Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma.

BACKGROUND: To establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngeal carcinoma (NPC) patients. METHODS: The cutoff time was used to randomly assign 219 consecutive patients who underwent chemoradiation treatment to the training group (n = 154) or the validation group (n = 65). Pretreatment multiparametric magnetic resonance (MR) images of individuals with NPC were employed to extract 428 radiomic features. LASSO regression analysis was used to select radiomic features related to early rapid metastasis and develop the Rad-score. Blood indicators were collected within 1 week of pretreatment. To identify independent risk variables for early rapid metastasis, univariate and multivariate logistic regression analyses were employed. Finally, multivariate logistic regression analysis was applied to construct a radiomics and clinical prediction nomogram that integrated radiomic features and clinical and blood inflammatory predictors. RESULTS: The NLR, T classification and N classification were found to be independent risk indicators for early rapid metastasis by multivariate logistic regression analysis. Twelve features associated with early rapid metastasis were selected by LASSO regression analysis, and the Rad-score was calculated. The AUC of the Rad-score was 0.773. Finally, we constructed and validated a prediction model in combination with the NLR, T classification, N classification and Rad-score. The area under the curve (AUC) was 0.936 (95% confidence interval (95% CI): 0.901-0.971), and in the validation cohort, the AUC was 0.796 (95% CI: 0.686-0.905). CONCLUSIONS: A predictive model that integrates the NLR, T classification, N classification and MR-based radiomics for distinguishing early rapid metastasis may serve as a clinical risk stratification tool for effectively guiding individual management.

Mutational landscape of nasopharyngeal carcinoma based on targeted next-generation sequencing: implications for predicting clinical outcomes.

BACKGROUND: The aim of this study was to draw a comprehensive mutational landscape of nasopharyngeal carcinoma (NPC) tumors and identify the prognostic factors for distant metastasis-free survival (DMFS). METHODS: A total of forty primary nonkeratinizing NPC patients underwent targeted next-generation sequencing of 450 cancer-relevant genes. Analysis of these sequencing and clinical data was performed comprehensively. Univariate Cox regression analysis and multivariate Lasso-Cox regression analyses were performed to identify factors that predict distant metastasis and construct a risk score model, and seventy percent of patients were randomly selected from among the samples as a validation cohort. A receiver operating characteristic (ROC) curve and Harrell's concordance index (C-index) were used to investigate whether the risk score was superior to the TNM stage in predicting the survival of patients. The survival of patients was determined by Kaplan-Meier curves and log-rank tests. RESULTS: The twenty most frequently mutated genes were identified, such as KMT2D, CYLD, and TP53 et al. Their mutation frequencies of them were compared with those of the COSMIC database and cBioPortal database. N stage, tumor mutational burden (TMB), PIK3CA, and SF3B1 were identified as predictors to build the risk score model. The risk score model showed a higher AUC and C-index than the TNM stage model, regardless of the training cohort or validation cohort. Moreover, this study found that patients with tumors harboring PI3K/AKT or RAS pathway mutations have worse DMFS than their wild-type counterparts. CONCLUSIONS: In this study, we drew a mutational landscape of NPC tumors and established a novel four predictor-based prognostic model, which had much better predictive capacity than TNM stage.

Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study.

BACKGROUND: Although surgical pathology or biopsy are considered the gold standard for glioma grading, these procedures have limitations. This study set out to evaluate and validate the predictive performance of a deep learning radiomics model based on contrast-enhanced T1-weighted multiplanar reconstruction images for grading gliomas. METHODS: Patients from three institutions who diagnosed with gliomas by surgical specimen and multiplanar reconstructed (MPR) images were enrolled in this study. The training cohort included 101 patients from institution 1, including 43 high-grade glioma (HGG) patients and 58 low-grade glioma (LGG) patients, while the test cohorts consisted of 50 patients from institutions 2 and 3 (25 HGG patients, 25 LGG patients). We then extracted radiomics features and deep learning features using six pretrained models from the MPR images. The Spearman correlation test and the recursive elimination feature selection method were used to reduce the redundancy and select most predictive features. Subsequently, three classifiers were used to construct classification models. The performance of the grading models was evaluated using the area under the receiver operating curve, sensitivity, specificity, accuracy, precision, and negative predictive value. Finally, the prediction performances of the test cohort were compared to determine the optimal classification model. RESULTS: For the training cohort, 62% (13 out of 21) of the classification models constructed with MPR images from multiple planes outperformed those constructed with single-plane MPR images, and 61% (11 out of 18) of classification models constructed with both radiomics features and deep learning features had higher area under the curve (AUC) values than those constructed with only radiomics or deep learning features. The optimal model was a random forest model that combined radiomic features and VGG16 deep learning features derived from MPR images, which achieved AUC of 0.847 in the training cohort and 0.898 in the test cohort. In the test cohort, the sensitivity, specificity, and accuracy of the optimal model were 0.840, 0.760, and 0.800, respectively. CONCLUSIONS: Multiplanar CE-T1W MPR imaging features are more effective than features from single planes when differentiating HGG and LGG. The combination of deep learning features and radiomics features can effectively grade glioma and assist clinical decision-making.

Tumor Blood Flow Is a Predictor of Radiotherapy Response in Patients With Nasopharyngeal Carcinoma.

PURPOSE: The aim of this study was to evaluate tumor blood flow (TBF) as a predictor of radiotherapy response for nasopharyngeal carcinoma (NPC). MATERIALS AND METHOD: A total of 134 patients were divided into two groups, the complete response (CR) group and the partial response (PR) group based on RECIST 1.1 recommendations. The statistical difference was evaluated for pre- and mid- or post-treatment TBF and changes of TBF for tumors and metastatic lymph nodes between CR and PR, respectively. The receiver operation characteristic (ROC) curve was utilized to evaluate the accuracy of TBF in predicting the response of radiation therapy. The association between TBF and SUVmax was also investigated. RESULTS: The reduction of TBF in CR was significantly lower than that in PR for primary tumors (P <0.001) and metastatic lymph nodes (P <0.001). The multivariate logistic regression analysis indicated that the reduction of TBF is an independent predictor of the response of radiation therapy for primary tumors (P <0.001) and metastatic lymph nodes (P <0.001). The accuracy of TBF reduction in predicting the response of radiation therapy was 0.817 in primary tumors and 0.924 in metastatic lymph nodes, respectively. No significant correlation was observed between the TBF values and SUVmax of primary tumors (r = -0.008, P = 0.954) and metastasis lymph nodes (r = -0.061, P = 0.652). CONCLUSION: This study suggests that the reduction of TBF is a promising parameter for evaluating the response of radiation therapy.

Is the Water System Healthy in Urban Agglomerations? A Perspective from the Water Metabolism Network.

Urban agglomerations are a primary spatial focus of socioeconomic activity and inherently include large volumes of embodied water. We have applied the concept of water metabolism health to comprehensively evaluate the overall operation of water systems in urban agglomerations and propose an innovative assessment framework. In particular, we constructed a water metabolism network (WMN) model to simulate a water system in which different cities and sectors are integrated, combining a newly compiled multiregional input-output (MRIO) table of water flow with ecological network analysis (ENA). A case study considering the Pearl River Delta (PRD) urban agglomeration in 2015 demonstrates that its network is well synergic but highly dependent, with considerable negative effects. Highly developed cities in southeastern of the PRD exhibit higher embodied water productivity and robustness but impose considerable negative effects on the water system. We found the agricultural sector to be a dominant controller of the network; the construction and service sectors represent the primary beneficiaries with strong competition. We suggest measures at various scales to improve water utilization efficiency and promote positive interactions between components, thus improving water metabolism system health for urban agglomerations.

Boschniakia Rossica Polysaccharide Triggers Laryngeal Carcinoma Cell Apoptosis by Regulating Expression of Bcl-2, Caspase-3, and P53.

BACKGROUND Laryngeal cancer is a malignant head and neck tumor with high morbidity and high mortality in humans. Recently, treatments with Chinese medicines and their extracts have gradually received great attention, and studies suggest that Boschniakia rossica polysaccharide (BRP) has potential anti-tumor activity. Therefore, this study investigating the role of BRP in inducing apoptosis in human laryngeal carcinoma cells. MATERIAL AND METHODS The BRP was extracted with organic solvent and HR column. We treated Hep2 laryngeal carcinoma cells with different concentrations of BRP, then assessed cell growth inhibition rate by flow cytometry and apoptosis index by TUNEL staining. The protein expression of p53, Bcl-2, Bax, and caspase-3 were analyzed by Western blot. RESULTS Flow cytometry results showed that BRP inhibited Hep2 cell proliferation in a dose-dependent manner (p<0.05), and TUNEL staining indicated that BRP significantly increased Hep2 apoptosis index (p<0.05). Western blot results showed that the expression levels of p53 and activation of caspase-3 in Hep2 cells were significantly up-regulated (p<0.05), while the expression of Bcl-2 was significantly down-regulated (p<0.05). CONCLUSIONS BRP might induce cell apoptosis by regulating the expression level of cell apoptosis-associated proteins, suggesting strong anti-laryngeal cancer activity.

Rice bran polysaccharides and oligosaccharides modified by Grifola frondosa fermentation: Antioxidant activities and effects on the production of NO.

Rice bran polysaccharides (RBPSs) are valuable compounds with many biological activities. In this work, a fungus called Grifola frondosa, was selected to ferment defatted rice bran water extracts and modify the RBPSs, which were then isolated by ethanol precipitation and deproteinization. GC analysis of fermented products suggested they are composed of glucose, arabinose, galactose, mannose, and xylose at a molar ratio of 9:5:8:2:5, which was 32:4:6:2:5 before fermentation. HPLC analysis revealed that the molecular weight of unfermented RBPS was distributed mainly from 103 to 104Da, and it changed to 102 to 103Da after fermentation. Antioxidant activities and effects on the production of NO were analyzed and it indicated that the scavenging ratios of hydroxyl and DPPH radicals by the fermented products were significantly enhanced compared to the unfermented ones, and also the products fermented for 9days exhibited two-way adjusting effects on the production of NO in macrophages.

Factors influencing the incidence of sinusitis in nasopharyngeal carcinoma patients after intensity-modulated radiation therapy.

The aim of the study was to investigate the incidence of sinusitis in nasopharyngeal carcinoma (NPC) patients before and after intensity-modulated radiation therapy (IMRT) and to analyze factors associated with the incidence of sinusitis following IMRT. We retrospectively analyzed 283 NPC patients who received IMRT in our hospital from March 2009 to May 2011. The diagnostic criteria for sinusitis are based on computed tomography (CT) or magnetic resonance imaging (MRI) findings. CT or MRI scans were performed before and after IMRT to evaluate the incidence of sinusitis. Factors influencing the incidence of sinusitis were analyzed by log-rank univariate and logistic multivariate analyses. Among the 283 NPC patients, 128 (45.2 %) suffered from sinusitis before radiotherapy. The incidence rates of sinusitis in patients with T1, T2, T3, and T4 NPC before radiotherapy were 22.6, 37.5, 46.8, and 61.3 %, respectively (χ 2 = 14.548, p = 0.002). Among the 155 NPC patients without sinusitis before radiotherapy, the incidence rates of sinusitis at the end of radiotherapy and at 1, 3, 6, 9, 12, and 18 months after radiotherapy were 32.9, 43.2, 61.3, 68.4, 73.5, 69.7, and 61.3 %, respectively (χ 2 = 86.461, p < 0.001). Univariate analysis showed that T stage, invasion of the nasal cavity, nasal irrigation, and radiation dose to the nasopharynx were associated with the incidence of sinusitis in NPC patients after IMRT (p = 0.003, 0.006, 0.002, and 0.020). Multivariate analysis showed that T stage, invasion of the nasal cavity, and nasal irrigation were influential factors for the incidence of sinusitis in NPC patients after IMRT (p = 0.002, 0.002, and 0.000). There was a higher incidence of sinusitis with higher T stage among NPC patients before radiotherapy, and the incidence of sinusitis in NPC patients after IMRT was high (45.2 %). The incidence of sinusitis increased rapidly within the first 3 months after IMRT, and the number of sinusitis cases peaked at 6-9 months after IMRT and showed a trend toward stabilization after 1 year. Advanced T stage, invasion of the nasal cavity, and nasal irrigation were positively associated with the incidence of sinusitis in NPC patients after IMRT.