Glioma survival prediction from whole-brain MRI without tumor segmentation using deep attention network: a multicenter study.

OBJECTIVES: To develop and validate a deep learning model for predicting overall survival from whole-brain MRI without tumor segmentation in patients with diffuse gliomas. METHODS: In this multicenter retrospective study, two deep learning models were built for survival prediction from MRI, including a DeepRisk model built from whole-brain MRI, and an original ResNet model built from expert-segmented tumor images. Both models were developed using a training dataset (n = 935) and an internal tuning dataset (n = 156) and tested on two external test datasets (n = 194 and 150) and a TCIA dataset (n = 121). C-index, integrated Brier score (IBS), prediction error curves, and calibration curves were used to assess the model performance. RESULTS: In total, 1556 patients were enrolled (age, 49.0 ± 13.1 years; 830 male). The DeepRisk score was an independent predictor and can stratify patients in each test dataset into three risk subgroups. The IBS and C-index for DeepRisk were 0.14 and 0.83 in external test dataset 1, 0.15 and 0.80 in external dataset 2, and 0.16 and 0.77 in TCIA dataset, respectively, which were comparable with those for original ResNet. The AUCs at 6, 12, 24, 26, and 48 months for DeepRisk ranged between 0.77 and 0.94. Combining DeepRisk score with clinicomolecular factors resulted in a nomogram with a better calibration and classification accuracy (net reclassification improvement 0.69, p < 0.001) than the clinical nomogram. CONCLUSIONS: DeepRisk that obviated the need of tumor segmentation can predict glioma survival from whole-brain MRI and offers incremental prognostic value. KEY POINTS: • DeepRisk can predict overall survival directly from whole-brain MRI without tumor segmentation. • DeepRisk achieves comparable accuracy in survival prediction with deep learning model built using expert-segmented tumor images. • DeepRisk has independent and incremental prognostic value over existing clinical parameters and IDH mutation status.

A Novel Fault Diagnosis Method for Rolling Bearing Based on Hierarchical Refined Composite Multiscale Fluctuation-Based Dispersion Entropy and PSO-ELM.

This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.

Biologic Pathways Underlying Prognostic Radiomics Phenotypes from Paired MRI and RNA Sequencing in Glioblastoma.

Background The biologic meaning of prognostic radiomics phenotypes remains poorly understood, hampered in part by lack of multicenter reproducible evidence. Purpose To uncover the biologic meaning of individual prognostic radiomics phenotypes in glioblastomas using paired MRI and RNA sequencing data and to validate the reproducibility of the identified radiogenomics linkages externally. Materials and Methods This retrospective multicenter study included four data sets gathered between January 2015 and December 2016. From a radiomics analysis set, a 13-feature radiomics signature was built using preoperative MRI for overall survival prediction. Using a radiogenomics training set with both MRI and RNA sequencing, biologic pathways were enriched and correlated with each of the 13 radiomics phenotypes. Radiomics-correlated key genes were identified to derive a prognostic radiomics gene expression (RadGene) score. The reproducibility of identified pathways and genes was validated with an external test set and a public data set (The Cancer Genome Atlas [TCGA]). A log-rank test was performed to assess prognostic significance. Results A total of 435 patients (mean age, 55 years ± 15 [standard deviation]; 263 men) were enrolled. The radiomics signature was associated with overall survival (hazard ratio [HR], 3.68; 95% CI: 2.08, 6.52; P < .001) in the radiomics validation subset. Four types of prognostic radiomics phenotypes were correlated with distinct pathways: immune, proliferative, treatment responsive, and cellular functions (false-discovery rate < 0.10). Thirty radiomics-correlated genes were identified. The prognostic significance of the RadGene score was confirmed in an external test set (HR, 2.02; 95% CI: 1.19, 3.41; P = .01) and a TCGA test set (HR, 1.43; 95% CI: 1.001, 2.04; P = .048). The radiomics-associated pathways and key genes can be replicated in an external test set. Conclusion Individual radiomics phenotypes on MRI scans predictive of overall survival were driven by distinct key pathways involved in immune regulation, tumor proliferation, treatment responses, and cellular functions in glioblastoma, which could be reproduced externally. © RSNA, 2021 Online supplemental material is available for this article.

Diagnostic performance of multiparametric MRI in the evaluation of treatment response in glioma patients at 3T.

BACKGROUND: MRI is one of the most important techniques to assess the treatment response of gliomas. However, differentiating tumor recurrence (TuR) from treatment effects (TrE) remains challenging. PURPOSE: To compare the diagnostic performance of MR diffusion-weighted imaging (DWI), arterial spin labeling (ASL), proton MR spectroscopy (MRS), and amide proton transfer (APT) imaging in differentiating between TuR and TrE in posttreatment glioma patients. STUDY TYPE: Prospective. POPULATION: Thirty patients with suspected tumor progression. FIELD STRENGTH/SEQUENCE: DWI, ASL, proton MRS, and APT imaging were performed at 3T MR. ASSESSMENT: MR indices, including ADC, relative cerebral blood flow (rCBF), ratios of Cho/Cr, Cho/NAA, and NAA/Cr and APT-weighted (APTw) effect were obtained from DWI, ASL, proton MRS, and APT imaging, respectively. Indices were measured in the contralateral normal-appearing white matter and lesions defined on the Gd-enhanced T1 w image. TuR or TrE was either determined histologically or clinically from longitudinal MRI follow-up for at least 6 months. STATISTICAL TESTS: The diagnostic performance of the indices was evaluated using Student's t-test, receiver operating characteristic (ROC) curve, and multivariate logistic regression analyses. RESULTS: Among the 30 patients, 16 were diagnosed as having TuR and the rest having TrE. The recurrent tumors showed a significantly higher APTw effect (1.56 ± 1.14%) and rCBF (1.44 ± 0.61) compared with lesions representing treatment effects (-0.44 ± 1.34% and 0.72 ± 0.25, respectively, with P < 0.001). The areas under the curve (AUCs) were 0.87 and 0.90 for APTw and rCBF, respectively, in differentiating between TuR and TrE. Combining APTw and rCBF achieved a higher AUC of 0.93. MRS index ratios of Cho/Cr (P = 0.25), Cho/NAA (P = 0.16), and NAA/Cr (P = 0.86) and ADC (P = 0.37) showed no significant differences between TuR and TrE lesions, with AUCs lower than 0.70. DATA CONCLUSION: Compared with DWI and MRS, ASL and APT imaging techniques showed better diagnostic capability in distinguishing TuR from TrE. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2020;51:1154-1161.

Prosaposin is a biomarker of mesenchymal glioblastoma and regulates mesenchymal transition through the TGF-ß1/Smad signaling pathway.

Mesenchymal glioblastoma (GBM) is the most aggressive subtype of GBM. Our previous study found that neurotrophic factor prosaposin (PSAP) is highly expressed and secreted in glioma and can promote the growth of glioma. The role of PSAP in mesenchymal GBM is still unclear. In this study, bioinformatic analysis, western blotting and RT-qPCR were used to detect the expression of PSAP in different GBM subtypes. Human glioma cell lines and patient-derived glioma stem cells were studied in vitro and in vivo, revealing that mesenchymal GBM expressed and secreted the highest level of PSAP among four subtypes of GBM, and PSAP could promote GBM invasion and epithelial-mesenchymal transition (EMT)-like processes in vivo and in vitro. Bioinformatic analysis and western blotting showed that PSAP mainly played a regulatory role in GBM invasion and EMT-like processes via the TGF-ß1/Smad signaling pathway. In conclusion, the overexpression and secretion of PSAP may be an important factor causing the high invasiveness of mesenchymal GBM. PSAP is therefore a potential target for the treatment of mesenchymal GBM. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Dual-energy spectral CT quantitative parameters for the differentiation of Glioma recurrence from treatment-related changes: a preliminary study.

BACKGROUND: Differentiating glioma recurrence from treatment-related changes can be challenging on conventional imaging. We evaluated the efficacy of quantitative parameters measured by dual-energy spectral computed tomographic (CT) for this differentiation. METHODS: Twenty-eight patients were examined by dual-energy spectral CT. The effective and normalized atomic number (Zeff and Zeff-N, respectively); spectral Hounsfield unit curve (λHU) slope; and iodine and normalized iodine concentration (IC and ICN, respectively) in the post-treatment enhanced areas were calculated. Pathological results or clinicoradiologic follow-up of ≥2 months were used for final diagnosis. Nonparametric and t-tests were used to compare quantitative parameters between glioma recurrence and treatment-related changes. Sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively), and accuracy were calculated using receiver operating characteristic (ROC) curves. Predictive probabilities were used to generate ROC curves to determine the diagnostic value. RESULTS: Examination of pre-contrast λHU, Zeff, Zeff-N, IC, ICN, and venous phase ICN showed no significant differences in quantitative parameters (P > 0.05). Venous phase λHU, Zeff, Zeff-N, and IC in glioma recurrence were higher than in treatment-related changes (P < 0.001). The optimal venous phase threshold was 1.03, 7.75, 1.04, and 2.85 mg/cm3, achieving 66.7, 91.7, 83.3, and 91.7% sensitivity; 100.0, 77.8, 88.9, and 77.8% specificity; 100.0, 73.3, 83.3, and 73.3% PPV; 81.8, 93.3, 88.9, and 93.3% NPV; and 86.7, 83.3, 86.7, and 83.3% accuracy, respectively. The respective areas under the curve (AUCs) were 0.912, 0.912, 0.931, and 0.910 in glioma recurrence and treatment-related changes. CONCLUSIONS: Glioma recurrence could be potentially differentiated from treatment-related changes based on quantitative values measured by dual-energy spectral CT imaging.

Direct radiofrequency saturation corrected amide proton transfer tumor MRI at 3T.

PURPOSE: Amide proton transfer (APT) imaging has been increasingly applied in tumor characterization that complements diffusion and dynamic contrast-enhanced MRI. However, quantification of in vivo APT effect is challenging because of concomitant semisolid magnetization transfer (MT) and nuclear overhauser enhancement effects. A direct saturation corrected (DISC) chemical exchange saturation transfer (CEST) analysis has been recently proposed that simplifies the determination of in vivo CEST effects. Our present study aimed to extend the DISC analysis to pulsed radiofrequency CEST MRI and evaluate it at 3T. METHODS: Nine adult male Sprague-Dawley rats implanted C6 gliomas underwent multiparametric MRI of T1 , T2 , CEST, and T1 -weighted gadolinium-enhanced imaging 1 day before and 3 days after chemoradiotherapy. The routine MT asymmetry, 3-point method, and the extended DISC analysis were compared in tumor characterization with histology as a reference. Regional variations were assessed by 1-way analysis of variance. RESULTS: T1 , T2 , and MT asymmetry and the DISC CEST effects showed significant alterations in tumor/necrosis with respect to the contralateral reference (P < 0.05). The resolved APT effect revealed a significant difference among the contralateral reference (2.42 ± 0.24%), necrosis (2.86 ± 0.19%), and tumor (3.25 ± 0.15%) regions after chemoradiotherapy (P < 0.05), consistent with histological observations. Conversely, the MT asymmetry did not show tumor regional variation post-treatment (P > 0.05), whereas the 3-point method detected no regional alteration at both time points (P > 0.05). CONCLUSION: Our study translated DISC CEST MRI to 3T, evaluated it in glioma rat models, and confirmed its advantages in resolving tumor heterogeneity over the routine asymmetry and 3-point analyses.

Clinical significance of the histological and molecular characteristics of ependymal tumors: a single institution case series from China.

BACKGROUND: Ependymal tumors are pathologically defined intrinsic neoplasms originating in the intracranial compartments or the spinal cord that affect both children and adults. The recently integrated classification of ependymomas based on both histological and molecular characteristics is capable of subgrouping patients with various prognoses. However, the application of histological and molecular markers in Chinese patients with ependymomas has rarely been reported. We aimed to demonstrate the significance of histological characteristics, the v-relavian reticuloendotheliosis viral oncogene homolog A (RELA) fusions and other molecular features in ependymal tumors. METHODS: We reviewed the histological characteristics of ependymal tumors using conventional pathological slides and investigate the RELA fusions and Cylclin D1 (CCND1) amplification by Fluorescence in situ hybridization (FISH) and trimethylation of histone 3 lysine 27 (H3K27me3) expression by immunohistochemistry (IHC) methods. SPSS software was used to analyze the data. RESULTS: We demonstrated that hypercellularity, atypia, microvascular proliferation, necrosis, mitosis, and an elevated Ki-67 index, were tightly associated with an advanced tumor grade. Tumor location, necrosis, mitosis and the Ki-67 index were related to the survival of the ependymomas, but Ki67 was the only independent prognostic factor. Additionally, RELA fusions, mostly presented in pediatric grade III intracranial ependymomas, indicated decreased survival times of patients, and closely related to the patients' age, tumor grade, cellularity, cellular atypia, necrosis and Ki67 index in the intracranial ependymal tumors, whereas reduction of H3K27me3 predicted the worse prognosis in ependymal tumors. CONCLUSIONS: Histological and molecular features facilitate tumor grading and prognostic predictions for ependymal tumors in Chinese patients.

Survival impacts of extent of resection and adjuvant radiotherapy for the modern management of high-grade meningiomas.

PURPOSE: We aim to investigate the impacts of extent of resection and adjuvant radiotherapy on survival of high-grade meningiomas (WHO grade II-III) according to modern diagnosis and management. METHODS: Patients with high-grade meningiomas were identified in the Surveillance Epidemiology and End Results (SEER) database between 2000 and 2015 and used for survival analysis. Propensity score matching (PSM) was conducted to reduce selection bias. Another 92 patients from Sun Yat-sen University Cancer Center (SYSUCC) were used for validation. RESULTS: 530 patients were enrolled from SEER. Patients with gross total resection (GTR) had no significantly different overall survival (OS) compared with those with subtotal resection (STR), even after performing PSM between these two groups. Multivariable analysis found that age ≥ 65 years (HR 2.22, P < 0.001), tumor diameter > 6 cm (HR 1.59, P = 0.004) and grade III tumor (HR 4.31, P < 0.001) were associated with worse OS. Stratification analysis showed that adjuvant radiotherapy conferred significantly improved OS for grade III meningiomas, but not for grade II meningiomas, regardless of resection extent. In SYSUCC cohort, resection extent was also not significantly associated with OS. However, patients with GTR (Simpson grade I-III) had distinctly increased progression-free survival (PFS) than those with STR (P < 0.001). Additionally, for grade II meningiomas after GTR, radiotherapy was unable to improve OS and PFS. CONCLUSION: On modern management of high-grade meningiomas, GTR does not improve OS, but seems to be associated with increased PFS. Radiotherapy is reasonable as a supplement for treating grade III meningiomas, whereas its effect for grade II meningiomas remains uncertain and needs further validation by prospective study.

Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine.

The health state of rotating machinery directly affects the overall performance of the mechanical system. The monitoring of the operation condition is very important to reduce the downtime and improve the production efficiency. This paper presents a novel rotating machinery fault diagnosis method based on the improved multiscale amplitude-aware permutation entropy (IMAAPE) and the multiclass relevance vector machine (mRVM) to provide the necessary information for maintenance decisions. Once the fault occurs, the vibration amplitude and frequency of rotating machinery obviously changes and therefore, the vibration signal contains a considerable amount of fault information. In order to effectively extract the fault features from the vibration signals, the intrinsic time-scale decomposition (ITD) was used to highlight the fault characteristics of the vibration signal by extracting the optimum proper rotation (PR) component. Subsequently, the IMAAPE was utilized to realize the fault feature extraction from the PR component. In the IMAAPE algorithm, the coarse-graining procedures in the multi-scale analysis were improved and the stability of fault feature extraction was promoted. The coarse-grained time series of vibration signals at different time scales were firstly obtained, and the sensitivity of the amplitude-aware permutation entropy (AAPE) to signal amplitude and frequency was adopted to realize the fault feature extraction of coarse-grained time series. The multi-classifier based on the mRVM was established by the fault feature set to identify the fault type and analyze the fault severity of rotating machinery. In order to demonstrate the effectiveness and feasibility of the proposed method, the experimental datasets of the rolling bearing and gearbox were used to verify the proposed fault diagnosis method respectively. The experimental results show that the proposed method can be applied to the fault type identification and the fault severity analysis of rotating machinery with high accuracy.

A Low-Temperature Micro Hotplate Gas Sensor Based on AlN Ceramic for Effective Detection of Low Concentration NO2.

Air pollution is one of the major threats to human health. The monitoring of toxic NO2 gas in urban air emission pollution is becoming increasingly important. Thus, the development of an NO2 sensor with low power consumption, low cost, and high performance is urgent. In this paper, a planar structural micro hotplate gas sensor based on an AlN ceramic substrate with an annular Pt film heater was designed and prepared by micro-electro-mechanical system (MEMS) technology, in which Pt/Nb/In2O3 composite semiconductor oxide was used as the sensitive material with a molar ratio of In:Nb = 9:1. The annular thermal isolation groove was designed around the heater to reduce the power consumption and improve the thermal response rate. Furthermore, the finite element simulation analysis of the thermal isolation structure of the sensor was carried out by using ANSYS software. The results show that a low temperature of 94 °C, low power consumption of 150 mW, and low concentration detection of 1 to 10 ppm NO2 were simultaneously realized for the Nb-doped In2O3-based gas sensor. Our findings provide a promising strategy for the application of In2O3-based sensors in highly effective and low concentration NO2 detection.

Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.

OBJECTIVES: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. KEY POINTS: • 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.

Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features.

PURPOSE OF THE STUDY: 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. MATERIALS AND METHODS: 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. RESULTS: Among 16,384 high-throughput features, 1356 features show significant differences between PCNSL and GBM with p < 0.05 and 420 features with p < 0.001. 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). CONCLUSIONS: 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.

Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array.

As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH4 as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.

Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making.

Metal Oxide Semiconductor (MOS) gas sensor has been widely used in sensor systems for the advantages of fast response, high sensitivity, low cost, and so on. But, limited to the properties of materials, the phenomenon, such as aging, poisoning, and damage of the gas sensitive material will affect the measurement quality of MOS gas sensor array. To ensure the stability of the system, a health management decision strategy for the prognostics and health management (PHM) of a sensor system that is based on health reliability degree (HRD) and grey group decision-making (GGD) is proposed in this paper. The health management decision-making model is presented to choose the best health management strategy. Specially, GGD is utilized to provide health management suggestions for the sensor system. To evaluate the status of the sensor system, a joint HRD-GGD framework is declared as the health management decision-making. In this method, HRD of sensor system is obtained by fusing the output data of each sensor. The optimal decision-making recommendations for health management of the system is proposed by combining historical health reliability degree, maintenance probability, and overhaul rate. Experimental results on four different kinds of health levels demonstrate that the HRD-GGD method outperforms other methods in decision-making accuracy of sensor system. Particularly, the proposed HRD-GGD decision-making method achieves the best decision accuracy of 98.25%.

[Primary central nervous system lymphoma and glioblastoma image differentiation based on sparse representation system].

It is of great clinical significance in the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) because there are enormous differences between them in terms of therapeutic regimens. In this paper, we propose a system based on sparse representation for automatic classification of PCNSL and GBM. The proposed system distinguishes the two tumors by using of the different texture detail information of the two tumors on T1 contrast magnetic resonance imaging (MRI) images. First, inspired by the process of radiomics, we designed a dictionary learning and sparse representation-based method to extract texture information, and with this approach, the tumors with different volume and shape were transformed into 968 quantitative texture features. Next, aiming at the problem of the redundancy in the extracted features, feature selection based on iterative sparse representation was set up to select some key texture features with high stability and discrimination. Finally, the selected key features are used for differentiation based on sparse representation classification (SRC) method. By using ten-fold cross-validation method, the differentiation based on the proposed approach presents accuracy of 96.36%, sensitivity 96.30%, and specificity 96.43%. Experimental results show that our approach not only effectively distinguish the two tumors but also has strong robustness in practical application since it avoids the process of parameter extraction on advanced MRI images.

Low preoperative prognostic nutritional index predicts poor survival in patients with newly diagnosed high-grade gliomas.

Preoperative prognostic nutritional index (PNI) has been widely demonstrated to predict survival of patients with malignant tumors. Its utility in predicting outcomes in patients with high-grade gliomas (HGG) remains undefined. A retrospective study of 188 HGG patients was conducted. An optimal PNI cut-off value was applied to stratify patients into high PNI (≥52.55, n = 78) and low PNI (<52.55, n = 110) groups. Univariate and multivariate analysis was performed to identify prognostic factors associated with overall survival (OS) and progression free survival (PFS). The resulting prognostic models were externally validated using a demographic-matched cohort of 130 HGG patients. In the training set, PNI value was negatively correlated with age (p = 0.027) and tumor grade (p = 0.048). Both PFS (8.27 vs. 20.77 months, p < 0.001) and OS (13.57 vs. 33.23 months, p < 0.001) were significantly worse in the low PNI group. Strikingly, patients in high PNI group had a 52% decrease in the risk of tumor progression and 55% decrease of death relative to low PNI. Multivariate analysis further demonstrated PNI as an independent predictor for PFS (HR = 0.62, 95% CI 0.43-0.87) and OS (HR = 0.56, 95% CI 0.38-0.80). The PNI retained independent prognostic value in the validation set for both PFS (p = 0.013) and OS (p = 0.003). On subgroup analysis by tumor grade and treatment modalities, both PFS and OS were better for the patients with high PNI. The PNI is a potentially valuable preoperative marker for the survival of patients following HGG resection.

Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays.

The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

A new aurone glycoside from Veratrum dahuricum (Turcz.) Loes. f.

To study the chemical constituents of Veratrum dahuricum (Turcz.) Loes. f., a new aurone glycoside named as (Z)-7, 4'-dimethoxy-6-hydroxyl-aurone-4-O-ß-glucopyranoside was isolated from the 95% ethanol extracts of the rhizomes and roots of Veratrum dahuricum (Turcz.) Loes. f. by repeated column chromatography on silica gel and recrystallization. Its structure was established by extensive spectroscopic analyses, and its cytotoxicities against HepG-2, MCF7 and A549 cell lines were measured in vitro.