Glioma is a primary brain tumor with high incidence rate. High-grade gliomas (HGG) are those with the highest degree of malignancy and the lowest degree of survival. Surgical resection and postoperative adjuvant chemoradiotherapy are often used in clinical treatment, so accurate segmentation of tumor-related areas is of great significance for the treatment of patients. In order to improve the segmentation accuracy of HGG, this paper proposes a multi-modal glioma semantic segmentation network with multi-scale feature extraction and multi-attention fusion mechanism. The main contributions are, (1) Multi-scale residual structures were used to extract features from multi-modal gliomas magnetic resonance imaging (MRI); (2) Two types of attention modules were used for features aggregating in channel and spatial; (3) In order to improve the segmentation performance of the whole network, the branch classifier was constructed using ensemble learning strategy to adjust and correct the classification results of the backbone classifier. The experimental results showed that the Dice coefficient values of the proposed segmentation method in this article were 0.909 7, 0.877 3 and 0.839 6 for whole tumor, tumor core and enhanced tumor respectively, and the segmentation results had good boundary continuity in the three-dimensional direction. Therefore, the proposed semantic segmentation network has good segmentation performance for high-grade gliomas lesions.
Attention ; Glioma/diagnostic imaging* ; Humans ; Magnetic Resonance Imaging/methods* ; Semantics

BACKGROUND: It is crucial to differentiate accurately glioma recurrence and pseudoprogression which have entirely different prognosis and require different treatment strategies. This study aimed to assess the diagnostic accuracy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a tool for distinguishing glioma recurrence and pseudoprogression. METHODS: According to particular criteria of inclusion and exclusion, related studies up to May 1, 2019, were thoroughly searched from several databases including PubMed, Embase, Cochrane Library, and Chinese biomedical databases. The quality assessment of diagnostic accuracy studies was applied to evaluate the quality of the included studies. By using the "mada" package in R, the heterogeneity, overall sensitivity, specificity, and diagnostic odds ratio were calculated. Moreover, funnel plots were used to visualize and estimate the publication bias in this study. The area under the summary receiver operating characteristic (SROC) curve was computed to display the diagnostic efficiency of DCE-MRI. RESULTS: In the present meta-analysis, a total of 11 studies covering 616 patients were included. The results showed that the pooled sensitivity, specificity, and diagnostic odds ratio were 0.792 (95% confidence interval [CI] 0.707-0.857), 0.779 (95% CI 0.715-0.832), and 16.219 (97.5% CI 9.123-28.833), respectively. The value of the area under the SROC curve was 0.846. In addition, the SROC curve showed high sensitivities (>0.6) and low false positive rates (<0.5) from most of the included studies, which suggest that the results of our study were reliable. Furthermore, the funnel plot suggested the existence of publication bias. CONCLUSIONS While the DCE-MRI is not the perfect diagnostic tool for distinguishing glioma recurrence and pseudoprogression, it was capable of improving diagnostic accuracy. Hence, further investigations combining DCE-MRI with other imaging modalities are required to establish an efficient diagnostic method for glioma patients.
Glioma/diagnostic imaging* ; Humans ; Magnetic Resonance Imaging ; Neoplasm Recurrence, Local/diagnostic imaging* ; ROC Curve ; Sensitivity and Specificity

Glioma is the most common malignant brain tumor and classification of low grade glioma (LGG) and high grade glioma (HGG) is an important reference of making decisions on patient treatment options and prognosis. This work is largely done manually by pathologist based on an examination of whole slide image (WSI), which is arduous and heavily dependent on doctors' experience. In the World Health Organization (WHO) criteria, grade of glioma is closely related to hypercellularity, nuclear atypia and necrosis. Inspired by this, this paper designed and extracted cell density and atypia features to classify LGG and HGG. First, regions of interest (ROI) were located by analyzing cell density and global density features were extracted as well. Second, local density and atypia features were extracted in ROI. Third, balanced support vector machine (SVM) classifier was trained and tested using 10 selected features. The area under the curve (AUC) and accuracy (ACC) of 5-fold cross validation were 0.92 ± 0.01 and 0.82 ± 0.01 respectively. The results demonstrate that the proposed method of locating ROI is effective and the designed features of density and atypia can be used to predict glioma grade accurately, which can provide reliable basis for clinical diagnosis.
Brain Neoplasms/diagnostic imaging* ; Glioma/diagnostic imaging* ; Humans ; Magnetic Resonance Imaging ; Neoplasm Grading ; Support Vector Machine

Nasal gliomas are rare benign congenital midline tumors composed of heterotopic neuroglial tissue with the potential for intracranial extension. They are commomly seen in newborns and children but rarely in adults. Preoperative diagnostic imaging is essential to delineate the exact site and extension of tumor and to plan the appropriate surgical approach. Endoscopic surgery is considered appropriate for the removal of intranasal glioma without intracranial extension. We experienced a case of nasophryngeal glioma in a newborn who had been presented with respiratory distress. The tumor was successfully removed by endoscopic surgery. So we report this rare case with the review of literatures.
Adult ; Child ; Diagnostic Imaging ; Endoscopes ; Glioma* ; Humans ; Infant, Newborn* ; Nasopharynx

Glioma is the most malignant tumor in the brain, the origin of glioma is still unknown. Recently some papers indicated that glioma may be developed from cerebral cavernous malformation (CCM). We describe a man with a right temporal lobe CCM, after gamma-knife radiotherapy, the patient developed a low-grade astrocytoma in the area of the preexistent CCM. This case, together with other reports, may indicated an oncogenetic properties of CCM, and we proposed that CCM may be a pre-glioma lesion.
Glioma ; diagnostic imaging ; pathology ; Hemangioma, Cavernous, Central Nervous System ; diagnostic imaging ; pathology ; Humans ; Male ; Radiography

Focus on the inconsistency of the shape, location and size of brain glioma, a dual-channel 3-dimensional (3D) densely connected network is proposed to automatically segment brain glioma tumor on magnetic resonance images. Our method is based on a 3D convolutional neural network frame, and two convolution kernel sizes are adopted in each channel to extract multi-scale features in different scales of receptive fields. Then we construct two densely connected blocks in each pathway for feature learning and transmission. Finally, the concatenation of two pathway features was sent to classification layer to classify central region voxels to segment brain tumor automatically. We train and test our model on open brain tumor segmentation challenge dataset, and we also compared our results with other models. Experimental results show that our algorithm can segment different tumor lesions more accurately. It has important application value in the clinical diagnosis and treatment of brain tumor diseases.
Brain Neoplasms ; diagnostic imaging ; Glioma ; diagnostic imaging ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Neural Networks (Computer)

OBJECTIVES: Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading. METHODS: Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T RESULTS: A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve ( CONCLUSIONS The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.
Brain Neoplasms/diagnostic imaging* ; Glioma/diagnostic imaging* ; Humans ; Logistic Models ; Magnetic Resonance Imaging ; ROC Curve ; Retrospective Studies

OBJECTIVE: To investigate the values of diffusion tensor imaging (DTI) and virtual reality (VR) techniques in design surgery program of gliomas near eloquent regions. METHODS: In this study, 35 cases were retrospectively analyzed with gliomas involved language areas or rolandic regions operated in Department of Neurosurgery, Peking University Third Hospital from January 2015 to January 2019. Surgery programs were performed by Dextroscope virtual reality system. The pre-operative data, such as the magnetic resonance imaging (MRI), magnetic resonance arteriography (MRA) and DTI was transferred into the VR computer for restitution,Tumors, neural fiber tracts and blood vessels were reconstructed to simulate operation and design individual surgical plan. Neurological function was evaluated 1 week, 1 month and 3 months after operation. RESULTS: Virtual reality three-dimensional images of the 35 cases were successfully achieved, including neural fiber tracts,blood vessels and the lesions. The displacement and destruction of fiber tracts, the anatomic relationship between tumor and important fiber bundle, artery and vein could be shown clearly. Surgical simulation and surgery program of VR of the 35 patients were successfully performed. The 3D images obtained from virtual reality near to the real surgery. Ten of the 35 cases were defined as rolandic regions tumors, 14 of the 35 cases were defined as language areas tumors and 11 of the 35 cases involved both language areas and rolandic regions. Complete resection of enhancing tumor (CRET) was achieved in 30 cases (85.7%), subtotal resection in 5 cases (14.3%), neurological function improved in 34 cases (97.1%) after operation,and 1 case had no improvement compared with that before(2.9%). Thirteen cases without neurological deficit pre-operation, showed transient neurological deficit ,which were recovered about 10 days post-operation, 12 of 22 cases with pre-operative neurologic deficit, improved one week postoperation, 9 of 22 cases with pre-operative neurologic deficit improved one month after operation, the rest 1 case was recurrent with glioblastoma with aggravated hemiplegia symptom after operation, who died of cerebral hernia 2 months later. CONCLUSION Dextroscope virtual reality system can clearly expose and quantify the 3D anatomic relationship of tumors, neural fiber tracts and blood vessels surrounding gliomas near eloquent regions, which is helpful to design the best individualized surgery program, to improve surgical effect.
Brain Neoplasms/diagnostic imaging* ; Diffusion Tensor Imaging ; Glioma/diagnostic imaging* ; Humans ; Imaging, Three-Dimensional ; Magnetic Resonance Imaging ; Retrospective Studies ; Virtual Reality

Objective:Gliomas are the most common neoplasm of the central nervous system (CNS); however, traditional imaging techniques do not show the boundaries of tumors well. Some researchers have found a new therapeutic mode to combine nanoparticles, which are nanosized particles with various properties for specific therapeutic purposes, and stem cells for tracing gliomas. This review provides an introduction of the basic understanding and clinical applications of the combination of stem cells and nanoparticles as a contrast agent for glioma imaging.

Data SourcesStudies published in English up to and including 2017 were extracted from the PubMed database with the selected key words of "stem cell," "glioma," "nanoparticles," "MRI," "nuclear imaging," and "Fluorescence imaging."

Study Selection:The selection of studies focused on both preclinical studies and basic studies of tracking glioma with nanoparticle-labeled stem cells.

Results:Studies have demonstrated successful labeling of stem cells with multiple types of nanoparticles. These labeled stem cells efficiently migrated to gliomas of varies models and produced signals sensitively captured by different imaging modalities.

ConclusionThe use of nanoparticle-labeled stem cells is a promising imaging platform for the tracking and treatment of gliomas.

Animals ; Contrast Media ; chemistry ; Glioma ; diagnostic imaging ; Humans ; Nanoparticles ; chemistry ; Stem Cells ; chemistry

OBJECTIVETo investigate the clinical value of CT perfusion in diagnosing and assessing intracranial neoplasms and tumor-like lesions.

METHODS16-slice helical CT perfusion imaging was performed in 56 patients who were clinically suspected to have intracranial neoplasm or tumor-like lesion. With a GE-Light Speed 16-slice helical CT scanner, routine plain-CT scanning was performed to localize the central slice of the lesion. Perfusion imaging was then carried out using cine scan technique to maintain a slice thickness of 5-10 mm, a total dose of 50-70 ml of contrast-medium at an injection flow rate of 3-5 ml/s, a delay time of 7 s and a total scan time of 50 s. The images were processed using perfusion software in an ADW 4.0 workstation, meanwhile, time-density curves (TDC) of different kinds of lesions were also produced and analyzed.

RESULTSThe pathological types in this series included: 29 gliomas (12 low-grade and 17 high-grade), 2 ependemomas, 2 hemangioblastomas, 1 medulloblastoma, 2 metastatic tumors, 1 lymphoma, 5 meningiomas, 2 schwannomas, 1 germinoma, 1 teratoma in the pineal region, 6 cavernous hemangiomas, 2 inflammatory granulomas, 1 tuberculoma, and 1 hyperplasia of the choroid plexus. TDC of high-grade glioma, low-grade glioma and meningioma was different from each other. The cerebral blood flow (CBF), cerebral blood volume (CBV), particularly, the permeability surface (PS) value of glioma was found to increase significantly with the escalation of tumor differentiation grade. In PS map, margin of the tumor could be clearly showed, which was very useful when hemorrhaging within the tumor occurred. CBF in meningioma was lower than that in high-grade glioma, but there was no statistical difference in CBV, MTT and PS between these two types of tumor. The features of intracranial cavernous hemangioma such as significant prolongation of MTT, different TDCs, and zero perfused areas were diverse on CTP image, which was helpful in differentiating it from the other lesions. The germinoma and teratoma had rather low CBF and CBV value, but a remarkably high PS value, furthermore, they showed a rapid escalated TDC with a slowly and continuously elevated platform. The perfusion features of schwannoma was concordant with its pathological findings. However, no visible specific feature of inflammatory lesion was found on CTP image in this series.

CONCLUSIONMulti-slice helical CT perfusion imaging may be helpful in revealing histopathological features and hemodynamic changes as well as differential diagnosis of intracranial neoplasms and tumor-like lesions. When combined with other image and clinical information, CTP can play an important role in pre-operative diagnosis and treatment planning for intracranial neoplasms and tumor-like lesions.

Brain ; blood supply ; Brain Neoplasms ; diagnosis ; diagnostic imaging ; Cerebrovascular Circulation ; Diagnosis, Differential ; Glioma ; diagnosis ; diagnostic imaging ; Hemangioma, Cavernous ; diagnosis ; diagnostic imaging ; Humans ; Meningeal Neoplasms ; diagnosis ; diagnostic imaging ; Meningioma ; diagnosis ; diagnostic imaging ; Reproducibility of Results ; Sensitivity and Specificity ; Tomography, Spiral Computed ; methods