A predictive radiotranscriptomics model based on DCE-MRI for tumor immune landscape and immunotherapy in cholangiocarcinoma.

This study aims to determine the predictive role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived radiomic model in tumor immune profiling and immunotherapy for cholangiocarcinoma. To perform radiomic analysis, immune related subgroup clustering was first performed by single sample gene set enrichment analysis (ssGSEA). Second, a total of 806 radiomic features for each phase of DCE-MRI were extracted by utilizing the Python package Pyradiomics. Then, a predictive radiomic signature model was constructed after a three-step features reduction and selection, and receiver operating characteristic (ROC) curve was employed to evaluate the performance of this model. In the end, an independent testing cohort involving cholangiocarcinoma patients with anti-PD-1 Sintilimab treatment after surgery was used to verify the potential application of the established radiomic model in immunotherapy for cholangiocarcinoma. Two distinct immune related subgroups were classified using ssGSEA based on transcriptome sequencing. For radiomic analysis, a total of 10 predictive radiomic features were finally identified to establish a radiomic signature model for immune landscape classification. Regarding to the predictive performance, the mean AUC of ROC curves was 0.80 in the training/validation cohort. For the independent testing cohort, the individual predictive probability by radiomic model and the corresponding immune score derived from ssGSEA was significantly correlated. In conclusion, radiomic signature model based on DCE-MRI was capable of predicting the immune landscape of chalangiocarcinoma. Consequently, a potentially clinical application of this developed radiomic model to guide immunotherapy for cholangiocarcinoma was suggested.

Predictive value of radiomic signature based on 2-[18F]FDG PET/CT in HER2 status determination for primary breast cancer with equivocal IHC results.

PURPOSE: To evaluate the predictive power of 2-[18F]FDG PET/CT-derived radiomic signature in human epidermal growth factor receptor 2 (HER2) status determination for primary breast cancer (BC) with equivocal immunohistochemistry (IHC) results for HER2. METHODS: A total of 154 primary BC with equivocal IHC results for HER2 were retrospectively enrolled in the study. First, the following five conventional PET parameters (SUVmax, SUVmean, SUVpeak, MTV, TLG) were measured and compared between HER2-positive and HER2-negative cohorts. After quantitative radiomic features extraction and reduction, the least absolute shrinkage and selection operator (LASSO) algorithm was used to establish a radiomic signature model. Then, the area under the curve (AUCs) after a receiver operator characteristic (ROC) analysis, accuracy, sensitivity and specificity were calculated and used as the main outcomes. Finally, a total of 37 BC patients from an external institution were included to perform an external validation. RESULTS: All the five conventional PET parameters were unable to discriminate between HER2-positive and HER2-negative cohorts for BC (P = 0.104-0.544). Whereas, the developed radiomic signature model was potentially predictive of HER2 status with an of AUC 0.887 (95% confidence interval [CI], 0.824-0.950) in the training cohort and 0.766 (95% CI, 0.616-0.916) in the validation cohort, respectively. For external validation, the AUC for the external test cohort was 0.788 (95% CI, 0.633-0.944). CONCLUSIONS: Radiomic signature based on 2-[18F]FDG PET/CT images was capable of non-invasively predicting the HER2 status with a comparable ability to FISH assay, especially for those with equivocal IHC results for HER2.

GPR81-mediated reprogramming of glucose metabolism contributes to the immune landscape in breast cancer.

BACKGROUND: Local tumor microenvironment (TME) plays a crucial role in immunotherapy for breast cancer (BC). Whereas, the molecular mechanism responsible for the crosstalk between BC cells and surrounding immune cells remains unclear. The present study aimed to determine the interplay between GPR81-mediated glucometabolic reprogramming of BC and the immune landscape in TME. MATERIALS AND METHODS: Immunohistochemistry (IHC) assay was first performed to evaluate the association between GPR81 and the immune landscape. Then, several stable BC cell lines with down-regulated GPR81 expression were established to directly identify the role of GPR81 in glucometabolic reprogramming, and western blotting assay was used to detect the underlying molecular mechanism. Finally, a transwell co-culture system confirmed the crosstalk between glucometabolic regulation mediated by GPR81 in BC and induced immune attenuation. RESULTS: IHC analysis demonstrated that the representation of infiltrating CD8+ T cells and FOXP3+ T cells were dramatically higher in BC with a triple negative (TN) subtype in comparison with that with a non-TN subtype (P < 0.001). Additionally, the ratio of infiltrating CD8+ to FOXP3+ T cells was significantly negatively associated with GPR81 expression in BC with a TN subtype (P < 0.001). Furthermore, GPR81 was found to be substantially correlated with the glycolytic capability (P < 0.001) of BC cells depending on a Hippo-YAP signaling pathway (P < 0.001). In the transwell co-culture system, GPR81-mediated reprogramming of glucose metabolism in BC significantly contributed to a decreased proportion of CD8+ T (P < 0.001) and an increased percentage of FOXP3+ T (P < 0.001) in the co-cultured lymphocytes. CONCLUSION: Glucometabolic reprogramming through a GPR81-mediated Hippo-YAP signaling pathway was responsible for the distinct immune landscape in BC. GPR81 was a potential biomarker to stratify patients before immunotherapy to improve BC's clinical prospect.

Predictive Value of 18F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer.

BACKGROUND: To develop and validate a radiomics model based on 18F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31-82 years) with pathologically proven IDC and a preoperative 18F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661-0.929 (mean AUC, 0.817), and the accuracy range was 65.3-93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative 18F-FDG PET/CT radiomic features.

Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2) expression status determination significantly contributes to HER2-targeted therapy in breast cancer (BC). The purpose of this study was to evaluate the role of radiomics and machine learning based on PET/CT images in HER2 status prediction, and to identify the most effective combination of machine learning model and radiomic features. METHODS: A total of 217 BC patients who underwent PET/CT examination were involved in the study and randomly divided into a training set (n = 151) and a testing set (n = 66). For all four models, the model parameters were determined using a threefold cross-validation in the training set. Each model's performance was evaluated on the independent testing set using the receiver operating characteristic (ROC) curve, and AUC was calculated to get a quantified performance measurement of each model. RESULTS: Among the four developed machine learning models, the XGBoost model outperformed other machine learning models in HER2 status prediction. Furthermore, compared to the XGBoost model based on PET alone or CT alone radiomic features, the predictive power for HER2 status by using XGBoost model based on PET/CTmean or PET/CTconcat radiomic fusion features was dramatically improved with an AUC of 0.76 (95% confidence interval [CI] 0.69-0.83) and 0.72 (0.65-0.80), respectively. CONCLUSIONS: The established machine learning classifier based on PET/CT radiomic features is potentially predictive of HER2 status in BC.

The Predictive Role of Immune Related Subgroup Classification in Immune Checkpoint Blockade Therapy for Lung Adenocarcinoma.

Background: In lung adenocarcinoma (LUAD), the predictive role of immune-related subgroup classification in immune checkpoint blockade (ICB) therapy remains largely incomplete. Methods: Transcriptomics analysis was performed to evaluate the association between immune landscape and ICB therapy in lung adenocarcinoma and the associated underlying mechanism. First, the least absolute shrinkage and selection operator (LASSO) algorithm and K-means algorithm were used to identify immune related subgroups for LUAD cohort from the Cancer Genome Atlas (TCGA) database (n = 572). Second, the immune associated signatures of the identified subgroups were characterized by evaluating the status of immune checkpoint associated genes and the immune cell infiltration. Then, potential responses to ICB therapy based on the aforementioned immune related subgroup classification were evaluated via tumor immune dysfunction and exclusion (TIDE) algorithm analysis, and survival analysis and further Cox proportional hazards regression analysis were also performed for LUAD. In the end, gene set enrichment analysis (GSEA) was performed to explore the metabolic mechanism potentially responsible for immune related subgroup clustering. Additionally, two LUAD cohorts from the Gene Expression Omnibus (GEO) database were used as validation cohort. Results: A total of three immune related subgroups with different immune-associated signatures were identified for LUAD. Among them, subgroup 1 with higher infiltration scores for effector immune cells and immune checkpoint associated genes exhibited a potential response to IBC therapy and a better survival, whereas subgroup 3 with lower scores for immune checkpoint associated genes but higher infiltration scores for suppressive immune cells tended to be insensitive to ICB therapy and have an unfavorable prognosis. GSEA revealed that the status of glucometabolic reprogramming in LUAD was potentially responsible for the immune-related subgroup classification. Conclusion: In summary, immune related subgroup clustering based on distinct immune associated signatures will enable us to screen potentially responsive LUAD patients for ICB therapy before treatment, and the discovery of metabolism associated mechanism is beneficial to comprehensive therapeutic strategies making involving ICB therapy in combination with metabolism intervention for LUAD.

Molecular subtype classification of breast cancer using established radiomic signature models based on 18F-FDG PET/CT images.

Backgrounds: To evaluate the predictive power of 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) derived radiomics in molecular subtype classification of breast cancer (BC). Methods: A total of 273 primary BC patients who underwent a 18F-FDG PET/CT imaging prior to any treatment were included in this retrospective study, and the values of five conventional PET parameters were calculated, including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The ImageJ 1.50i software and METLAB package were used to delineate the contour of BC lesions and extract PET/CT derived radiomic features reflecting heterogeneity. Then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select optimal subsets of radiomic features and establish several corresponding radiomic signature models. The predictive powers of individual PET parameters and developed PET/CT derived radiomic signature models in molecular subtype classification of BC were evaluated by using receiver operating curves (ROCs) analyses with areas under the curve (AUCs) as the main outcomes. Results: All of the three SUV parameters but not MTV nor TLG were found to be significantly underrepresented in luminal and non-triple (TN) subgroups in comparison with corresponding non-luminal and TN subgroups. Whereas, no significant differences existed in all the five conventional PET parameters between human epidermal growth factor receptor 2+ (HER2+) and HER2- subgroups. Furthermore, all of the developed radiomic signature models correspondingly exhibited much more better performances than all the individual PET parameters in molecular subtype classification of BC, including luminal vs. non-luminal, HER2+ vs. HER2-, and TN vs. non-TN classification, with a mean value of 0.856, 0.818, and 0.888 for AUC. Conclusions: PET/CT derived radiomic signature models outperformed individual significant PET parameters in molecular subtype classification of BC.

Corrigendum: Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma.

[This corrects the article DOI: 10.3389/fonc.2021.709137.].

Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma.

OBJECTIVE: The purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). METHODS: Three hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SECT and SEPET) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SECT and SEPET. RESULTS: The AUCs of the SECT and SEPET were 0.72 (95% CI, 0.62-0.80) and 0.74 (95% CI, 0.65-0.82) in the testing data set, respectively. After integrating SECT and SEPET with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75-0.90), significantly higher than SECT (p<0.05). CONCLUSION: The stacking model based on 18F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR-targeted therapy.

Feedforward and feedback pathways of nociceptive and tactile processing in human somatosensory system: A study of dynamic causal modeling of fMRI data.

Nociceptive and tactile information is processed in the somatosensory system via reciprocal (i.e., feedforward and feedback) projections between the thalamus, the primary (S1) and secondary (S2) somatosensory cortices. The exact hierarchy of nociceptive and tactile information processing within this 'thalamus-S1-S2' network and whether the processing hierarchy differs between the two somatosensory submodalities remains unclear. In particular, two questions related to the ascending and descending pathways have not been addressed. For the ascending pathways, whether tactile or nociceptive information is processed in parallel (i.e., 'thalamus-S1' and 'thalamus-S2') or in serial (i.e., 'thalamus-S1-S2') remains controversial. For the descending pathways, how corticothalamic feedback regulates nociceptive and tactile processing also remains elusive. Here, we aimed to investigate the hierarchical organization for the processing of nociceptive and tactile information in the 'thalamus-S1-S2' network using dynamic causal modeling (DCM) combined with high-temporal-resolution fMRI. We found that, for both nociceptive and tactile information processing, both S1 and S2 received inputs from thalamus, indicating a parallel structure of ascending pathways for nociceptive and tactile information processing. Furthermore, we observed distinct corticothalamic feedback regulations from S1 and S2, showing that S1 generally exerts inhibitory feedback regulation independent of external stimulation whereas S2 provides additional inhibition to the thalamic activity during nociceptive and tactile information processing in humans. These findings revealed that nociceptive and tactile information processing have similar hierarchical organization within the somatosensory system in the human brain.

MOFs-Derived Fe-N Codoped Carbon Nanoparticles as O2-Evolving Reactor and ROS Generator for CDT/PDT/PTT Synergistic Treatment of Tumors.

Metal-organic frameworks (MOFs) derivatives had been widely explored in electronic and environmental fields, but rarely evaluated in the biomedical applications. Herein, Fe-N codoped carbon (FeNC) nanoparticles were synthesized and characterized via facile pyrolysis of precursor ZIF-8 (Fe/Zn) nanoparticles, and their potential applications in tumor therapy were assessed in this investigation both in vitro and in vivo. After PAA (sodium polyacrylate) modification, the FeNC@PAA nanoparticles were able to initiate a Fe-based Fenton-like reaction to generate ·OH and O2 for chemodynamic therapy (CDT) and O2 evolution. Meanwhile, the porphyrin-like metal center in the FeNC@PAA nanoparticles could be used as a photosensitizer for photodynamic therapy (PDT) of tumors, which could be enhanced by O2 generated in CDT. Furthermore, the FeNC@PAA nanoparticles were also found to be effective in photothermal therapy (PTT) with a photothermal conversion efficiency of 29.15%, owing to a high absorbance in the near-infrared region (NIR). In conclusion, the synthesized FeNC@PAA nanoparticles exhibited promising applications in O2 evolution and CDT/PDT/PTT synergistic treatment of tumors.

Prediction of mediastinal lymph node metastasis based on 18F-FDG PET/CT imaging using support vector machine in non-small cell lung cancer.

OBJECTIVE: The purpose of this study was to develop a classification method based on support vector machine (SVM) to improve the diagnostic performance of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) to detect the lymph node (LN) metastasis in non-small cell lung cancer (NSCLC). METHOD: Two hundred nineteen lymph nodes (37 metastatic) from 71 patients were evaluated in this study. SVM models were developed with 7 LN features. The area under the curve (AUC) and accuracy of 9 models were compared to select the best model. The best SVM model was simplified on the basis of the feature weights and value distribution to further suit the clinical application. RESULTS: The maximum, minimum, and mean accuracy of the best model was 91.89% (68/74, 95% CI 83.11~96.54%), 66.22% (49/74, 95% CI 54.85~75.98%), and 80.09% (59,266/74,000, 95% CI 70.27~89.19%), respectively, with an AUC of 0.94, 0.66, and 0.81, respectively. The best SVM model was finally simplified into a score rule: LNs with scores more than 3.0 were considered as malignant ones, whereas LNs with scores less than 1.5 tended to be benign ones. For the LNs with scores within a range of 1.5-3.0, metastasis was suspected. CONCLUSION: An SVM model based on 18F-FDG PET/CT images was able to predict the metastatic LNs for patients with NSCLC. The ratio of the maximum of standard uptake value of LNs to aortic arch played a major role in the model. After simplification, the model could be transferred into a scoring method which may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier. KEY POINTS: • The SVM model based on 18F-FDG PET/CT features may help clinicians to make a decision for metastatic mediastinal lymph nodes in patients with NSCLC. • The SURblood plays a major role in the SVM model. • The score rule based on the SVM model simplified the complexity of the model and may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.

18F-FHBG PET-CT Reporter Gene Imaging of Adoptive CIK Cell Transfer Immunotherapy for Breast Cancer in a Mouse Model.

BACKGROUND: To further improve the efficiency of adoptively transferred cytokine-induced killer (CIK) cell immunotherapy in breast cancer (BC), a reliable imaging method is required to visualize and monitor these transferred cells in vivo. METHODS: Herpes simplex virus 1-thymidine kinase (HSV1-TK) and 9-(4-[18F]fluoro-3-(hydroxymethyl)butyl)guanine (18F-FHBG) were used as a pair of reporter gene/reporter probe for positron emission tomography (PET) imaging in this study. Following the establishment of subcutaneous BC xenograft-bearing nude mice models, induced human CIK cells expressing reporter gene HSV1-TK through lentiviral transduction were intravenously injected to nude mice. γ-radioimmunoassay was used to determine the specific uptake of 18F-FHBG by these genetically engineered CIK cells expressing HSV1-TK in vitro, and 18F-FHBG micro positron emission tomography-computed tomography (PET-CT) imaging was performed to visualize these adoptively transferred CIK cells in tumor-bearing nude mice. RESULTS: Specific uptake of 18F-FHBG by CIK cells expressing HSV1-TK was clearly observed in vitro. Consistently, the localization of adoptively transferred CIK cells in tumor target could be effectively visualized by 18F-FHBG micro PET-CT reporter gene imaging. CONCLUSION: PET-CT reporter gene imaging using 18F-FHBG as a reporter probe enables the visualization and monitoring of adoptively transferred CIK cells in vivo.

Enhanced glucose metabolism mediated by CD147 contributes to immunosuppression in hepatocellular carcinoma.

From a metabolic perspective, cancer may be considered as a metabolic disease characterized by reprogrammed glycolytic metabolism. The aim of the present study was to investigate CD147-mediated glucose metabolic regulation in hepatocellular carcinoma (HCC) and its contribution to altered immune responses in the tumor microenvironment. Several HCC cell lines and corresponding nude mice xenografts models differing in CD147 expressions were established to directly investigate the role of CD147 in the reprogramming of glucose metabolism, and to determine the underlying molecular mechanisms. Immunohistochemistry (IHC) analyses and flow cytometry were used to identify the relationship between reprogrammed glycolysis and immunosuppression in HCC. Upregulated CD147 expressions were found to be associated with enhanced expressions of GLUT1, MCT1 in HCC tumorous tissues. CD147 promoted the glycolytic metabolism in HCC cell lines in vitro via the PI3K/Akt/mTOR signaling pathway. A positive correlation existed between a profile of immunosuppressive lymphocytes infiltration and CD147 expression in HCC tissues. Accumulation of FOXP3-expressing regulatory T cells was induced under a stimulation with lactate in vitro. In conclusion, CD147 promoted glycolytic metabolism in HCC via the PI3K/Akt/mTOR signaling pathway, and was related to immunosuppression in HCC.

Predictive Power of a Radiomic Signature Based on 18F-FDG PET/CT Images for EGFR Mutational Status in NSCLC.

Radiomics has become an area of interest for tumor characterization in 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging. The aim of the present study was to demonstrate how imaging phenotypes was connected to somatic mutations through an integrated analysis of 115 non-small cell lung cancer (NSCLC) patients with somatic mutation testings and engineered computed PET/CT image analytics. A total of 38 radiomic features quantifying tumor morphological, grayscale statistic, and texture features were extracted from the segmented entire-tumor region of interest (ROI) of the primary PET/CT images. The ensembles for boosting machine learning scheme were employed for classification, and the least absolute shrink age and selection operator (LASSO) method was used to select the most predictive radiomic features for the classifiers. A radiomic signature based on both PET and CT radiomic features outperformed individual radiomic features, the PET or CT radiomic signature, and the conventional PET parameters including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), in discriminating between mutant-type of epidermal growth factor receptor (EGFR) and wild-type of EGFR- cases with an AUC of 0.805, an accuracy of 80.798%, a sensitivity of 0.826 and a specificity of 0.783. Consistently, a combined radiomic signature with clinical factors exhibited a further improved performance in EGFR mutation differentiation in NSCLC. In conclusion, tumor imaging phenotypes that are driven by somatic mutations may be predicted by radiomics based on PET/CT images.

18 F-FDG PET/CT imaging: A supplementary understanding of pulmonary sclerosing pneumocytoma.

BACKGROUND: We sought to investigate the clinical features and 18 F-FDG PET/CT characteristics of pulmonary sclerosing pneumocytoma (PSP). METHODS: We retrospectively reviewed and comparatively analyzed 18 F-FDG PET/CT imaging results of 22 patients with diagnosed PSP in our hospital from November 2009 to September 2015. RESULTS: The SUVmax in tumors was positively correlated with tumor size in typical PSPs (R = 0.806, R2 = 0.650, P = 0.001); however, the SUVmax in tumors had no significant correlation with tumor size of atypical PSPs (R = 0.479, R2 = 0.229, P = 0.162), and the degree of correlation between them attenuated when atypical PSPs were included (R = 0.518, R2 = 0.268, P = 0.011). A majority (90%) of atypical PSPs were found in males. Symptomatic patients showed a higher SUVmax than the asymptomatic group (5.68 ± 3.63 vs. 2.76 ± 1.18, respectively, P = 0.002). CONCLUSION: Tumor size and clinical features may be associated with increased FDG uptake in PSPs. Morphological differences may affect the correlation between tumor size and SUVmax in PSPs. The atypical form of PSP may be more common in men.

Correlation between EGFR mutation status and F18 -fluorodeoxyglucose positron emission tomography-computed tomography image features in lung adenocarcinoma.

BACKGROUND: The purpose of this study was to investigate an association between EGFR mutation status and 18 F-fluorodeoxyglucose positron emission tomography-computed tomography (18 F-FDG PET-CT) image features in lung adenocarcinoma. METHODS: Retrospective analysis of the data of 139 patients with lung adenocarcinoma confirmed by surgical pathology who underwent preoperative 18 F-FDG PET-CT was conducted. Correlations between EGFR mutation status, clinical characteristics, and PET-CT parameters, including the maximum standardized uptake value (SUVmax), the mean of the SUV (SUVmean), the peak of the SUV (SUVpeak) of the primary tumor, and the ratio of SUVmax between the primary tumor and the mediastinal blood pool (SUVratio), were statistically analyzed. Multivariate logistic regression analysis was performed to identify predictors of EGFR mutation. Receiver operating characteristic curves of statistical quantitative parameters were compared. RESULTS: EGFR mutations were detected in 74 (53.2%) of the 139 lung adenocarcinomas and were more frequent in non-smoking patients. Univariate analysis showed that the SUVmax, SUVmean, SUVpeak, and SUVratio were lower in EGFR-mutated than in wild-type tumors. The receiver operating characteristic curves showed no significant differences between their diagnostic efficiencies. Multivariate logistic regression analysis showed that being a never smoker was an independent predictor of EGFR mutation. CONCLUSION: Quantitative parameters based on 18 F-FDG PET-CT have modest power to predict the presence of EGFR mutation in lung adenocarcinoma; however, when compared to smoking history, they are not good or significant predictive factors.

Synthesis and Characterization of WO3/Graphene Nanocomposites for Enhanced Photocatalytic Activities by One-Step In-Situ Hydrothermal Reaction.

Tungsten trioxide (WO3) nanorods are synthesized on the surface of graphene (GR) sheets by using a one-step in-situ hydrothermal method employing sodium tungstate (Na2WO4·2H2O) and graphene oxide (GO) as precursors. The resulting WO3/GR nanocomposites are characterized by X-ray diffraction, Raman spectroscopy, transmission electron microscopy, scanning electron microscopy and X-ray photoelectron spectroscopy. The results confirm that the interface between WO3 nanorod and graphene contains chemical bonds. The enhanced optical absorption properties are measured by UV-vis diffuse reflectance spectra. The photocatalytic activity of the WO3/GR nanocomposites under visible light is evaluated by the photodegradation of methylene blue, where the degradation rate of WO3/GR nanocomposites is shown to be double that of pure WO3. This is attributed to the synergistic effect of graphene and the WO3 nanorod, which greatly enhances the photocatalytic performance of the prepared sample, reduces the recombination of the photogenerated electron-hole pairs and increases the visible light absorption efficiency. Finally, the photocatalytic mechanism of the WO3/GR nanocomposites is presented. The synthesis of the prepared sample is convenient, direct and environmentally friendly. The study reports a highly efficient composite photocatalyst for the degradation of contaminants that can be applied to cleaning up the environment.