A Convolutional Neural Network Model for Distinguishing Hemangioblastomas From Other Cerebellar-and-Brainstem Tumors Using Contrast-Enhanced MRI.

BACKGROUND: Hemangioblastoma (HB) is a highly vascularized tumor most commonly occurring in the posterior cranial fossa, requiring accurate preoperative diagnosis to avoid accidental intraoperative hemorrhage and even death. PURPOSE: To accurately distinguish HBs from other cerebellar-and-brainstem tumors using a convolutional neural network model based on a contrast-enhanced brain MRI dataset. STUDY TYPE: Retrospective. POPULATION: Four hundred five patients (182 = HBs; 223 = other cerebellar-and brainstem tumors): 305 cases for model training, and 100 for evaluation. FIELD STRENGTH/SEQUENCE: 3 T/contrast-enhanced T1-weighted imaging (T1WI + C). ASSESSMENT: A CNN-based 2D classification network was trained by using sliced data along the z-axis. To improve the performance of the network, we introduced demographic information, various data-augmentation methods and an auxiliary task to segment tumor region. Then, this method was compared with the evaluations performed by experienced and intermediate-level neuroradiologists, and the heatmap of deep feature, which indicates the contribution of each pixel to model prediction, was visualized by Grad-CAM for analyzing the misclassified cases. STATISTICAL TESTS: The Pearson chi-square test and an independent t-test were used to test for distribution difference in age and sex. And the independent t-test was exploited to evaluate the performance between experts and our proposed method. P value <0.05 was considered significant. RESULTS: The trained network showed a higher accuracy for identifying HBs (accuracy = 0.902 ± 0.031, F1 = 0.891 ± 0.035, AUC = 0.926 ± 0.040) than experienced (accuracy = 0.887 ± 0.013, F1 = 0.868 ± 0.011, AUC = 0.881 ± 0.008) and intermediate-level (accuracy = 0.827 ± 0.037, F1 = 0.768 ± 0.068, AUC = 0.810 ± 0.047) neuroradiologists. The recall values were 0.910 ± 0.050, 0.659 ± 0.084, and 0.828 ± 0.019 for the trained network, intermediate and experienced neuroradiologists, respectively. Additional ablation experiments verified the utility of the introduced demographic information, data augmentation, and the auxiliary-segmentation task. DATA CONCLUSION: Our proposed method can successfully distinguish HBs from other cerebellar-and-brainstem tumors and showed diagnostic efficiency comparable to that of experienced neuroradiologists. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.

OBJECTIVES: To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images. METHODS: A retrospective, bi-center study of ICH patients was conducted. Firstly, a custom 3D convolutional model was built for predicting the functional outcome of ICH patients based on CT scans from randomly selected ICH patients in H training dataset collected from H hospital. Secondly, clinical data and radiological features were collected at admission and the Extreme Gradient Boosting (XGBoost) algorithm was used to establish a second model, named the XGBoost model. Finally, the Convolution model and XGBoost model were fused to build the third "Fusion model." Favorable outcome was defined as modified Rankin Scale score of 0-3 at discharge. The prognostic predictive accuracy of the three models was evaluated using an H test dataset and an external Y dataset, and compared with the performance of ICH score and ICH grading scale (ICH-GS). RESULTS: A total of 604 patients with ICH were included in this study, of which 450 patients were in the H training dataset, 50 patients in the H test dataset, and 104 patients in the Y dataset. In the Y dataset, the areas under the curve (AUCs) of the Convolution model, XGBoost model, and Fusion model were 0.829, 0.871, and 0.905, respectively. The Fusion model prognostic performance exceeded that of ICH score and ICH-GS (p = 0.043 and p = 0.045, respectively). CONCLUSIONS: Deep learning models have good accuracy for predicting functional outcome of patients with spontaneous intracerebral hemorrhage. CLINICAL RELEVANCE STATEMENT: The proposed deep learning Fusion model may assist clinicians in predicting functional outcome and developing treatment strategies, thereby improving the survival and quality of life of patients with spontaneous intracerebral hemorrhage. KEY POINTS: • Integrating clinical presentations, CT images, and radiological features to establish deep learning model for functional outcome prediction of patients with intracerebral hemorrhage. • Deep learning applied to CT images provides great help in prognosing functional outcome of intracerebral hemorrhage patients. • The developed deep learning model performs better than clinical prognostic scores in predicting functional outcome of patients with intracerebral hemorrhage.

Magnetic resonance imaging with upconversion nanoprobes capable of crossing the blood-cerebrospinal fluid barrier.

The central nervous system (CNS) maintains homeostasis with its surrounding environment by restricting the ingress of large hydrophilic molecules, immune cells, pathogens, and other external harmful substances to the brain. This function relies heavily on the blood-cerebrospinal fluid (B-CSF) and blood-brain barrier (BBB). Although considerable research has examined the structure and function of the BBB, the B-CSF barrier has received little attention. Therapies for disorders associated with the central nervous system have the potential to benefit from targeting the B-CSF barrier to enhance medication penetration into the brain. In this study, we synthesized a nanoprobe ANG-PEG-UCNP capable of crossing the B-CSF barrier with high targeting specificity using a hydrocephalus model for noninvasive magnetic resonance ventriculography to understand the mechanism by which the CSF barrier may be crossed and identify therapeutic targets of CNS diseases. This magnetic resonance nanoprobe ANG-PEG-UCNP holds promising potential as a safe and effective means for accurately defining the ventricular anatomy and correctly locating sites of CSF obstruction.

d-mannose suppresses oxidative response and blocks phagocytosis in experimental neuroinflammation.

Inflammation drives the pathology of many neurological diseases. d-mannose has been found to exert an antiinflammatory effect in peripheral diseases, but its effects on neuroinflammation and inflammatory cells in the central nervous system have not been studied. We aimed to determine the effects of d-mannose on key macrophage/microglial functions-oxidative stress and phagocytosis. In murine experimental autoimmune encephalomyelitis (EAE), we found d-mannose improved EAE symptoms compared to phosphate-buffered saline (PBS)-control mice, while other monosaccharides did not. Multiagent molecular MRI performed to assess oxidative stress (targeting myeloperoxidase [MPO] using MPO-bis-5-hydroxytryptamide diethylenetriaminepentaacetate gadolinium [Gd]) and phagocytosis (using cross-linked iron oxide [CLIO] nanoparticles) in vivo revealed that d-mannose-treated mice had smaller total MPO-Gd+ areas than those of PBS-control mice, consistent with decreased MPO-mediated oxidative stress. Interestingly, d-mannose-treated mice exhibited markedly smaller CLIO+ areas and much less T2 shortening effect in the CLIO+ lesions compared to PBS-control mice, revealing that d-mannose partially blocked phagocytosis. In vitro experiments with different monosaccharides further confirmed that only d-mannose treatment blocked macrophage phagocytosis in a dose-dependent manner. As phagocytosis of myelin debris has been known to increase inflammation, decreasing phagocytosis could result in decreased activation of proinflammatory macrophages. Indeed, compared to PBS-control EAE mice, d-mannose-treated EAE mice exhibited significantly fewer infiltrating macrophages/activated microglia, among which proinflammatory macrophages/microglia were greatly reduced while antiinflammatory macrophages/microglia increased. By uncovering that d-mannose diminishes the proinflammatory response and boosts the antiinflammatory response, our findings suggest that d-mannose, an over-the-counter supplement with a high safety profile, may be a low-cost treatment option for neuroinflammatory diseases such as multiple sclerosis.

Inhibition of Notch1 signal promotes brain recovery by modulating glial activity after stroke.

BACKGROUND: Notch1 signaling inhibiton with N-[N-(3,5-difluorophenacetyl)-1-alanyl]-S-phenylglycine t-butylester] (DAPT) treatment could promote brain recovery and the intervention effect is different between striatum (STR) and cortex (CTX), which might be accounted for different changes of glial activities, but the in-depth mechanism is still unknown. The purpose of this study was to identify whether DAPT could modulate microglial subtype shifts and astroglial-endfeet aquaporin-4 (AQP4) mediated waste solute drainage. METHODS: Sprague-Dawley rats (n=10) were subjected to 90min of middle cerebral artery occlusion (MCAO) and were treated with DAPT (n=5) or act as control with no treatment (n=5). Two groups of rats underwent MRI scans at 24h and 4 week, and sacrificed at 4 week after stroke for immunofluorescence (IF). RESULTS: Compared with control rats, MRI data showed structural recovery in ipsilateral STR but not CTX. And IF showed decreased pro-inflammatory M1 microglia and increased anti-inflammatory M2 microglia in striatal lesion core and peri-lesions of STR, CTX. Meanwhile, IF showed decreased AQP4 polarity in ischemic brain tissue, however, AQP4 polarity in striatal peri-lesions of DAPT treated rats was higher than that in control rats but shows no difference in cortical peri-lesions between control and treated rats. CONCLUSIONS: The present study indicated that DAPT could promote protective microglia subtype shift and striatal astrocyte mediated waste solute drainage, that the later might be the major contributor of waste solute metabolism and one of the accounts for discrepant recovery of STR and CTX.

DKI can distinguish high-grade gliomas from IDH1-mutant low-grade gliomas and correlate with their different nuclear-to-cytoplasm ratio: a localized biopsy-based study.

OBJECTIVES: To explore whether differences in diffusional kurtosis imaging (DKI) between therapy-naïve high-grade gliomas (HGGs) and low-grade gliomas (LGGs) are related to the cellularity and/or the nuclear-to-cytoplasmic (N/C) ratio. METHODS: We analyzed 44 and 40 diffuse glioma samples that were pathologically confirmed as HGGs and IDH1-mutant LGGs, respectively. The DKI parameters included kurtosis metrics (mean kurtosis [MK], axial kurtosis [K//], and radial kurtosis [K⊥]), and the diffusional metrics (fractional anisotropy [FA], mean diffusion [MD], axial diffusion [λ//], and radial diffusion [λ⊥]). The cellularity and the N/C ratio were compared within LGGs and HGGs using the Mann-Whitney U test (significant level, p < 0.007 [0.05/7]); Bonferroni correction). Spearman's correlation analysis was used to calculate the correlation coefficients among DKI metrics, cellularity, and the N/C ratio at a significant level of p = 0.05. RESULTS: Excluding FA, all DKI metrics showed significant differences between HGGs and LGGs (all p ≤ 0.001). The N/C ratio of HGGs was significantly higher than that of LGGs; however, differences in cellularity were not significant between the two glioma groups (p = 0.525). Similarly, excluding FA, all DKI metrics were significantly correlated with the N/C ratio in LGGs, with correlation coefficients of - 0.365 (MD), - 0.313 (λ//), - 0.376 (λ⊥), 0.859 (MK), 0.772 (K//), and 0.842 (K//). There was a non-significant correlation between any DKI parameters and the cellularity in LGGs. Additionally, the cellularity and N/C ratios in HGGs did not correlate with any DKI metrics. CONCLUSIONS: DKI differentiate LGGs from HGGs associated with their different N/C ratios. CLINICAL RELEVANCE STATEMENT: This study shows that DKI differentiates LGGs from HGGs may correlated with their different N/C ratios, this could provide a possible histopathological mechanism about why DKI can DKI differentiate LGGs from HGGs. KEY POINTS: • Excluding FA, all DKI metrics showed a significant difference between high-grade gliomas and IDH1-mutant low-grade gliomas. • The nuclear-to-cytoplasm ratios in high-grade gliomas were significantly more extensive than that in IDH1-mutant low-grade gliomas, but not the cellularity. • Significant associations were seen between DKI measures and the N/C ratio; a non-significant correlation was noted between any DKI metric and cellularity in glioma specimens.

Correlations between intravoxel incoherent motion-derived fast diffusion and perfusion fraction parameters and VEGF- and MIB-1-positive rates in brain gliomas: an intraoperative MR-navigated, biopsy-based histopathologic study.

OBJECTIVES: To explore the correlations between histopathologic findings and intravoxel incoherent motion (IVIM)-derived perfusion and diffusion parameters in brain gliomas. METHODS: Thirty-two biopsy samples from twenty-one patients with newly diagnosed gliomas from a previous prospective cohort study were retrospectively analyzed. All patients underwent diffusion-weighted MRI with 22 b values (0-5000 s/mm2), followed by intraoperative MR-guided biopsy surgery and surgical resection. All 32 biopsy samples underwent immunohistochemical staining followed by quantitative analysis of cell density (cellularity), percent of MIB-1 (Ki67)-positive expression (pMIB-1), number of CD34-stained vessels (CD34-MVD), and percent of VEGF-positive expressing cells (pVEGF) using a multispectral phenotyping microscope. Based on the co-registered localized biopsy, correlation analysis was performed between the IVIM-derived biexponential model-based parameters (Dfast1500 and Dfast5000, Dslow1500 and Dslow5000, PF1500 and PF5000) and the above four pathological biomarkers and glioma grades. RESULTS: Significant positive correlations were revealed between Dfast5000 and pVEGF (rho (r) = 0.466, p = 0.007), and Dfast1500 and pVEGF (r = 0.371, p = 0.037). A significant negative correlation was revealed between PF5000 with pMIB-1 (r = - 0.456, p = 0.01). Moderate to good positive correlations were shown between Dfast5000 and glioma grades (r = 0.509, p = 0.003) and Dfast1500 and glioma grades (r = 0.476, p = 0.006). CONCLUSIONS: IVIM-DWI-derived Dfast and PF correlate, respectively, with intratumor pVEGF and pMIB-1. When using the wide-high b value scheme, IVIM-derived Dfast and PF tend to demonstrate better efficacy in evaluating malignancy-related characteristics such as angiogenesis and cellular proliferation in gliomas. KEY POINTS: • Intravoxel incoherent motion-diffusion-weighted imaging (IVIM-DWI)-derived fast diffusion (Dfast) and perfusion fraction (PF) can quantitatively reflect intratumor pVEGF and pMIB-1. • IVIM-DWI-derived Dfast and PF tend to demonstrate better efficacy in evaluating glioma malignancy when an optimized scheme is used. • IVIM-DWI-derived Dfast5000 and PF5000 are promising non-invasive parameters correlating with pVEGF and pMIB-1 in gliomas.

Alignment rule and geometric confinement lead to stability of a vortex in active flow.

Vortices are hallmarks of a wide range of nonequilibrium phenomena in fluids at multiple length scales. In this work, we numerically study the whirling motion of self-propelled soft point particles confined in circular domain, and aim at addressing the stability issue of the coherent vortex structure. By the combination of dynamical and statistical analysis at the individual particle level, we reveal the persistence of the whirling motion resulting from the subtle competition of activity and geometric confinement. In the stable whirling motion, the scenario of the coexistence of the irregular microscopic motions of individual particles and the regular global whirling motion is fundamentally different from the motion of a vortex in passive fluid. Possible orientational order coexisting with the whirling are further explored. This work shows the stability mechanism of vortical dynamics in active media under the alignment rule in confined space and may have implications in creating and harnessing macroscale coherent dynamical states by tuning the confining geometry.

Intravoxel incoherent motion MRI for rectal cancer: correlation of diffusion and perfusion characteristics with clinical-pathologic factors.

BACKGROUND: Colorectal cancer is the most common cause of cancer-related death worldwide. Magnetic resonance imaging (MRI) has become a promising alternative method for staging the cancer. PURPOSE: To evaluate parameters of intravoxel incoherent motion (IVIM) and their relationships with clinical-pathologic factors in rectal cancers. MATERIAL AND METHODS: A total of 51 patients with histopathologically proven rectal cancer who underwent preoperative pelvic MRI were prospectively enrolled. Parameters (ADC, D, D*, and f) derived from IVIM-diffusion-weighted imaging (DWI) were independently measured by two radiologists. Student's t-test, receiver operating characteristic curves, and Spearman correlation were used for statistical analysis. RESULTS: ADC, D, and D* were significantly higher in pT1-2 tumors than in pT3-4 tumors (1.108 ± 0.233 vs. 0.950 ± 0.176, 0.796 ± 0.199 vs. 0.684 ± 0.114, 0.013 ± 0.005 vs. 0.008 ± 0.003, respectively; P < 0.05). D* exhibited a strong correlation with the tumor stage (r = -0.675, P < 0.001). In poorly differentiated cluster (PDC) grading, ADC, D*, and f were significantly lower in high-grade tumors than in low-grade tumors (0.905 ± 0.148 vs. 1.064 ± 0.200, 0.008 ± 0.002 vs. 0.011 ± 0.005, and 0.252 ± 0.032 vs. 0.348 ± 0.058, respectively; P < 0.05). The f value exhibited a significantly strong correlation with the PDC grades (r = -0.842, P < 0.001), and higher sensitivity and specificity (95.2% and 75.9%) than those shown by the ADC, D, and D* values. CONCLUSION: IVIM parameters, especially f, demonstrated a strong correlation with histologic grades and showed a better performance in differentiating between high- and low-grade rectal cancers. These parameters would be helpful in predicting tumor aggressiveness and prognosis.

Contrast-enhanced 3D-T2-weighted SPACE sequence for MRI detection and localization of adrenocorticotropin (ACTH)-secreting pituitary microadenomas.

OBJECTIVE: Cushing disease is a potentially fatal endocrine disorder caused by adrenocorticotropin (ACTH)-secreting microadenomas in the pituitary gland. Accurate detection and localization of the adenomas is the key to clinical treatment. This study analysed the value of contrast-enhanced Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) sequence in magnetic resonance imaging (MRI) assessment of ACTH-secreting pituitary microadenomas. DESIGN AND PATIENTS: We carried out a retrospective study in which 45 patients with ACTH-secreting pituitary microadenomas were enrolled. Dynamic contrast-enhanced (DCE) coronal T1-SE sequence was performed. A contrast-enhanced coronal SPACE sequence was added immediately after DCE MRI finished. Two independent observers assessed the tumour existence and location, then the results were compared with surgical findings. RESULTS: Twenty-four lesions (53.3%) were detected by the DCE T1-SE sequence alone, while 35 lesions (80.0%) were detected with the addition of contrast-enhanced SPACE sequence. The sensitivity (58.5% vs. 85.3%; p < .05) and best diagnostic accuracy (62.0% vs. 84.4%; p < .05) were significantly better for addition with SPACE sequence than DCE-SE images alone in detection of ACTH-secreting pituitary microadenomas. For lesions <5 mm, the detected numbers were 4 (16.6%) versus 10 (27.8%) by DCE T1-SE sequence and combined DCE T1-SE with SPACE sequence. CONCLUSIONS: A combination of contrast-enhanced SPACE with DCE T1-SE sequence could improve the detection of ACTH-secreting pituitary microadenomas. Contrast-enhanced SPACE sequence could be a supplementary sequence for imaging of ACTH-secreting pituitary adenomas when T1-SE sequence provides negative or equivocal findings.

A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas.

OBJECTIVES: To construct a radiomics nomogram based on multiparametric MRI data for predicting isocitrate dehydrogenase 1 mutation (IDH +) and loss of nuclear alpha thalassemia/mental retardation syndrome X-linked expression (ATRX -) in patients with lower-grade gliomas (LrGG; World Health Organization [WHO] 2016 grades II and III). METHODS: A total of 111 LrGG patients (76 mutated IDH and 35 wild-type IDH) were enrolled, divided into a training set (n = 78) and a validation set (n = 33) for predicting IDH mutation. IDH + LrGG patients were further stratified into the ATRX - (n = 38) and ATRX + (n = 38) subtypes. A total of 250 radiomics features were extracted from the region of interest of each tumor, including that from T2 fluid-attenuated inversion recovery (T2 FLAIR), contrast-enhanced T1 WI, ASL-derived cerebral blood flow (CBF), DWI-derived ADC, and exponential ADC (eADC). A radiomics signature was selected using the Elastic Net regression model, and a radiomics nomogram was finally constructed using the age, gender information, and above features. RESULTS: The radiomics nomogram identified LrGG patients for IDH mutation (C-index: training sets = 0.881, validation sets = 0.900) and ATRX loss (C-index: training sets = 0.863, validation sets = 0.840) with good calibration. Decision curve analysis further confirmed the clinical usefulness of the two nomograms for predicting IDH and ATRX status. CONCLUSIONS: The nomogram incorporating age, gender, and the radiomics signature provided a clinically useful approach in noninvasively predicting IDH and ATRX mutation status for LrGG patients. The proposed method could facilitate MRI-based clinical decision-making for the LrGG patients. KEY POINTS: • Non-invasive determination of IDH and ATRX gene status of LrGG patients can be obtained with a radiomics nomogram. • The proposed nomogram is constructed by radiomics signature selected from 250 radiomics features, combined with age and gender. • The proposed radiomics nomogram exhibited good calibration and discrimination for IDH and ATRX gene mutation stratification of LrGG patients in both training and validation sets.

Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas.

OBJECTIVE: To predict silent corticotroph adenomas (SCAs) among non-functioning pituitary adenomas preoperatively using noninvasive radiomics. METHODS: A total of 302 patients including 146 patients diagnosed with SCAs and 156 patients with non-SCAs were enrolled (training set: n = 242; test set: n = 60). Tumor segmentation was manually generated using ITK-SNAP. From T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI, 2550 radiomics features were extracted using Pyradiomics. Pearson's correlation coefficient values were calculated to exclude redundant features. Several machine learning algorithms were developed to predict SCAs incorporating the radiomics and semantic features including clinical, laboratory, and radiology-associated features. The performance of models was evaluated by AUC. RESULTS: Patients in the SCA group were younger (49.5 vs 55.2 years old) and more female (85.6% vs 37.2%) than those in the non-SCA group (p < 0.001). More invasiveness (p = 0.011) and cystic and microcystic change (p < 0.001) were observed in patients with SCAs. The ensemble algorithm presented the largest AUC of 0.927 among all the algorithms trained in the test set, and the accuracy, specificity, and sensitivity of predicting SCAs were all 0.867 (at cut-off 0.5). The overall model performed better than that only using semantic features available in the clinic. Radiomics prediction was the most important feature, with gender ranking second and age ranking third. Radiomics features on T2WI were superior to those on other MR modalities in SCA prediction. CONCLUSION: Our ensemble learning model outperformed current clinical practice in differentiating patients with SCAs and non-SCAs using radiomics, which might help make appropriate treatment strategies. KEY POINTS: • Radiomics might improve the preoperative diagnosis of SCAs by MR images. • T2WI was superior to T1WI and CE-T1WI in the preoperative diagnosis of SCAs. • The ensemble machine learning model outperformed current clinical practice in SCAs diagnosis and treatment decision-making could be more individualised using the nomogram.

Geometry and physics in the deformations of crystalline caps.

Elucidating the interplay of stress and geometry is a fundamental scientific question arising in multiple fields. In this work, we investigate the geometric frustration of crystalline caps confined on the sphere in both elastic and plastic regimes. Based on the revealed quasi-conformal ordering, we discover the partial but uniform screening of the substrate curvature by the induced curvature underlying the inhomogeneous lattice. This scenario is fundamentally different from the conventional screening mechanism based on topological defects. In the plastic regime, the yield of highly stressed caps leads to fractures with featured morphologies not found in planar systems. We also demonstrate the strategy of engineering stress and fractures by vacancies. These results advance our general understanding of the organization and adaptivity of the geometrically frustrated crystalline order.

Collective dynamics and shattering of disturbed two-dimensional Lennard-Jones crystals.

Elucidating collective dynamics in crystalline systems is a common scientific question in multiple fields. In this work, by combination of high-precision numerical approach and analytical normal mode analysis, we systematically investigate the dynamical response of two-dimensional Lennard-Jones crystal as a purely classical mechanical system under random disturbance of varying strength, and reveal rich microscopic dynamics. Specifically, we observe highly symmetric velocity field composed of sharply divided coherent and disordered regions, and identify the order-disorder dynamical transition of the velocity field. Under stronger disturbance, we reveal the vacancy-driven shattering of the crystal. This featured disruption mode is fundamentally different from the dislocation-unbinding scenario in two-dimensional melting. We also examine the healing dynamics associated with vacancies of varying size. The results in this work advance our understanding about the formation of collective dynamics and crystal disruption, and may have implications in elucidating relevant non-equilibrium behaviors in a host of crystalline systems. Microscopic dynamics and underlying topological defects in the disruption of the Lennard-Jones lattice under random disturbance.

Application of Contrast-Enhanced 3-Dimensional T2-Weighted Volume Isotropic Turbo Spin Echo Acquisition Sequence in the Diagnosis of Prolactin-Secreting Pituitary Microadenomas.

OBJECTIVE: This study aimed to investigate the value of contrast-enhanced 3-dimensional (3D) T2-weighted (T2W) Volume Isotropic Turbo Spin Echo Acquisition (VISTA) sequence in prolactin-secreting pituitary adenoma diagnosis. METHODS: We enrolled 158 patients with hyperprolactinemia. Coronal dynamic contrast-enhanced (DCE) T1 spin echo and T2W VISTA sequences were performed. The detection of pituitary microadenomas in 3 imaging groups (DCE magnetic resonance imaging [MRI], VISTA, and DCE MRI + VISTA) were compared using McNemar test and χ2 test. RESULTS: The DCE MRI + VISTA group detected 28 more pituitary microlesions than DCE MRI alone. Among these, 20 lesions were clearly observed on VISTA images but were negative on DCE MRI. The combined sequences showed higher sensitivity (85.3%) and diagnostic accuracy (89.2%) for adenoma detection than any of the sequences alone (P < 0.01). We noted that in 65.7% of the patients with adenoma (46 of 70), a "hypointense rim" was present around the lesion on the VISTA images. Of them, 11 patients underwent surgery. Histopathology confirmed that the "hypointense rim" was a pseudocapsular structure at the edge of the adenoma. CONCLUSIONS: For patients with hyperprolactinemia, the 3D T2W VISTA sequence is an important supplement to DCE MRI, because it could improve the detection rate of pituitary microadenomas.

Evaluation of Inferior Intercavernous Sinus in Patients With Pituitary Microadenoma by Contrast-Enhanced SPACE Imaging.

OBJECTIVE: Inferior intercavernous sinus (iICS) is a venous channel below the pituitary gland. Inferior intercavernous sinus injury is predisposed to cause venous bleeding during dura incision in transsphenoidal surgery for pituitary adenomas. Therefore, this study aimed to perform a radiological assessment of iICS before transsphenoidal surgery for pituitary microadenoma. METHODS: A retrospective evaluation was performed on 156 patients who underwent magnetic resonance imaging examinations in our hospital before endoscopic transsphenoidal surgery for pituitary microadenoma. Both sagittal reformatted and coronal contrast-enhanced (CE) sampling perfection with application optimized contrast using different flip angle evolutions (SPACE) images were interpreted for the presence, shape, and size of the iICS. RESULTS: In CE SPACE, the iICS was identified in 72 patients (46.15%) with pituitary microadenoma. The iICS was appeared as a filiform-shaped hyperintense structure below the pituitary gland on coronal CE SPACE planes and a crescent-shaped hyperintense structure on sagittal CE SPACE planes. The mean ± SD width, depth, and height of iICS were 11.15 ± 3.47 mm, 5.29 ± 1.24 mm, and 1.41 ± 0.19 mm, respectively. CONCLUSIONS: Contrast-enhanced SPACE may serve as a promising technique in evaluating iICS and individualized preoperative planning before transsphenoidal surgery for pituitary microadenoma.

Extramedullary Plasmacytoma of the Sinonasal Cavity: Magnetic Resonance Imaging Characteristics With Readout-Segmented Diffusion-Weighted Imaging and Dual-Energy Computed Tomography Features.

PURPOSE: To determine magnetic resonance imaging (MRI) with readout-segmented diffusion-weighted imaging (RESOLVE-DWI) and dual-energy computed tomography (DECT) features of sinonasal extramedullary plasmacytoma (SN-EMP). METHODS: The MRI and/or DECT of 10 patients with SN-EMP confirmed by pathology were retrospectively reviewed. Apparent diffusion coefficient (ADC) values of RESOLVE-DWI were analyzed in 9 patients. The quantitative parameters derived from DECT, including the iodine concentration (IC), effective atomic number, and the slope (k) of spectral attenuation curve, were measured in 3 patients. RESULTS: On conventional MRI, typical lesions were well defined (7 of 9), and isointense to the brain on both T1WI and T2WI (9 of 9). Most lesions presented with marked enhancement on contrast-enhanced T1WI without significant necrosis (8 of 9). Notably, multiple flow-void signals were observed in all lesions (9 of 9). On RESOLVE-DWI, the average ADC value was 0.55 × 10-3 mm2/s, and the normalized ADC value was 0.66 ± 0.04. On DECT, the average values of IC, effective atomic number, and slope (k) was 2.7 mg/mL, 8.62, and 3.8, respectively. CONCLUSIONS: Some typical MRI features (well-defined mass, isointensity to the brain, marked enhancement without obvious cystic changes, multiple flow voids, and a lower ADC value) strongly suggest the diagnosis of SN-EMP. The quantitative parameters derived from RESOLVE-DWI and DECT may provide more information for the diagnosis of SN-EMP.

CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research.

The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18-30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.

Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema.

OBJECTIVES: To evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetric measurements of intracerebral hemorrhage (ICH), intraventricular extension of intracerebral hemorrhage (IVH), and perihematomal edema (PHE) in primary ICH on CT. METHODS: Three hundred and eighty primary ICH patients who underwent CT at hospital arrival were divided into a training cohort (n = 300) and a validation cohort (n = 80). An independent cohort with 80 patients was used for testing. Ground truth (segmentation masks) was manually generated by radiologists. Model performance on lesion segmentation and volumetric measurement of ICH, IVH, and PHE were evaluated by comparing the model results with the segmentations performed by radiologists. RESULTS: In the test cohort, the Dice scores of lesion segmentation were 0.92, 0.79, and 0.71 for ICH, IVH, and PHE, respectively. The sensitivities were 0.93 for ICH, 0.88 for IVH, and 0.81 for PHE. The positive predictive values were 0.92, 0.76, and 0.69 for ICH, IVH, and PHE, respectively. Excellent concordance (concordance correlation coefficients [CCCs] ≥ 0.98) of ICH and IVH and good concordance of PHE (CCCs ≥ 0.92) were demonstrated between manually and automatically measured volumes. The model took approximately 15 s to provide automatic segmentation and volume analysis for each patient. CONCLUSION: Our model demonstrates good reliability for automatic segmentation and volume measurement of ICH, IVH, and PHE in primary ICH, which can be useful to reduce the effort and time of doctors to calculate volumes of ICH, IVH, and PHE. KEY POINTS: • Deep learning algorithms can provide automatic and reliable assessment of intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT. • Non-contrast CT-based deep learning method can be helpful to provide efficient and accurate measurements of ICH, IVH, and PHE in primary ICH patients, thereby reducing the effort and time of doctors to segment and calculate volumes of ICH, IVH, and PHE in primary ICH patients.

Enhancement degree of brain metastases: correlation analysis between enhanced T2 FLAIR and vascular permeability parameters of dynamic contrast-enhanced MRI.

OBJECTIVES: To investigate the correlation between enhancement degrees of brain metastases on contrast-enhanced T2-fluid-attenuated inversion recovery (CE-T2 FLAIR) and vascular permeability parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS: Thirty-nine patients with brain metastases were prospectively collected. They underwent non-enhanced T2 FLAIR, DCE-MRI, CE-T2 FLAIR, and contrast-enhanced three-dimensional brain volume imaging (CE-BRAVO). Quantitative parameters of DCE-MRI were evaluated for all lesions, which included volume transfer constant (Ktrans), rate constant (Kep), and fractional volume of the extracellular extravascular space (Ve). Contrast ratio (CR) and percentage increase (PI) values of all lesions on CE-T2 FLAIR were also measured. The tumor enhancement degree on CE-T2 FLAIR in relation to CE-BRAVO was visually classified as higher (group A), equal (group B), and lower (group C). RESULTS: A total of 82 brain metastases were evaluated, including 31 in group A, 19 in group B, and 32 in group C. The Ktrans and Kep were negatively correlated with the CR (ρ = - 0.551, p < 0.001 and ρ = - 0.708, p < 0.001, respectively) and PI (ρ = - 0.511, p < 0.001 and ρ = - 0.621, p < 0.001, respectively). The Ktrans and Kep of group A were significantly lower than those of group C (both p < 0.001). No significant difference was found in Ve among the groups (p = 0.327). CONCLUSIONS: The enhancement degree of brain metastases on CE-T2 FLAIR is negatively correlated with Ktrans and Kep values, which indicate that vascular permeability parameters may play an important role in explaining the difference in enhancement between CE-T2 FLAIR and CE-BRAVO. KEY POINTS: • The enhancement degree on CE-T2 FLAIR was negatively correlated with Ktrans and Kep values. • The vascular permeability of brain metastasis accounted for the difference in enhancement degree between CE-T2 FLAIR and CE-BRAVO. • CE-T2 FLAIR is useful for detecting brain metastases with mild disruption of the blood-brain barrier.