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Brain tumors are the most common malignant neurologic tumors with the highest mortality and disability rate. Because of the delicate structure of the brain, the clinical use of several commonly used biopsy diagnosis is limited for brain tumors. Radiomics is an emerging technique for noninvasive diagnosis based on quantitative medical image analyses. However, current radiomics techniques are not standardized regarding feature extraction, feature selection, and decision making. In this paper, we propose a sparse representation-based radiomics (SRR) system for the diagnosis of brain tumors. First, we developed a dictionary learning- and sparse representation-based feature extraction method that exploits the statistical characteristics of the lesion area, leading to fine and more effective feature extraction compared with the traditional explicitly calculation-based methods. Then, we set up an iterative sparse representation method to solve the redundancy problem of the extracted features. Finally, we proposed a novel multi-feature collaborative sparse representation classification framework that introduces a new coefficient of regularization term to combine features from multi-modal images at the sparse representation coefficient level. Two clinical problems were used to validate the performance and usefulness of the proposed SRR system. One was the differential diagnosis between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), and the other was isocitrate dehydrogenase 1 estimation for gliomas. The SRR system had superior PCNSL and GBM differentiation performance compared with some advanced imaging techniques and yielded 11% better performance for estimating IDH1 compared with the traditional radiomics methods.

Background The pineal gland has been demonstrated to be involved in the development of mood and psychotic disorders. However, few studies have looked at the relationship between pineal region tumors and psychiatric disorders. Intracranial epidermoid cysts usually arise in the cerebellopontine angle area and are extremely rare in the pineal region. The case of pineal epidermoid cyst presenting as schizophrenia has never been reported before. Case Presentation We described the case of a 23-year-old man who presented to the hospital with symptoms suggestive of schizophrenia. During work-up, he was found to have a pineal lesion on brain magnetic resonance imaging. Total resection of the tumor was subsequently performed, and pathology confirmed an epidermoid cyst. One month after surgery, the patient's psychotic symptoms significantly improved free of drug and fully returned to work 3 months postoperatively. Conclusions This case highlights the importance of including mass lesions of the pineal region in the differential diagnosis of psychotic disorders. It also provides further support that the pineal region may play a role in the pathophysiology of psychiatric diseases, although more studies will be needed to elucidate this interesting connection.

Due to the totally different therapeutic regimens needed for primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), accurate differentiation of the two diseases by noninvasive imaging techniques is important for clinical decision-making.Thirty cases of PCNSL and 66 cases of GBM with conventional T1-contrast magnetic resonance imaging (MRI) were analyzed in this study. Convolutional neural networks was used to segment tumor automatically. A modified scale invariant feature transform (SIFT) method was utilized to extract three-dimensional local voxel arrangement information from segmented tumors. Fisher vector was proposed to normalize the dimension of SIFT features. An improved genetic algorithm (GA) was used to extract SIFT features with PCNSL and GBM discrimination ability. The data-set was divided into a cross-validation cohort and an independent validation cohort by the ratio of 2:1. Support vector machine with the leave-one-out cross-validation based on 20 cases of PCNSL and 44 cases of GBM was employed to build and validate the differentiation model.Among 16,384 high-throughput features, 1356 features show significant differences between PCNSL and GBM with p0.05 and 420 features with p0.001. A total of 496 features were finally chosen by improved GA algorithm. The proposed method produces PCNSL vs. GBM differentiation with an area under the curve (AUC) curve of 99.1% (98.2%), accuracy 95.3% (90.6%), sensitivity 85.0% (80.0%) and specificity 100% (95.5%) on the cross-validation cohort (and independent validation cohort).Since the local voxel arrangement characterization provided by SIFT features, proposed method produced more competitive PCNSL and GBM differentiation performance by using conventional MRI than methods based on advanced MRI.

To build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM). In this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors. The radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis. Radiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features. • Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts. • All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features. • Combing clinical factors with radiomics features did not benefit the prediction performance.

Glioblastoma is a highly invasive and vascularized primary CNS tumor. Although anti-angiogenesis therapy was effective in some tumors but not in GBM. Vasculogenic mimicry (VM), a vessel-like structure formed by highly invasive tumor cells, has been considered responsible for the failure of GBM patients for anti-angiogenesis therapy. Tenascin-c (TNC), an extracellular protein involves in tumor angiogenesis, is overexpressed in GBM. However, how TNC contributes to VM formation remains unclear. We first knocked down the expression of TNC in two glioma cell lines (U251 and A172), which had TNC overexpression. Then the formations of VM were observed by three-dimensional culture. Transwell and wound-healing assay were applied to investigate the role of TNC in cell invasion and migration abilities. The expression of MMP2, MMP9 and the phosphorylation status of AKT were determined by western-blot, RT-qPCR and gelatin zymography. Furthermore, AKT inhibitor, MK-2206 was used to block the AKT/MMP2/MMP9 signaling pathway to investigate its role in TNC regulation at different concentration (0µm, 5µm, 10µm and 20µm) for 24 hours. The knockdown efficiency was detected by western blot and RT-qPCR in U251 and A172 after siRNA transfection for 48 hours. Three-dimensional culture showed that VM-like structure formation decreased in knockdown groups compared to controls dramatically (19.67 ± 6.65 and 46.34 ± 9.10 vs. 125.67 ± 8.34 p

The accuracy and reliability of multifunctional sensor outputs directly influence the running state and performance of measurement and control systems in chemical processes. Given their importance, self-validating multifunctional sensors are presented to improve the reliability of measurements in operation. A novel strategy based on the grey bootstrap method (GBM) is proposed for the online data validation and dynamic uncertainty estimation of self-validating multifunctional sensors. The data validation algorithm and the working principle based on GBM are applied for multiple faults detection, isolation and recovery (FDIR). The proposed FDIR scheme can simultaneously isolate multiple faults of multifunctional sensors and accomplish failure recovery with high accuracy and good timeliness. Moreover, it has a good performance of discriminating between fault-free signals with sudden changes and undoubted faults. On account of the unknown probability distribution and small sample size, the traditional expression of uncertainty has limitation in dynamic measurements. As a data-driven method, the GBM can evaluate the measurement uncertainty from poor information without prior information about the probability distribution of measurand in real-time. The performance of the proposed strategy is verified by computer simulations and a real experimental system of chemical gas concentration monitoring. Through the comparison of different methods, the results show that the GBM has superiority for the data validation and dynamic uncertainty estimation of self-validating multifunctional sensors.

In fully-automatic radiomics model for predicting overall survival (OS) of glioblastoma multiforme (GBM) patients, the effect of image standardization parameters such as voxel size, quantization method and gray level on model reproducibility and prognostic performance are still unclear. In this study, 45792 multiregional radiomics features were automatically extracted from multi-modality MR images with different voxel sizes, quantization methods, and gray levels. The feature reproducibility and prognostic performance were assessed. Multiparametric and fixed-parameter radiomics signatures were constructed based on a training cohort (60 patients). In an independent validation cohort (32 patients), the multiparametric signature achieved better performance for OS prediction (C-Index = 0.705, 95% CI: 0.672, 0.738) and significant stratification of patients into high- and low-risk groups (P = 0.0040, HR = 3.29, 95% CI: 1.40, 7.70), which outperformed the fixed-parameter signatures and conventional factors such as age, Karnofsky Performance Score and tumor volume. This study demonstrated that voxel size, quantization method and gray level had influence on reproducibility and prognosis of radiomics features for GBM OS prediction. An automatic method to determine the optimal parameter settings was provided. It indicated that multiparametric radiomics signature had the potential of offering better prognostic performance than fixed-parameter signatures.

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P

This paper identifies a MR imaging radiomics signature for prediction of overall survival (OS) in patients with glioblastoma multiforme (GBM). A fully-automatic radiomics model is presented, including automatic tumor segmentation, high-throughput features extraction, features selection, and multi-feature signature identification. The automatic GBM segmentation method employs a random forest classifier with a CRF spatial regulation where the importances of the multi-modality features are considered. After feature selection, a 4-feature radiomics signature is identified based on training data and further confirmed on independent validation data. The proposed signature succeeds to stratify patients into prognostically high-risk and low-risk groups, indicating the potential to facilitate the preoperative patient care of GBM patients.

Magnetic resonance amide proton transfer (APT) imaging has proved its potential for detecting tumors and evaluating treatment response by depicting chemical exchange saturation transfer effect between the endogenous protein/peptide amide proton and bulk water. However, conventional asymmetry analysis for APT effect measurement is susceptible to concomitant contributions, particular semisolid magnetic transfer (MT) and nuclear overhauser effect. In this study, dominant saturation transfer (ST) effects, including direct water saturation and MT, were estimated from a sum of two Lorentzian functions. APT effect was then quantified by subtracting the Z-spectrum with the two ST effects. Feasibility of the method in the assessment of glioma therapeutic response was investigated in a rodent model at 3 Tesla.

This paper proposes a fully-automatic method for extraction of high-throughput MRI radiomics features in Glioblastoma Multiforme (GBM). First, the tumor subregions are automatically segmented from multi- modality MR images using a proposed random forest classifier with a conditional random field spatial regulation, where the importances of the multi-modality features are considered. Within the segmented subregions, 44928 high-order texture features are extracted at different voxel sizes by using different quantization methods with varying gray levels. The DICE score, sensitivity and specificity of the proposed automatic segmentation algorithm are calculated. The reproducibility of the extracted features against the changes in voxel sizes, quantization methods and gray levels are assessed quantitatively. The proposed radiomics method can be used for exploring of imaging biomarker and facilitate the pre-treatment care of GBM patients.

Machine Olfaction is a bionic detection technique that adopts electronic device to simulate biological olfactory system. As an information acquisition device, MOS gas sensor array is an important component of machine olfactory system. In this paper, a novel gas concentration estimation method based on multivariate relevance vector machine (MVRVM) is proposed for mixed gas detection by using MOS gas sensor array. The proposed method utilizes the excellent multivariate regression analysis performance of MVRVM to estimate any one analyte concentration in the mixed gas. To demonstrate the performance of proposed gas concentration estimation method, a machine olfactory system is designed and implemented. Gas concentration experiments with methane and carbon monoxide gas mixture as the research object were performed in the above machine olfactory system. Experimental results show that the proposed method can provide a good solution for mixed gas concentration estimation in the aspects of estimated accuracy and computational complexity.

The vibration signals of rolling bearing have the characteristics of nonlinearity and nonstationarity, in which a large number of working condition informations on rolling bearing are contained. By using vibration signals of rolling bearing, a novel rolling bearing fault diagnosis method based on ensemble empirical mode decomposition and permutation entropy (EEMD-PE) coupled with multiclass relevance vector machines (M-RVM) is proposed in this paper. The proposed method can decompose the vibration signals of rolling bearing to a series of intrinsic mode functions (IMFs) that contain different fault features of rolling bearing by using the self-adaptive decomposition ability of EEMD. Permutation entropy (PE) is used to extract the feature of each IMF and the fault feature vector of vibration singal is constituted by the PE values of IMFs. The M-RVM classifier is built by the feature vectors extracted from rolling bearing vibration samples in different fault modes. Moreover, the multiple faults identification result can be implemented by the MRVM classifier in the form of probability output. The experimental results demonstrate that the proposed fault diagnosis method is capable of effectively extracting fault features in vibration signal and improving the fault identification accuracy compared with the existing bearing fault diagnosis methods.

The outputs of Metal Oxide Semiconductor (MOS) gas sensors drift due to the change of temperature and humidity in the environment. This phenomenon leads to additional errors in the measurement and the test precision and measurement stability of gas sensor are greatly affected. A novel strategy for temperature and humidity compensation for MOS Gas Sensor is proposed in this paper. The environmental gas concentrations are measured separately and accurately based Random Forest (RF) method to demonstrate that the proposed strategy is superior at both accuracy and runtime compared with the conventional methods, such as RBF neural network and BP neural network. Results show that the proposed methodology provides a better solution to temperature and humidity drift. The accuracy of the environmental gas sensor array improves about 1%.

The metal oxide semiconductor (MOS) gas sensor plays an important role in the machine olfactory system, and the accuracy of the measured value affects the performance of the system. Because of the material characteristics of MOS gas sensors, the sensors are prone to be faulty under the condition of long-time working. Therefore, it is necessary to identify the faults of MOS gas sensors online to improve the maintainability and reliability of the machine olfactory system during the measuring process. The self-validating technology can improve the reliability of sensors. Combining with self-validating sensor technology, a fault diagnosis method for the MOS gas sensor based on t-distribution Stochastic Neighbor Embedding (t-SNE) and random forest (RF) is proposed in this article. The trailing effect of t-SNE is used to enhance the separability of the extracted fault features, and the fault feature set is utilized to construct a RF multifault classifier. To verify the effectiveness of the proposed method, a gas monitoring experimental system is designed and developed to obtain sufficient experimental samples and compose simulation data of different fault types. The simulation experimental result shows that compared with the other methods, the proposed method has higher fault diagnosis accuracy, which is up to 99.78%.

Because of the low accuracy of the current machine olfactory algorithms in detecting two mixed gases, this study proposes a hybrid gas detection algorithm based on an extreme random tree to greatly improve the classification accuracy and time efficiency. The method mainly uses the dynamic time warping algorithm (DTW) to perform data pre-processing and then extracts the gas characteristics from gas signals at different concentrations by applying a principal component analysis (PCA). Finally, the model is established by using a new extreme random tree algorithm to achieve the target gas classification. The sample data collected by the experiment was verified by comparison experiments with the proposed algorithm. The analysis results show that the proposed DTW algorithm improves the gas classification accuracy by 26.87%. Compared with the random forest algorithm, extreme gradient boosting (XGBoost) algorithm and gradient boosting decision tree (GBDT) algorithm, the accuracy rate increased by 4.53%, 5.11% and 8.10%, respectively, reaching 99.28%. In terms of the time efficiency of the algorithms, the actual runtime of the extreme random tree algorithm is 66.85%, 90.27%, and 81.61% lower than that of the random forest algorithm, XGBoost algorithm, and GBDT algorithm, respectively, reaching 103.2568 s.

In this paper, a robust guaranteed cost controller (RGCC) is proposed for quadrotor UAV system with uncertainties to address set-point tracking problem. A sufficient condition of the existence for RGCC is derived by Lyapunov stability theorem. The designed RGCC not only guarantees the whole closed-loop system asymptotically stable but also makes the quadratic performance level built for the closed-loop system have an upper bound irrespective to all admissible parameter uncertainties. Then, an optimal robust guaranteed cost controller is developed to minimize the upper bound of performance level. Simulation results verify the presented control algorithms possess small overshoot and short setting time, with which the quadrotor has ability to perform set-point tracking task well.

The traditional gas sensor array has been viewed as a simple apparatus for information acquisition in chemosensory systems. Gas sensor arrays frequently undergo impairments in the form of sensor failures that cause significant deterioration of the performance of previously trained pattern recognition models. Reliability monitoring of gas sensor arrays is a challenging and critical issue in the chemosensory system. Because of its importance, we design and implement a status self-validating gas sensor array prototype to enhance the reliability of its measurements. A novel fault detection, isolation, and diagnosis (FDID) strategy is presented in this paper. The principal component analysis-based multivariate statistical process monitoring model can effectively perform fault detection by using the squared prediction error statistic and can locate the faulty sensor in the gas sensor array by using the variables contribution plot. The signal features of gas sensor arrays for different fault modes are extracted by using ensemble empirical mode decomposition (EEMD) coupled with sample entropy (SampEn). The EEMD is applied to adaptively decompose the original gas sensor signals into a finite number of intrinsic mode functions (IMFs) and a residual. The SampEn values of each IMF and the residual are calculated to reveal the multi-scale intrinsic characteristics of the faulty sensor signals. Sparse representation-based classification is introduced to identify the sensor fault type for the purpose of diagnosing deterioration in the gas sensor array. The performance of the proposed strategy is compared with other different diagnostic approaches, and it is fully evaluated in a real status self-validating gas sensor array experimental system. The experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID of status self-validating gas sensor arrays.

This paper proposed a radiomics model from magnetic resonance imaging (MRI) for Glioblastoma Multiforme (GBM) patients. One challenge of radiomics study is to reduce the redundancy of the features. Totally 466 radiomics features were extracted from automatically segmented tumors from T1, T1 contrast, T2, and FLAIR MRIs. The consensus clustering method was used and 10 feature clusters were obtained. All clusters had a prognostic association with survival, where three clusters had a mean C-index \(\ge \)0.60. The medoid features in each clusters with highest C-index were selected as radiomics signature candidates. The maximum and mean C-indices of the medoids are 0.75 and 0.68. The results demonstrated that the clusters reduced the data redundancy as well as generated clinical relevant radiomics features.

BACKGROUND AND OBJECTIVE: We have found, in previous study, there were four types of microcirculation pattern in human glioblastoma: endothelium dependent vessels, tumor cell dependent vessels, extracellular matrix (ECM) dependent vessels, and mosaic vessels. This study is, by utilizing live cell imaging technique, to visualize the differentiation of glioma stem cells (GSCs) into endothelial cells. METHODS: Live cell imaging was used to record the transforming process of GSCs. The immunofluorescence staining was also performed to evaluate the differentiation phenomenon during GSCs three dimensional culture at time points of 0 hour, 2 hours and 6 hours. RESULTS: Tubular structures were observed in GSC-1 three dimensional culture, which was derived from a GBM patient. Some GSC-1 cells were found endothelial maker CD34 expression, and lost stem phenotype following three dimensional culture. The number of CD34+ cells were increased, and reach to highest up to 6-hour three dimensional culture (P < 0.05). Two main phenomenon were recorded by live cell imaging: (1) the number of CD34+ cells was gradually increasing following GSCs three dimensional culture; (2) The process of a single GSC-1 cell from CD34- to CD34+ . CONCLUSIONS: The result of live cell imaging has confirmed the results from conventional immunofluorescence staining that glioma stem cell may differentiate into endothelial cells, suggesting that GSCs could contribute to vasculargenesis in GBM.

Background: Vasculogenic mimicry (VM) describes the functional plasticity of aggressive tumor cells to form newly recognized microcirculation, lined by tumor cells rather than endothelial cells, in solid tumors. The presence of VM in glioma is significantly associated with high tumor grade and poor prognosis. However, whether VM is a regular feature or only a temporary phenomenon during glioma growth is unknown. This study was designed to observe VM during the growth of subcutaneous and orthotopic xenograft glioma in Balb/c nude mice. Methods: The human glioblastoma cell line (U87) was used as xenografts in Balb/c nude mice models. The xenografts were obtained at different stages of tumor growth, and evaluated for VM and endothelium-dependent vessels by dual staining for endothelial marker CD34 and periodic acid-Schiff (PAS). Results: It was found that the PAS-positive patterns which were identified as VM were persistent during tumor growth of both subcutaneous and orthotropic xenografts. Further analysis showed that the microvessel density (MVD) of endothelium-dependent vessels was positively correlated with the tumor size of subcutaneous xenograft (r = 0.406, P = 0.009), while no significant correlation was found between VM density (VMD) and the tumor size (r = 0.107, P = 0.512). Furthermore, VMD was negatively correlated with MVD (r = −0.404, P = 0.010). Conclusion: The study results revealed that both VM and endothelium-dependent vessels coexist persistently during glioblastoma xenograft growth, indicating that VM may complement microcirculation in gliomas.

BACKGROUND: Vasculogenic mimicry(VM) has been reported in many malignant human tumors, including gliomas. In the present study, we have found four types of microcirculation vessels in human glioblastomas. Method and material: Thirty-seven human glioblastoma samples were examined for VM by CD34 and PAS dual staining,as well as immunofluorescence dual staining for CD34 and GFAP. The GFAP positive (GFAP+) cells were considered as glioma cells. Even though CD34 positive (CD34+) was endothelial cells, but both CD34 + / GFAP+ should be also considered as glioma cells. Result: CD34 and PAS dual staining revealed 4 types of microcirculation vessels in human glioblastoma samples: endothelium dependent vessels, tumor cell dependent vessels, extracellular matrix (ECM) dependent vessels, and mosaic vessels. While immunofluorescence study confirmed VM should be vessels lined with GFAP+ cells (could be CD34- or co-express CD34). Twelve samples were found VM by CD34 and PAS dual staining (32%), while 20 samples were revealed VM by immunofluorescence dual staining (57%). CONCLUSION: Our results suggest that glioblastoma cells have potential to express endothelial cell marker CD34. And immunofluorescence dual staining is a more sensitive as well accurate technique revealing VM vessels in glioblastoma.

Background In this study, we assessed the prognostic value of the lymph node ratio (LNR), established a hypothetical tumor–ratio–metastasis (TRM) staging system and compared it with the 7th edition International Union Against Cancer pathological N (pN) and tumor–node–metastasis (TNM) system. Patients and methods A total of 1343 gastric cancer patients undergoing D2 resection were staged using the TRM staging system and the 7th edition TNM system. Optimal cut points of LNR were calculated using X-tile software and validated by bootstrapping. Homogeneity, discriminatory ability, and monotonicity of gradients of the TRM and TNM systems were compared using linear trend χ2, likelihood ratio χ2 statistics, and Akaike information criterion (AIC) calculations. Results Optimal cut points classified patients into LNR0 (0%), LNR1 (1%–30%), LNR2 (31%–60%), and LNR3 (61%–100%) groups. In univariate, multivariate and stratified analyses, the LNR staging showed superiority to the 7th edition pN staging. The TRM staging system had higher linear trend and likelihood ratio χ2 scores and smaller AIC values compared with those for the TNM system, which represented the optimum prognostic stratification. Conclusions The novel TRM staging system predicts survival of gastric cancer more accurately than the 7th edition TNM system. It may be considered as an alternative to TNM system.

Glioblastoma is one of the most angiogenic human tumors and characterized by microvascular proliferations. A better understanding of glioblastoma vasculature is needed to optimize anti-angiogenic therapy that has shown a promising but incomplete efficacy. The present study examined 48 glioblastomas by CD34 endothelial marker periodic acid-Schiff (PAS) dual staining and found non-endothelial cell-lined blood vessels that were formed by tumor cells (vasculogenic mimicry, VM) existing in a fraction of these tumors. We hypothesized that CD133-positive glioblastoma stem-like cells (GSCs) may play a pivotal role in glioblastoma VM formation and then demonstrated in vitro and in vivo that a subset of GSCs were capable of vasculogenesis. Moreover, we found that several growth factors involved in normal angiogenesis were expressed in GSCs. We describe here a new mechanism of alternative glioblastoma vascularization and open a new perspective for the anti-vascular treatment strategy.