Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography

PURPOSE

This study aims to assess the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUVmax) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC).

METHODS

A retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set. The clinical information, tumor SUVmax, and 13 related CT features were analyzed. Subgroup analysis was carried out based on tumor density type. In the training set, univariable and multivariable logistic regression analyses were employed to identify the most significant variables. A multivariable logistic regression model was constructed and the corresponding nomogram was developed to predict STAS in NSCLC, and its diagnostic efficacy was evaluated in the testing set.

RESULTS

SUVmax, consolidation-to-tumor ratio (CTR), and lobulation sign emerged as the best combination of variables for predicting STAS in NSCLC. Among these, SUVmax and CTR were identified as independent predictors for STAS prediction. The constructed prediction model demonstrated area under the curve (AUC) values of 0.796 and 0.821 in the training and testing sets, respectively. Subgroup analysis revealed a 2.69 times higher STAS-positive rate in solid nodules compared to part-solid nodules. SUVmax was an independent predictor for predicting STAS in solid nodular NSCLC, while CTR and an emphysema background were independent predictors for STAS in part-solid nodular NSCLC.

CONCLUSION

Our nomogram based on preoperative 18F-FDG PET/CT radiological features and SUVmax effectively predicts STAS status in clinical-stage IA NSCLC. Furthermore, our study highlights that metabolic parameters and CT variables associated with STAS differ between solid and part-solid nodular NSCLC.

CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer

The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.

Nomogram based on clinical characteristics and radiological features for the preoperative prediction of spread through air spaces in patients with clinical stage IA non-small cell lung cancer: a multicenter study

PURPOSE

To investigate the value of clinical characteristics and radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC).

METHODS

A total of 336 patients with NSCLC from our hospital were randomly divided into two groups, i.e., the training cohort (n = 236) and the internal validation cohort (n = 100) (7:3 ratio). Furthermore, 69 patients from two other hospitals were collected as the external validation cohort. Eight clinical patient characteristics were recorded, and 20 tumor radiological features were quantitatively measured and qualitatively analyzed. In the training cohort, the differences in clinical characteristics and radiological features were compared using univariate and multivariate analysis. A nomogram was created, and the predictive efficacy of the model was evaluated in the validation cohorts. The receiver operating characteristic curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. In addition, the Hosmer-Lemeshow test and calibration curve were used to evaluate the goodness-of-fit of the model, and the decision curve was used to analyze the model's clinical application value.

RESULTS

The best predictors included gender, the carcinoembryonic antigen (CEA), consolidation-to-tumor ratio (CTR), density type, and distal ribbon sign. Among these, the tumor density type [odds ratio (OR): 6.738] and distal ribbon sign (OR: 5.141) were independent risk factors for predicting the STAS status. Moreover, three different STAS prediction models were constructed, i.e., a clinical, radiological, and combined model. The clinical model comprised gender and the CEA, the radiological model included the CTR, density type, and distal ribbon sign, and the combined model comprised the above two models. A DeLong test results revealed that the combined model was superior to the clinical model in all three cohorts and superior to the radiological model in the external validation cohort; the cohort AUC values were 0.874, 0.822, and 0.810, respectively. The results also showed that the combined model had the highest diagnostic efficacy among the models. The Hosmer-Lemeshow test showed that the combined model showed a good fit in all three cohorts, and the calibration curve showed that the predicted probability value of the combined model was in good agreement with the actual STAS status. Finally, the decision curve showed that the combined model had a better clinical application value than the clinical and radiological models.

CONCLUSION

The nomogram created in this study, based on clinical characteristics and radiological features, has a high diagnostic efficiency for predicting the STAS status in patients with clinical stage IA NSCLC and may support the creation of personalized treatment strategies before surgery.

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study

PURPOSE

To assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol.

METHODS

A chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared.

RESULTS

With each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs.

CONCLUSION

Compared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography

PURPOSE

Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images.

MATERIALS AND METHODS

Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently.

RESULTS

The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set.

CONCLUSION

The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules

Objective

To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics.

Methods

This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise.

Results

The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%.

Conclusion

The baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.

Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study

Purpose

This study aimed to screen out computed tomography (CT) morphological features and clinical characteristics of patients with lung cancer to identify chronic obstructive pulmonary disease (COPD). Further, we aimed to develop and validate different diagnostic nomograms for predicting whether lung cancer is comorbid with COPD.

Patients and Methods

This retrospective study examined data from 498 patients with lung cancer (280 with COPD, 218 without COPD; 349 in training cohort, 149 in validation cohort) from two centers. Five clinical characteristics and 20 CT morphological features were evaluated. Differences in all variables were assessed between COPD and non-COPD groups. Models were developed using multivariable logistic regression to identify COPD, including clinical, imaging, and combined nomograms. Receiver operating characteristic curves were used to evaluate and compare the performance of nomograms.

Results

Age, sex, interface, bronchus cutoff sign, spine-like process, and spiculation sign were independent predictors of COPD in patients with lung cancer. In the training and validation cohorts, the clinical nomogram showed good performance to predict COPD in lung cancer patients (areas under the curves [AUCs] of 0.807 [95% CI, 0.761-0.854] and 0.753 [95% CI, 0.674-0.832]); while the imaging nomogram showed slightly better performance (AUCs of 0.814 [95% CI, 0.770-0.858] and 0.780 [95% CI, 0.705-0.856]). For the combined nomogram generated with clinical and imaging features, the performance was further improved (AUC=0.863 [95% CI, 0.824-0.903], 0.811 [95% CI, 0.742-0.880] in the training and validation cohort). At 60% risk threshold, there were more true negative predictions (48 vs 44) and higher accuracy (73.15% vs 71.14%) for the combined nomogram compared with the clinical nomogram in the validation cohort.

Conclusion

The combined nomogram developed with clinical and imaging features outperformed clinical and imaging nomograms; this provides a convenient method to detect COPD in patients with lung cancer using one-stop CT scanning.

Multivariate analysis based on the maximum standard unit value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography and computed tomography features for preoperative predicting of visceral pleural invasion in patients with subpleural clinical stage IA peripheral lung adenocarcinoma

PURPOSE

Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery.

METHODS

A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set.

RESULTS

The size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit.

CONCLUSION

The nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.

Thoracic Imaging in China: Yesterday, Today, and Tomorrow

Thoracic imaging has been revolutionized through advances in technology and research around the world, and so has China. Thoracic imaging in China has progressed from anatomic observation to quantitative and functional evaluation, from using traditional approaches to using artificial intelligence. This article will review the past, present, and future of thoracic imaging in China, in an attempt to establish new accepted strategies moving forward.

Re-Defining High Risk COPD with Parameter Response Mapping Based on Machine Learning Models

Purpose

To explore optimal threshold of FEV1% predicted value (FEV1%pre) for high-risk chronic obstructive pulmonary disease (COPD) using the parameter response mapping (PRM) based on machine learning classification model.

Patients and Methods

A total of 561 consecutive non-COPD subjects who were screened for chest diseases in our hospital between August and October 2018 and who had complete questionnaire surveys, pulmonary function tests (PFT), and paired respiratory chest CT scans were enrolled retrospectively. The CT quantitative parameter for small airway remodeling was PRM, and 72 parameters were obtained at the levels of whole lung, left and right lung, and five lobes. To identify a more reasonable thresholds of FEV1% predicted value for distinguishing high-risk COPD patients from the normal, 80 thresholds from 50% to 129% were taken with a partition of 1% to establish a random forest classification model under each threshold, such that novel PFT-parameter-based high-risk criteria would be more consistent with the PRM-based machine learning classification model.

Results

Machine learning-based PRM showed that consistency between PRM parameters and PFT was better able to distinguish high-risk COPD from the normal, with an AUC of 0.84 when the threshold was 72%. When the threshold was 80%, the AUC was 0.72 and when the threshold was 95%, the AUC was 0.64.

Conclusion

Machine learning-based PRM is feasible for redefining high-risk COPD, and setting the optimal FEV1% predicted value lays the foundation for redefining high-risk COPD diagnosis.

Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules

Objective

This study aimed to develop effective artificial intelligence (AI) diagnostic models based on CT images of pulmonary nodules only, on descriptional and quantitative clinical or image features, or on a combination of both to differentiate benign and malignant ground-glass nodules (GGNs) to assist in the determination of surgical intervention.

Methods

Our study included a total of 867 nodules (benign nodules: 112; malignant nodules: 755) with postoperative pathological diagnoses from two centers. For the diagnostic models to discriminate between benign and malignant GGNs, we adopted three different artificial intelligence (AI) approaches: a) an image-based deep learning approach to build a deep neural network (DNN); b) a clinical feature-based machine learning approach based on the clinical and image features of nodules; c) a fusion diagnostic model integrating the original images and the clinical and image features. The performance of the models was evaluated on an internal test dataset (the “Changzheng Dataset”) and an independent test dataset collected from an external institute (the “Longyan Dataset”). In addition, the performance of automatic diagnostic models was compared with that of manual evaluations by two radiologists on the ‘Longyan dataset’.

Results

The image-based deep learning model achieved an appealing diagnostic performance, yielding AUC values of 0.75 (95% confidence interval [CI]: 0.62, 0.89) and 0.76 (95% CI: 0.61, 0.90), respectively, on both the Changzheng and Longyan datasets. The clinical feature-based machine learning model performed well on the Changzheng dataset (AUC, 0.80 [95% CI: 0.64, 0.96]), whereas it performed poorly on the Longyan dataset (AUC, 0.62 [95% CI: 0.42, 0.83]). The fusion diagnostic model achieved the best performance on both the Changzheng dataset (AUC, 0.82 [95% CI: 0.71-0.93]) and the Longyan dataset (AUC, 0.83 [95% CI: 0.70-0.96]), and it achieved a better specificity (0.69) than the radiologists (0.33-0.44) on the Longyan dataset.

Conclusion

The deep learning models, including both the image-based deep learning model and the fusion model, have the ability to assist radiologists in differentiating between benign and malignant nodules for the precise management of patients with GGNs.

Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?

Background

The peri-tumor microenvironment plays an important role in the occurrence, growth and metastasis of cancer. The aim of this study is to explore the value and application of a CT image-based deep learning model of tumors and peri-tumors in predicting the invasiveness of ground-glass nodules (GGNs).

Methods

Preoperative thin-section chest CT images were reviewed retrospectively in 622 patients with a total of 687 pulmonary GGNs. GGNs are classified according to clinical management strategies as invasive lesions (IAC) and non-invasive lesions (AAH, AIS and MIA). The two volumes of interest (VOIs) identified on CT were the gross tumor volume (GTV) and the gross volume of tumor incorporating peritumoral region (GPTV). Three dimensional (3D) DenseNet was used to model and predict GGN invasiveness, and five-fold cross validation was performed. We used GTV and GPTV as inputs for the comparison model. Prediction performance was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

Results

The GTV-based model was able to successfully predict GGN invasiveness, with an AUC of 0.921 (95% CI, 0.896–0.937). Using GPTV, the AUC of the model increased to 0.955 (95% CI, 0.939–0.971).

Conclusions

The deep learning method performed well in predicting GGN invasiveness. The predictive ability of the GPTV-based model was more effective than that of the GTV-based model.

Multitask Learning and Reinforcement Learning for Personalized Dialog Generation: An Empirical Study

Open-domain dialog generation, which is a crucial component of artificial intelligence, is an essential and challenging problem. In this article, we present a personalized dialog system, which leverages the advantages of multitask learning and reinforcement learning for personalized dialogue generation (MRPDG). Specifically, MRPDG consists of two subtasks: 1) an author profiling module that recognizes user characteristics from the input sentence (auxiliary task) and 2) a personalized dialog generation system that generates informative, grammatical, and coherent responses with reinforcement learning algorithms (primary task). Three kinds of rewards are proposed to generate high-quality conversations. We investigate the effectiveness of three widely used reinforcement learning methods [i.e., Q-learning, policy gradient, and actor-critic (AC) algorithm] in a personalized dialog generation system and demonstrate that the AC algorithm achieves the best results on the underlying framework. Comprehensive experiments are conducted to evaluate the performance of the proposed model on two real-life data sets. Experimental results illustrate that MRPDG is able to produce high-quality personalized dialogs for users with different characteristics. Quantitatively, the proposed model can achieve better performance than the compared methods across different evaluation metrics, such as the human evaluation, BiLingual Evaluation Understudy (BLEU), and perplexity.

Quantifying Tissue Properties of the Optic Radiations Using Strategically Acquired Gradient Echo Imaging and Enhancing the Contrast Using Diamagnetic Susceptibility Weighted Imaging

Visualization of the optic radiations is of clinical importance for diagnosing many diseases and depicting their anatomic structures for neurosurgical interventions. In this study, we quantify proton density, T1, T2*, and susceptibility of the optic radiation fiber bundles in a series of 10 healthy control participants using strategically acquired gradient echo imaging. Furthermore, we introduce a novel means to enhance the contrast of the optic radiations using diamagnetic susceptibility weighted imaging.

Evaluating a Fully Automated Pulmonary Nodule Detection Approach and Its Impact on Radiologist Performance

Purpose

To compare sensitivity in the detection of lung nodules between the deep learning (DL) model and radiologists using various patient population and scanning parameters and to assess whether the radiologists' detection performance could be enhanced when using the DL model for assistance.

Materials and Methods

A total of 12 754 thin-section chest CT scans from January 2012 to June 2017 were retrospectively collected for DL model training, validation, and testing. Pulmonary nodules from these scans were categorized into four types: solid, subsolid, calcified, and pleural. The testing dataset was divided into three cohorts based on radiation dose, patient age, and CT manufacturer. Detection performance of the DL model was analyzed by using a free-response receiver operating characteristic curve. Sensitivities of the DL model and radiologists were compared by using exploratory data analysis. False-positive detection rates of the DL model were compared within each cohort. Detection performance of the same radiologist with and without the DL model were compared by using nodule-level sensitivity and patient-level localization receiver operating characteristic curves.

Results

The DL model showed elevated overall sensitivity compared with manual review of pulmonary nodules. No significant dependence regarding radiation dose, patient age range, or CT manufacturer was observed. The sensitivity of the junior radiologist was significantly dependent on patient age. When radiologists used the DL model for assistance, their performance improved and reading time was reduced.

Conclusion

DL shows promise to enhance the identification of pulmonary nodules and benefit nodule management.© RSNA, 2019Supplemental material is available for this article.

Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology

OBJECTIVES

To compare the predictive performance of radiomics signature and CT morphological features for epidermal growth factor receptor (EGFR) mutation status; then further to develop and compare the different predictive models for EGFR mutation in non-small cell lung cancer (NSCLC) patients.

MATERIALS AND METHODS

This retrospective study involved 404 patients with NSCLC (243 cases in the training cohort and 161 cases in the validation cohort). Radiomics features were extracted from preoperative non-contrast CT images of the entire tumor. Correlations between the EGFR mutation status and candidate predictors were assessed using Mann-Whitney U test or Chi-square test. Unsupervised consensus clustering was used to analyze the representativeness and reduce the redundancy of radiomics features. Multivariable logistic regression analysis was performed to build radiomics signature and develop predictive models of EGFR mutation. ROC curve analysis and Delong test were used to compare the predictive performance among individual features and models.

RESULTS

Of the 234 radiomics features, 93 radiomics features with high repeatability and high predictive significance were selected. The radiomics signature, which was built with one histogram and two textural features, showed the best predictive performance (AUC = 0.762 and 0.775 in the training and validation cohort) in comparison with all the clinical characteristics and conventional CT morphological features to differentiate EGFR mutation status (P < 0.05). The integrated model was developed with maximum diameter, location, sex and radiomics signature. In the training and validation cohort, the integrated model showed the most optimal predictive performance (AUC = 0.798, 0.818 in the training and validation cohort) compared with the clinical models.

CONCLUSION

The radiomics signature showed better performance for predicting EGFR mutant than all the clinical and morphological features. Moreover, the integrated model built with radiomics signature, clinical and morphological features outperformed the clinical models, which is helpful for physicians to determine the targeted therapy.

The potential of cadmium ion-immobilized Rhizobium pusense KG2 to prevent soybean root from absorbing cadmium in cadmium-contaminated soil

AIMS

Because the effect of Cd2+ -immobilized microbe on Cd uptake of plants in Cd-contaminated soil remains underexplored, this study focuses on the effect of Cd2+ -immobilized rhizobia on Cd uptake of soybean.

METHODS AND RESULTS

Strain KG2 from soybean nodule was identified as Rhizobium pusense KG2 by phylogenetic analysis. Rhizobium pusense KG2 showed the 120 mg l-1 of minimal lethal concentration for Cd2+ . In 50 and 100 mg l-1 of Cd2+ liquid, approximately 2 × 1010 cells removed 56·71 and 22·11% of Cd2+ , respectively. In pot soil containing 50 and 100 mg kg-1 of Cd2+ , strain KG2 caused a 45·9 and 35·3% decrease in soybean root Cd content, respectively. Meanwhile, KG2 improved the root and shoot length, nitrogen content and biomass of soybean plants and superoxide dismutase activity.

CONCLUSIONS

The Cd2+ -immobilized rhizobia could inhibit soybean plants to absorb Cd2+ from soil, promote plant growth and improve plant's tolerance against Cd. This study is the first time to report that R. pusense is an effective nodulating rhizobium of legume.

SIGNIFICANCE AND IMPACT OF THE STUDY

Some Cd2+ -immobilized microbe lowering Cd uptake of plant and promoting plant growth should be considered as an effective strategy for producing safety crops in the Cd-contaminated agricultural soil.

Glioma cells promote angiogenesis through the release of exosomes containing long non-coding RNA POU3F3

OBJECTIVE

Angiogenesis is a key event in the progression of gliomas, and emerging evidence suggests that exosomes are signaling extracellular organelles that modulate the tumor microenvironment and promote angiogenesis and tumor progression. This study aimed to explore the mechanism by which glioma-derived exosomes affect angiogenesis.

MATERIALS AND METHODS

qRT-PCR was used to determine the expression level of linc-POU3F3 in glioma tissue as well as glioma cell lines. Ultrafiltration combined with a purification method was used to isolate exosomes derived from A172 cells (A172-Exo) and linc-POU3F3 shRNA-treated A172 cells (shA172-Exo). Transmission electron microscopy, Western blot and tunable resistive pulse sensing (TRPS) were used to identify exosomes. In vitro migration, proliferation, and tube formation experiments, as well as in vivo CAM assays, were used to analyze the pro-angiogenesis ability of exosomes. qRT-PCR and Western blot were used to identify expression levels of angiogenesis-related genes and proteins in human brain microvascular endothelial cells (HBMECs) after being cultured with exosomes.

RESULTS

The levels of linc-POU3F3 were upregulated in glioma tissue and significantly correlated with the advanced tumor stage. A172 cells exhibited the highest expression level. A172-Exo was similar to shA172-Exo (50-100 nm in diameter) and expressed Alix, Tsg101 and CD9, while the expression level of linc-POU3F3 in A172-Exo was significantly higher than that in shA172-Exo. HBMECs rapidly internalized A172-Exo and shA172-Exo, and the linc-POU3F3 expression level in HBMECs treated with A172-Exo was significantly higher than the level in HBMECs treated with shA172-Exo. A172-Exo exhibited better function in promoting HBMECs migration, proliferation, tubular-like structure formation in vitro and arteriole formation in vivo. The gene and protein expression level of bFGF, bFGFR, VEGFA, and Angio in HBMECs treated with A172-Exo was much higher than that of HBMECs treated with shA172-Exo.

CONCLUSIONS

These results indicated that gliomas can induce angiogenesis by secreting exosomes enriched in linc-POU3F3. Exosomes and lncRNA-POU3F3 may, therefore, function as a putative therapeutic target in glioma.

[Research progress of the mechanisms underlying cadmium-induced carcinogenesis]

Since cadmium is an indirect genotoxic carcinogen, epigenetic modifications could be one of the major mechanisms underlying cadmium-induced carcinogenesis. It has been proposed that epigenetic alterations might be associated with cadmium-induced carcinogenesis, such as disruptions of DNA methylation, histone modifications, and microRNA expression profile, which would result in abnormal expression of carcinogenesis-related genes and/or imbalance of signal transduction pathways. Other mechanisms of cadmium-induced carcinogenesis consist of disruption of gene regulation and signaling pathways, repression of DNA repair, suppression of apoptosis, induction of oxidative stress and autophagy. Here, we reviewed the research of both epigenetic and non-epigenetic mechanisms underlying cadmium-induced carcinogenesis.

Vitamin D Status and the Risk of Recurrent Stroke and Mortality in Ischemic Stroke Patients: Data from a 24-Month Follow-Up Study in China

PURPOSE

In this study, we determined serum 25-hydroxyvitamin D [25(OH) D] levels in serum, and investigated their associations with risk of recurrent stroke and mortality in a 24-month follow up study in Chinese patients with first-ever ischemic stroke.

METHODS

In this preplanned post hoc analysis, serum levels of 25(OH) D and NIH stroke scale (NIHSS) were measured at the time of admission in a cohort of patients with ischemic stroke. We used logistic regression model to assess the relationship between 25(OH) D levels and risk recurrent stroke or mortality.

RESULTS

The follow-up rate was 98.2% in 220 stroke patients. Of 216 patients, 18.5% (95%CI: 13.3%-23.7%) patients had a stroke recurrence, and 30.1% (95% CI: 24.0%-36.2%) died. After adjustment for traditional risk factors, serum 25(OH) D levels were negatively associated with the risk of stroke recurrence (odds ratio [OR], 0.77; 95% confidence interval [CI], 0.70-0.85; P < 0.001) and negatively associated with mortality during 24 months of follow-up (OR, 0.72; 95% CI, 0.64-0.80; P< 0.001). Compared with the first quartile of serum 25(OH) D levels, the ORs for stroke recurrence and mortality were as follows: second quartile, 0.80 (95% CI, 0.63-0.93) and 0.77 (95% CI, 0.65-0.89); third quartile, 0.42 (95% CI, 0.31-0.55) and 0.39 (95% CI, 0.30-0.52); fourth quartile, 0.12 (95% CI, 0.07-0.19) and 0.10 (95% CI, 0.06-0.15), respectively.

CONCLUSIONS

Lower serum levels of 25(OH) D are independently associated with the stroke recurrence and mortality at 24 months in ischemic stroke patients.

RAM-SCB simulations of electron transport and plasma wave scattering during the October 2012 "double-dip" storm

Mechanisms for electron injection, trapping, and loss in the near-Earth space environment are investigated during the October 2012 "double-dip" storm using our ring current-atmosphere interactions model with self-consistent magnetic field (RAM-SCB). Pitch angle and energy scattering are included for the first time in RAM-SCB using L and magnetic local time (MLT)-dependent event-specific chorus wave models inferred from NOAA Polar-orbiting Operational Environmental Satellites (POES) and Van Allen Probes Electric and Magnetic Field Instrument Suite and Integrated Science observations. The dynamics of the source (approximately tens of keV) and seed (approximately hundreds of keV) populations of the radiation belts simulated with RAM-SCB is compared with Van Allen Probes Magnetic Electron Ion Spectrometer observations in the morning sector and with measurements from NOAA 15 satellite in the predawn and afternoon MLT sectors. We find that although the low-energy (E< 100 keV) electron fluxes are in good agreement with observations, increasing significantly by magnetospheric convection during both SYM-H dips while decreasing during the intermediate recovery phase, the injection of high-energy electrons is underestimated by this mechanism throughout the storm. Local acceleration by chorus waves intensifies the electron fluxes at E≥50 keV considerably, and RAM-SCB simulations overestimate the observed trapped fluxes by more than an order of magnitude; the precipitating fluxes simulated with RAM-SCB are weaker, and their temporal and spatial evolutions agree well with POES/Medium Energy Proton and Electron Detectors data.

Identification of chromatin accessibility domains in human breast cancer stem cells

Epithelial-to-mesenchymal transition (EMT) is physiological in embryogenesis and wound healing but also associated with the formation of cancer stem cells (CSCs). Many EMT signaling pathways are implicated in CSC formation, but the precise underlying mechanisms of CSC formation remain elusive. We have previously demonstrated that PKC is critical for EMT induction and CSC formation in inducible breast EMT/CSC models. Here, we used formaldehyde-assisted isolation of regulatory elements-sequencing (FAIRE-seq) to investigate DNA accessibility changes after PKC activation and determine how they influence EMT and CSC formation. During EMT, DNA accessibility principally increased in regions distant from transcription start sites, low in CpG content, and enriched with chromatin enhancer marks. ChIP-sequencing revealed that a subset of these regions changed from poised to active enhancers upon stimulation, with some even more acteylated in CSCs. While regions with increased accessibility were enriched for FOX, AP-1, TEAD, and TFAP2 motifs, those containing FOX and AP-1 motif were associated with increased expression of CSC-associated genes, while those with TFAP2 were associated with genes with increased expression in non-CSCs. Silencing of 2 members of the FOX family, FOXN2 and FOXQ1, repressed CSCs and the mesenchymal phenotype and inhibited the CSC gene signature. These novel, PKC-induced DNA accessibility regions help explain how the epigenomic plasticity of cells undergoing EMT leads to CSC gene activation.

0.95 W high-repetition-rate, picosecond 335 nm laser based on a frequency quadrupled, diode-pumped Nd:YVO(4) MOPA system

An efficient all-solid-state picosecond (ps) ultraviolet (UV) laser at 335 nm was demonstrated based on frequency quadrupling of a mode-locked 1342 nm MOPA system. An output power of 0.95 W was obtained under a fundamental wave power of 16.38 W, corresponding to a conversion efficiency of 5.8% from infrared to UV. The repetition rate and pulse duration were 77 MHz and 20.2 ps, respectively. The beam quality factor M(2) was measured to be 1.56. This is, to the best of our knowledge, the highest output power at 335 nm.

Association of the IRF5 SNP rs2004640 with systemic sclerosis in Han Chinese

Systemic sclerosis (SSc) is a complex disease involving multiple genetic factors. An association of the IRF5 polymorphism with SSc was reported in Caucasian populations of Europe and North America, as well as in Japanese populations. The present study aimed to examine whether the SSc-associated SNP rs2004640 of IRF5 gene confer susceptibility to SSc and clinical features of SSc in a Han Chinese population. A Han Chinese cohort consisting of 424 SSc patients and 502 healthy controls were examined in the study. TaqMan assays were carried out to examine the SNP. Exact p-values were obtained (Fisher’s test) from 2x2 tables of allele counts and disease status. SSc patients of Han Chinese showed increased homozygous TT genotype of the rs2004640 (p = 0.027, odds ratio (OR) = 1.4, CI =1.03-1.93), which was significantly associated with pulmonary fibrosis of SSc and ATA-positive SSc of Han Chinese. The lcSSc and ACA-positive SSc of Han Chinese appeared also in association with the increased T allele frequency. However, the Chinese dcSSc did not show any association with the rs2004640. The results were consistent with previous reports in other ethnic populations in supporting the notion that polymorphisms of IRF5 may play an important role in susceptibility to SSc.

Serotonin in the ventral hippocampus modulates anxiety-like behavior during amphetamine withdrawal

Withdrawal from amphetamine is associated with increased anxiety and sensitivity to stressors which are thought to contribute to relapse. Rats undergoing amphetamine withdrawal fail to exhibit stress-induced increases in serotonin (5-HT) release in the ventral hippocampus and show heightened anxiety-like behaviors. Therefore, we tested the hypothesis that reducing 5-HT levels in the ventral hippocampus is a causal mechanism in increasing anxiety-like behaviors during amphetamine withdrawal. First, we tested whether reducing 5-HT levels in the ventral hippocampus directly increases anxiety behavior. Male rats were bilaterally infused with 5,7-dihydroxytryptamine (5,7-DHT) into the ventral hippocampus, which produced a 83% decrease in ventral hippocampus 5-HT content, and were tested on the elevated plus maze (EPM) for anxiety-like behavior. Reducing ventral hippocampus 5-HT levels decreased the time spent in the open arms of the maze, suggesting that diminished ventral hippocampus 5-HT levels increases anxiety-like behavior. Next, we tested whether increasing 5-HT levels in the ventral hippocampus reverses anxiety behavior exhibited by rats undergoing amphetamine withdrawal. Rats were treated daily with either amphetamine (2.5-mg/kg, i.p.) or saline for 2weeks, and at 2weeks withdrawal, were infused with the selective serotonin reuptake inhibitor paroxetine (0.5μM) bilaterally into the ventral hippocampus and tested for anxiety-like behavior on the EPM. Rats pre-treated with amphetamine exhibited increased anxiety-like behavior on the EPM. This effect was reversed by ventral hippocampus infusion of paroxetine. Our results suggest that 5-HT levels in the ventral hippocampus are critical for regulating anxiety behavior. Increasing 5-HT levels during withdrawal may be an effective strategy for reducing anxiety-induced drug relapse.

VNP: Interactive Visual Network Pharmacology of Diseases, Targets, and Drugs

In drug discovery, promiscuous targets, multifactorial diseases, and "dirty" drugs construct complex network relationships. Network pharmacology description and analysis not only give a systems-level understanding of drug action and disease complexity but can also help to improve the efficiency of target selection and drug design. Visual network pharmacology (VNP) is developed to visualize network pharmacology of targets, diseases, and drugs with a graph network by using disease, target or drug names, chemical structures, or protein sequence. To our knowledge, VNP is the first free interactive VNP server that should be very helpful for systems pharmacology research. VNP is freely available at http://cadd.whu.edu.cn/ditad/vnpsearch.

The use of propensity scores in pharmacoepidemiologic research

PURPOSE

To describe the application of propensity score analysis in pharmacoepidemiologic research using a study comparing the renal effects of two commonly prescribed non-steroidal anti-inflammatory drugs (NSAIDs).

METHOD

Observational data were collected on the change in renal function, as measured by serum creatinine concentration, before and after use of two NSAIDs, Ibuprofen and Sulindac. To estimate the treatment effect of the different NSAIDs, we used the propensity score methodology to reduce the potential confounding effects caused by unbalanced covariates. After estimating the propensity scores (the probabilities of each patient being prescribed Sulindac) from a logistic regression model, we stratified the data based on sample quintiles of the propensity score distribution. The final estimate of the treatment effect was then obtained by averaging the treatment estimates from the stratified samples.

RESULTS

Initially, 23 covariates differed significantly between the two treatment groups. Using the propensity score methodology, we were able to balance the distributions of 16 covariates. The imbalances in the remaining seven covariates were also greatly reduced. Although the use of either drug resulted in a decrease in renal function, overall differences between them were not statistically significant with respect to their effect on creatinine concentrations based on the propensity score analysis.

CONCLUSION

Observational studies often produce treatment groups that are not directly comparable due to imbalances in covariate distributions between the treatment groups. Propensity score analysis provides a simple and effective way of controlling the effects of these covariates and obtaining a less biased estimate of the treatment effect. Copyright (c) 2000 John Wiley & Sons, Ltd.

Low-level persistence of human papillomavirus 16 DNA in a cohort of closely followed adolescent women

Most human papillomavirus (HPV) infections in young women become undetectable by standard assays after a few months. It is possible that many HPV infections do not actually clear, but persist at very low levels for years, becoming detected again later in life. The purpose of this study is to describe HPV 16 clearance, reappearance, and low-level persistence in a cohort of adolescent women. Adolescent women (N = 66), not vaccinated against HPV, were recruited from 1998 to 2008 into a longitudinal study. Self-collected vaginal samples were obtained quarterly and tested for HPV by Linear Array HPV Genotyping Test (LA-HPV). To explore low-level persistence, a type-specific nested PCR for HPV 16 (TSN-PCR-16) was developed. Women with HPV 16 detected by LA-HPV had their negative swabs retested with TSN-PCR-16. Forty-two participants with HPV 16, followed for a mean of 6.3 years, were analyzed. Using LA-HPV, the median duration of HPV 16 detection was 428 days (SD 852.5 days). TSN-PCR-16 detected HPV 16 during periods of LA-HPV non-detection in samples from many women. Using a combination of LA-HPV and TSN-PCR-16 results, the median duration of HPV 16 detection was 1,022.5 days (SD 943.7 days). The durations of detection differed significantly between the two methods (P = 0.0042) with a mean difference of 434.5 days. In adolescent females, duration of HPV 16 detection was significantly longer when TSN-PCR-16 was combined with LA-HPV. Some apparently cleared HPV 16 could be shown to persist at low levels using nested PCR.