64%] male; mean [standard deviation] age, 43.55 [12.80] years) were enrolled. SSNHL patients were categorized into subgroups based on the time from disease onset to the initial magnetic resonance imaging scan: early- (n = 16; 1-6 days), intermediate- (n = 9; 7-13 days), and late- stage (n = 13; 14-30 days) groups. We first identified slow state transitions between varied dFNC states at early-stage SSNHL, then revealed the decreased NVC restricted to the auditory cortex at the intermediate- and late-stage SSNHL. Finally, a significantly decreased volume of the left medial superior frontal gyrus (SFGmed) was observed only in the late-stage SSNHL cohort. Furthermore, the volume of the left SFGmed is robustly correlated with both disease duration and patient prognosis. Our study offered neuroimaging evidence for the evolvement from functional to structural brain alterations of SSNHL patients with disease duration less than 1 month, which may explain, from a neuroimaging perspective, why early-stage SSNHL patients have better therapeutic responses and hearing recovery.

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.

Intersubject correlation analysis reveals the plasticity of cerebral functional connectivity in the long-term use of social media.

Owing to the limitations of cross-sectional studies, it is unclear whether social media induce brain changes, or if individuals with certain biological traits are more likely to use social media. Functional connectivity (FC) can reflect cerebral functional plasticity, and if social media can influence cerebral FC, then the FC of light social media users should be more similar to that of heavy users after they "heavily" used social media for a long period. We combined longitudinal study design and intersubject correlation (ISC) analysis to investigate this similarity. Thirty-five heavy and 21 light social media users underwent cognitive tests and functional MRIs. The 21 light social media users underwent another functional MRI scan after completing an additional four-week social media task. We conducted the ISC at the group, individual, and brain-region levels to investigate the similarity of FC and locate the brain regions most affected by social media. The FC of light social media users was more similar to that of heavy social media users after they completed the four-week social media task. Then, social media had an impact on half of the brain, involving almost all brain networks. Finally, cerebral FC that mostly affected by social media was associated with selective attention. We concluded that the impact of social media use on cerebral functional connectivity changes is revealed by ISC method and longitudinal design, which may provide guidance for clinical practice. The methods used in the current research could also be applied to similar domains.

Differentiating thymoma, thymic carcinoma and lymphoma based on collagen fibre patterns with T2- and diffusion-weighted magnetic resonance imaging.

OBJECTIVES: The amount and distribution of intratumoural collagen fibre vary among different thymic tumours, which can be clearly detected with T2- and diffusion-weighted MR images. To explore the incidences of collagen fibre patterns (CFPs) among thymomas, thymic carcinomas and lymphomas on imaging, and to evaluate the efficacy and reproducibility of CFPs in differential diagnosis of thymic tumours. MATERIALS AND METHODS: Three hundred and ninety-eight patients with pathologically diagnosed thymoma, thymic carcinoma and lymphoma who underwent T2- and diffusion-weighted MR imaging were retrospectively enrolled. CFPs were classified into four categories: septum sign, patchy pattern, mixed pattern and no septum sign. The incidences of CFPs were compared among different thymic tumours, and the efficacy and reproducibility in differentiating the defined tumour types were analysed. RESULTS: There were significant differences in CFPs among thymomas, thymic squamous cell carcinomas (TSCCs), other thymic carcinomas and neuroendocrine tumours (OTC&NTs) and thymic lymphomas. Septum signs were found in 209 (86%) thymomas, which differed between thymomas and any other thymic neoplasms (all p < 0.005). The patchy, mixed patterns and no septum sign were mainly seen in TSCCs (80.3%), OTC&NTs (78.9%) and thymic lymphomas (56.9%), respectively. The consistency of different CFP evaluation between two readers was either good or excellent. CFPs achieved high efficacy in identifying the thymic tumours. CONCLUSION: The CFPs based on T2- and diffusion-weighted MR imaging were of great value in the differential diagnosis of thymic tumours. KEY POINTS: • Significant differences are found in intratumoural collagen fibre patterns among thymomas, thymic squamous cell carcinomas, other thymic carcinomas and neuroendocrine tumours and thymic lymphomas. • The septum sign, patchy pattern, mixed pattern and no septum sign are mainly seen in thymomas (86%), thymic squamous cell carcinomas (80.3%), other thymic carcinomas and neuroendocrine tumours (79%) and thymic lymphomas (57%), respectively. • The collagen fibre patterns have high efficacy and reproducibility in differentiating thymomas, thymic squamous cell carcinomas and thymic lymphomas.

Predicting Subtype of Growth Hormone Pituitary Adenoma based on Magnetic Resonance Imaging Characteristics.

PURPOSE: This study aimed to investigate the value of magnetic resonance (MR) characteristics in differentiating the subtypes of growth hormone pituitary adenomas. MATERIALS AND METHODS: The clinical and MR imaging data of 70 patients with growth hormone pituitary adenoma confirmed by surgery and pathology were retrospectively analyzed. The tumors were divided into dense granular (DG; 36 cases) and sparse granular subtypes (SG; 34 cases). The tumors' MR features were analyzed, including the mean and maximum diameters, T2 signal intensity, T2 relative signal intensity (rSI), homogeneity, enhancement degree, and invasiveness (Knosp grade). Mann-Whitney U test and χ2 test were used to analyze MR characteristics between the 2 groups. The independent predictors and predictive probabilities of tumor subtypes were obtained via a logistic regression model, and the efficacy was compared by receiver operating characteristic curve. RESULTS: The mean and maximum diameters of growth hormone adenoma in DG and SG were 1.77 versus 2.45 and 1.95 versus 3.00 cm (median, P < 0.05), respectively. There was a significant difference between the 2 groups in T2 signal intensity and rSI (P values were 0.02 and 0.001, respectively). Most DG adenomas (86.1%) appeared as hypointense on T2 images, and 38.2% of SG adenomas were hyperintense. There was no significant difference in tumor homogeneity (P = 0.622). A significant difference was found in the Knosp grade between the 2 subtypes (P = 0.004). In addition, the enhancement degree of SG adenomas was significantly higher than that of DG adenomas (P = 0.001). Logistic regression analysis showed that high T2 rSI value and marked contrast enhancement were independent predictors of the 2 subtypes, and the odds ratios were 4.811 and 4.649, respectively. The multivariate logistic model obtained relatively high predicting efficacy, and the area under the curve, sensitivity, and specificity were 0.765, 0.882, and 0.500, respectively. CONCLUSIONS: There are significant differences in tumor size, T2 signal intensity, T2 rSI, enhancement degree, and invasiveness between DG and SG adenomas. The logistic model based on the marked contrast enhancement and high T2 rSI value has an important value in predicting the subtype of growth hormone adenoma.

MR imaging of thymomas: a combined radiomics nomogram to predict histologic subtypes.

OBJECTIVES: Accurately predicting the WHO classification of thymomas is urgently needed to optimize individualized therapeutic strategies. We aimed to develop and validate a combined radiomics nomogram for personalized prediction of histologic subtypes in patients with thymomas. METHODS: A total of 182 thymoma patients were divided into training (n = 128) and test (n = 54) cohorts. Radiomics features were extracted from T2-weighted, T2-weighted fat suppression, and diffusion-weighted images to establish a radiomics signature in the training cohort. Multivariate logistic regression analysis was used to develop a combined radiomics nomogram that incorporated clinical, conventional MR imaging variables, apparent diffusion coefficient (ADC) value, and radiomics signature. The efficacy of clinical, conventional MR imaging, or ADC model was also evaluated respectively. The performances of different models were compared by receiver operating characteristic analysis and Delong test. The discrimination, calibration, and clinical usefulness of the combined radiomics nomogram were assessed. RESULTS: The radiomics signature, consisting of 14 features, achieved favorable predictive efficacy in differentiating low-risk from high-risk thymomas, outperforming clinical, conventional MR imaging, and ADC models. The combined radiomics nomogram incorporating tumor shape, ADC value, and radiomics signature yielded the best performance (training cohort: area under the curve [AUC] = 0.946, test cohort: AUC = 0.878). The calibration curve and decision curve analysis indicated the clinical utility of the combined radiomics nomogram. CONCLUSIONS: The radiomics signature is a useful tool that can be used to predict histologic subtypes of thymomas. The combined radiomics nomogram improved the individualized subtype prediction in patients with thymomas. KEY POINTS: • Fourteen robust features were selected to develop a radiomics signature for preoperative prediction of thymoma subtype. • MRI-based radiomics signature can differentiate low-risk thymomas from high-risk thymomas with favorable predictive efficacy compared with clinical, conventional MR imaging, and ADC models. • Combined radiomics nomogram based on tumor shape, ADC value, and radiomics signature could improve the individualized subtype prediction in patients with thymomas.

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.

MRI Radiomics Analysis for Predicting the Pathologic Classification and TNM Staging of Thymic Epithelial Tumors: A Pilot Study.

OBJECTIVE. The purpose of this study was to explore the performance of MRI radiomics in predicting the pathologic classification and TNM staging of thymic epithelial tumors (TETs). MATERIALS AND METHODS. Clinical and MRI data for 189 patients with TETs were retrospectively collected. A total of 2088 radiomics features were extracted from T2-weighted images and T2-weighted fat-suppressed (FS) images. With the use of a support vector machine with recursive feature elimination, the optimal feature subsets were selected and used to construct two predictive models for pathologic classification and TNM staging. In multivariable logistic regression analysis, we incorporated the radiomics model, conventional MRI findings, and clinical variables to develop a radiomics nomogram for predicting risk stratification of advanced TETs. RESULTS. Of the extracted features, 125 features were selected to construct the radiomics model for predicting pathologic classification, and 69 features were selected to construct the radiomics model for predicting TNM staging. The models achieved AUC values of 0.880 and 0.948 in the training cohort and 0.771 and 0.908 in the test cohort, respectively, for distinguishing among low-risk thymomas, high-risk thymomas, and thymic carcinomas and differentiating between early-stage and advanced-stage TETs. The radiomics model, symptom, and pericardial effusion constituted a radiomics nomogram, with an AUC value of 0.967 (95% CI, 0.891-0.989) in the training cohort and 0.957 (95% CI, 0.842-0.974) in the test cohort. CONCLUSION. MRI radiomics analysis has the potential to differentiate the pathologic classification and TNM staging of TETs. A radiomics nomogram provides a useful tool for in dividualized prediction of the risk of advanced-stage TET before a patient undergoes treatment.

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.

BACKGROUND: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. METHODS: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. RESULTS: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. CONCLUSIONS: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain?

BACKGROUND: Our study aims to reveal whether the low b-values distribution, high b-values upper limit, and the number of excitation (NEX) influence the accuracy of the intravoxel incoherent motion (IVIM) parameter derived from multi-b-value diffusion-weighted imaging (DWI) in the brain. METHODS: This prospective study was approved by the local Ethics Committee and informed consent was obtained from each participant. The five consecutive multi-b DWI with different b-value protocols (0-3500 s/mm2) were performed in 22 male healthy volunteers on a 3.0-T MRI system. The IVIM parameters from normal white matter (WM) and gray matter (GM) including slow diffusion coefficient (D), fast perfusion coefficient (D*) and perfusion fraction (f) were compared for differences among defined groups with different IVIM protocols by one-way ANOVA. RESULTS: The D* and f value of WM or GM in groups with less low b-values distribution (less than or equal to 5 b-values) were significantly lower than ones in any other group with more low b-values distribution (all P <  0.05), but no significant differences among groups with more low b-values distribution (P > 0.05). In addition, no significant differences in the D, D* and f value of WM or GM were found between group with one and more NEX of low b-values distribution (all P > 0.05). IVIM parameters in normal WM and GM strongly depended on the choice of the high b-value upper limit. CONCLUSIONS: Metrics of IVIM parameters can be affected by low and high b value distribution. Eight low b-values distribution with high b-value upper limit of 800-1000 s/mm2 may be the relatively proper set when performing brain IVIM studies.

Neurovascular decoupling in type 2 diabetes mellitus without mild cognitive impairment: Potential biomarker for early cognitive impairment.

Type 2 diabetes mellitus (T2DM) is a significant risk factor for mild cognitive impairment (MCI) and the acceleration of MCI to dementia. The high glucose level induce disturbance of neurovascular (NV) coupling is suggested to be one potential mechanism, however, the neuroimaging evidence is still lacking. To assess the NV decoupling pattern in early diabetic status, 33 T2DM without MCI patients and 33 healthy control subjects were prospectively enrolled. Then, they underwent resting state functional MRI and arterial spin labeling imaging to explore the hub-based networks and to estimate the coupling of voxel-wise cerebral blood flow (CBF)-degree centrality (DC), CBF-mean amplitude of low-frequency fluctuation (mALFF) and CBF- mean regional homogeneity (mReHo). We further evaluated the relationship between NV coupling pattern and cognitive performance (false discovery rate corrected). T2DM without MCI patients displayed significant decrease in the absolute CBF-mALFF, CBF-mReHo coupling of CBFnetwork and in the CBF-DC coupling of DCnetwork. Besides, networks which involved CBF and DC hubs mainly located in the default mode network (DMN). Furthermore, less severe disease and better cognitive performance in T2DM patients were significantly correlated with higher coupling of CBF-DC, CBF-mALFF or CBF-mReHo, especially for the cognitive dimensions of general function and executive function. Thus, coupling of CBF-DC, CBF-mALFF and CBF-mReHo may serve as promising indicators to reflect NV coupling state and to explain the T2DM related early cognitive impairment.

Fetal brain development at 25-39 weeks gestational age: A preliminary study using intravoxel incoherent motion diffusion-weighted imaging.

BACKGROUND: The fetal brain developmental changes of water diffusivity and perfusion has not been extensively explored. PURPOSE/HYPOTHESIS: To evaluate the fetal brain developmental changes of water diffusivity and perfusion using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). STUDY TYPE: Prospective. POPULATION: Seventy-nine normal singleton fetuses were scanned without sedation of healthy pregnant women. FIELD STRENGTH/SEQUENCE: 5 T MRI/T1 /2 -weighted image and IVIM-DWI. ASSESSMENT: Pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) values were calculated in the frontal (FWM), temporal (TWM), parietal (PWM), and occipital white matter (OWM) as well as cerebellar hemisphere (CH), basal ganglia region (BGR), thalamus (TH), and pons using an IVIM model. STATISTICAL TESTS: One-way analysis of variable (ANOVA) followed by Bonferroni post-hoc multiple comparison was employed to reveal the difference of IVIM parameters among the investigated brain regions. The linear and the nonlinear polynomial regression analyses were utilized to reveal the correlation between gestational age (GA) and IVIM parameters. RESULTS: There were significant differences in both D (F(7,623) = 96.64, P = 0.000) and f values (F(7,623) = 2.361, P = 0.0219), but not D* values among the varied brain regions. D values from TWM (r2 = 0.1402, P = 0.0002), PWM (r2 = 0.2245, P = 0.0002), OWM (r2 = 0.2519, P = 0.0002), CH (r2 = 0.2245, P = 0.0002), BGR (r2 = 0.3393, P = 0.0001), TH (r2 = 0.1259, P = 0.0001), and D* value from pons (r2 = 0.2206, P = 0.0002) were significantly correlated with GA using linear regression analysis. Quadratic regression analysis led to results similar to those using the linear regression model. DATA CONCLUSION: IVIM-DWI parameters may indicate fetal brain developmental alterations but the conclusion is far from reached due to the not as high-powered correlation between IVIM parameters and GA. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:899-909.

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.

Predicting pathological subtypes and stages of thymic epithelial tumors using DWI: value of combining ADC and texture parameters.

OBJECTIVES: To explore the value of combining apparent diffusion coefficients (ADC) and texture parameters from diffusion-weighted imaging (DWI) in predicting the pathological subtypes and stages of thymic epithelial tumors (TETs). METHODS: Fifty-seven patients with TETs confirmed by pathological analysis were retrospectively enrolled. ADC values and optimal texture feature parameters were compared for differences among low-risk thymoma (LRT), high-risk thymoma (HRT), and thymic carcinoma (TC) by one-way ANOVA, and between early and advanced stages of TETs were tested using the independent samples t test. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy. RESULTS: The ADC values in LRT and HRT were significantly higher than the values in TC (p = 0.004 and 0.001, respectively), also in early stage, values were significantly higher than ones in advanced stage of TETs (p < 0.001). Among all texture parameters analyzed in order to differentiate LRT from HRT and TC, the V312 achieved higher diagnostic efficacy with an AUC of 0.875, and combination of ADC and V312 achieved the highest diagnostic efficacy with an AUC of 0.933, for differentiating the LRT from HRT and TC. Furthermore, combination of ADC and V1030 achieved a relatively high differentiating ability with an AUC of 0.772, for differentiating early from advanced stages of TETs. CONCLUSIONS: Combination of ADC and DWI texture parameters improved the differentiating ability of TET grades, which could potentially be useful in clinical practice regarding the TET evaluation before treatment. KEY POINTS: • DWI texture analysis is useful in differentiating TET subtypes and stages. • Combination of ADC and DWI texture parameters may improve the differentiating ability of TET grades. • DWI texture analysis could potentially be useful in clinical practice regarding the TET evaluation before treatment.

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.

Usefulness of Volume Perfusion Computed Tomography in Differentiating Histologic Subtypes of Thymic Epithelial Tumors.

PURPOSE: This study aimed to evaluate the usefulness of volume perfusion computed tomography (VPCT) parameters in differentiating the World Health Organization subtypes of thymic epithelial tumors. MATERIALS AND METHODS: This study was approved by the local ethics committee, and informed written consent was obtained. Fifty-one thymic epithelial tumor patients confirmed by histopathological analysis underwent conventional CT and a 48-second VPCT scan of the tumor bulk before any treatment. The VPCT parameters (blood volume [BV], blood flow [BF], mean transit time [MTT], and permeability [PMB]) based on volume of interest (VOI) or region of interest (ROI) were compared for differences among low-risk thymomas (LRTs; types A, AB, and B1), high-risk thymomas (HRTs; types B2 and B3) and thymic carcinomas (TCs) by one-way analysis of variance. RESULTS: The BVVOI, PMBVOI, BVROI, and PMBROI values in LRT were significantly higher than the values from HRT and thymic carcinoma (BVVOI: 13.75, 6.17, and 5.48 mL/100 mL; PMBVOI: 22.47, 9.56, and 13.37 mL/100 mL/min; BVROI: 14.75, 6.87, and 6.06 mL/100 mL; PMBROI: 24.05, 9.79, and 15.63 mL/100 mL/min, respectively; all P < 0.05/3). However, the BFVOI, MTTVOI, BFROI, and MTTROI values did not differ between LRT and HRT or thymic carcinoma groups (P > 0.05/3). CONCLUSIONS: These results suggest that VPCT could be useful in differentiating LRTs from HRTs and TCs preoperatively.

Iodine Quantification Using Dual-Energy Computed Tomography for Differentiating Thymic Tumors.

The aim of the study was to explore the efficacy of iodine quantification with dual-energy computed tomography (DECT) in differentiating thymoma, thymic carcinoma, and thymic lymphoma. MATERIALS AND METHODS: Fifty-seven patients with pathologically confirmed low-risk thymoma (n = 16), high-risk thymoma (n = 15), thymic carcinoma (n = 14), and thymic lymphoma (n = 12) underwent chest contrast-enhanced DECT scan were enrolled in this study. Tumor DECT parameters including iodine-related Hounsfield unit (IHU), iodine concentration (IC), mixed HU (MHU), and iodine ratio in dual phase, slope of energy spectral HU curve (λ), and virtual noncontrast (VNC) were compared for differences among 4 groups by one-way analysis of variance. Receiver operating characteristic curve was used to determine the efficacy for differentiating the low-risk thymoma from other thymic tumor by defined parameters. RESULTS: According to quantitative analysis, dual-phase IHU, IC, and MHU values in patients with low-risk thymoma were significantly increased compared with patients with high-risk thymoma, thymic carcinoma, and thymic lymphoma (P < 0.05/4).The venous phase IHU value yielded the highest performance with area under the curve of 0.893, 75.0% sensitivity, and 89.7% specificity for differentiating the low-risk thymomas from high-risk thymomas or thymic carcinoma at the cutoff value of 34.3 HU. When differentiating low-risk thymomas from thymic lymphoma, the venous phase IC value obtained the highest diagnostic efficacy with the area under the curve of 0.969, and sensitivity, specificity, and cutoff value were 87.5%, 100.0%, and 1.25 mg/mL, respectively. CONCLUSIONS: Iodine quantification with DECT may be useful for differentiating the low-risk thymomas from other thymic tumors.

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.