Experience with aortic arch inclusion technique using artificial blood vessel for type A aortic dissection: an application study.

BACKGROUND: This study aimed to elucidate the methodology and assess the efficacy of the aortic arch inclusion technique using an artificial blood vessel in managing acute type A aortic dissection (ATAAD). METHODS: We conducted a retrospective review of 18 patients (11 males and 7 females, average age: 56.2 ± 8.6 years) diagnosed with ATAAD who underwent total aortic arch replacement (TAAR) using an artificial vascular "inclusion" between June 2020 and October 2022. During the operation, deep hypothermic circulatory arrest (DHCA) and selective antegrade cerebral perfusion (ACP) of the right axillary artery were employed for brain protection. The 'inclusion' total aortic arch replacement and stented elephant trunk (SET) surgery were performed. RESULTS: Four patients underwent the Bentall procedure during the study, with one additional patient requiring coronary artery bypass grafting (CABG) due to significant involvement of the right coronary orifice. Three patients died during postoperative hospitalization. Other notable complications included two cases of postoperative renal failure necessitating continuous renal replacement therapy (CRRT), one case of postoperative double lower limb paraplegia, and one case of cerebral infarction resulting in unilateral impairment of the left upper limb. Eleven patients underwent computed tomography angiography (CTA) examinations of the aorta three months to one-year post-operation. The CTA results revealed thrombosis in the false lumen surrounding the aortic arch stent in seven patients and complete thrombosis of the false lumen around the descending aortic stent in eight patients. One patient had partial thrombosis of the false lumen around the descending aortic stent, and another patient's false lumen in the thoracic and abdominal aorta completely resolved after one year of follow-up. CONCLUSIONS: Incorporating vascular graft in aortic arch replacement simplifies the procedure and yields promising short-term outcomes. It achieves the aim of total arch replacement using a four-branch prosthetic graft. However, extensive sampling and thorough, prolonged follow-up observations are essential to fully evaluate the long-term results.

Study on the benefit analysis based on whole life cycle carbon emission calculation after the construction of photovoltaic systems in macau's construction waste landfills.

This study seeks to assess both environmental and economic effects associated with installing photovoltaic systems within construction waste landfills in Macau by employing an effective carbon emissions calculation methodology and benefit analysis method. Beginning by outlining characteristics and challenges associated with construction waste landfills, as well as photovoltaic systems used for this application in this paper. Here, we present a detailed outline of our methodology design, outlining its principles of life cycle analysis, data collection processes and the creation of carbon emissions calculation models. Subsequently, we examine photovoltaic systems within Macau's construction waste landfills by studying system design, component selection and operational strategies as well as carbon emission data collection during their operational time period. Under life cycle carbon emissions calculations, we assess the carbon emissions generated from photovoltaic systems as well as conduct an environmental and economic benefit analysis for carbon reduction benefit analysis purposes. This research incorporates sensitivity analysis and uncertainty consideration in order to conduct an extensive benefit analysis. The research results offer strong support for sustainable photovoltaic systems within Macau waste landfills as well as insights to inform planning and policy formation for similar future projects.

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD.

BACKGROUND: Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. METHODS: This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. RESULTS: Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869-0.906], 0.874 (95%CI 0.844-0.904) and 0.846 (95%CI 0.822-0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model. CONCLUSIONS: The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.

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.

Deep learning-based dose map prediction for high-dose-rate brachytherapy.

Background. Creating a clinically acceptable plan in the time-sensitive clinic workflow of brachytherapy is challenging. Deep learning-based dose prediction techniques have been reported as promising solutions with high efficiency and accuracy. However, current dose prediction studies mainly target EBRT which are inappropriate for brachytherapy, the model designed specifically for brachytherapy has not yet well-established.Purpose. To predict dose distribution in brachytherapy using a novel Squeeze and Excitation Attention Net (SE_AN) model.Method. We hypothesized the tracks of192Ir inside applicators are essential for brachytherapy dose prediction. To emphasize the applicator contribution, a novel SE module was integrated into a Cascaded UNet to recalibrate informative features and suppress less useful ones. The Cascaded UNet consists of two stacked UNets, with the first designed to predict coarse dose distribution and the second added for fine-tuning 250 cases including all typical clinical applicators were studied, including vaginal, tandem and ovoid, multi-channel, and free needle applicators. The developed SE_AN was subsequently compared to the classic UNet and classic Cascaded UNet (without SE module) models. The model performance was evaluated by comparing the predicted dose against the clinically approved plans using mean absolute error (MAE) of DVH metrics, includingD2ccandD90%.Results. The MAEs of DVH metrics demonstrated that SE_AN accurately predicted the dose with 0.37 ± 0.25 difference for HRCTVD90%, 0.23 ± 0.14 difference for bladderD2cc, and 0.28 ± 0.20 difference for rectumD2cc. In comparison studies, UNet achieved 0.34 ± 0.24 for HRCTV, 0.25 ± 0.20 for bladder, 0.25 ± 0.21 for rectum, and Cascaded UNet achieved 0.42 ± 0.31 for HRCTV, 0.24 ± 0.19 for bladder, 0.23 ± 0.19 for rectum.Conclusion. We successfully developed a method specifically for 3D brachytherapy dose prediction. Our model demonstrated comparable performance to clinical plans generated by experienced dosimetrists. The developed technique is expected to improve the standardization and quality control of brachytherapy treatment planning.

CT-Based Radiomic Nomogram for the Prediction of Chronic Obstructive Pulmonary Disease in Patients with Lung cancer.

RATIONALE AND OBJECTIVES: To develop and validate a model for predicting chronic obstructive pulmonary disease (COPD) in patients with lung cancer based on computed tomography (CT) radiomic signatures and clinical and imaging features. MATERIALS AND METHODS: We retrospectively enrolled 443 patients with lung cancer who underwent pulmonary function test as the primary cohort. They were randomly assigned to the training (n = 311) or validation (n = 132) set in a 7:3 ratio. Additionally, an independent external cohort of 54 patients was evaluated. The radiomic lung nodule signature was constructed using the least absolute shrinkage and selection operator algorithm, while key variables were selected using logistic regression to develop the clinical and combined models presented as a nomogram. RESULTS: COPD was significantly related to the radiomics signature in both cohorts. Moreover, the signature served as an independent predictor of COPD in the multivariate regression analysis. For the training, internal, and external cohorts, the area under the receiver operating characteristic curve (ROC, AUC) values of our radiomics signature for COPD prediction were 0.85, 0.85, and 0.76, respectively. Additionally, the AUC values of the radiomic nomogram for COPD prediction were 0.927, 0.879, and 0.762 for the three cohorts, respectively, which outperformed the other two models. CONCLUSION: The present study presents a nomogram that incorporates radiomics signatures and clinical and radiological features, which could be used to predict the risk of COPD in patients with lung cancer with one-stop chest CT scanning.

Bio-Inspired FeMo2S4 Microspheres as Bifunctional Electrocatalysts for Boosting Hydrogen Oxidation/Evolution Reactions in Alkaline Solution.

Developing robust and effectual nonprecious electrocatalysts for the bifunctional hydrogen oxidation and evolution reactions (HOR and HER) in alkaline electrolyte is of critical significance for the realization of future hydrogen economy but challenging. Herein, this work demonstrates a new routine for the preparation of bio-inspired FeMo2S4 microspheres via the one-step sulfuration of Keplerate-type polyoxometalate {Mo72Fe30}. The bio-inspired FeMo2S4 microspheres feature potential-abundant structural defects and atomically precise iron doping and act as an effective bifunctional electrocatalyst for hydrogen oxidation/reduction reactions. The FeMo2S4 catalyst presents an impressive alkaline HOR activity compared to FeS2 and MoS2 with the high mass activity of 1.85 mA·mg-1 and high specific activity as well as excellent tolerance to carbon monoxide poisoning. Meanwhile, FeMo2S4 electrocatalyst also displayed prominent alkaline HER activity with a low overpotential of 78 mV at a current density of 10 mA·cm-2 and robust long-term durableness. Density functional theory (DFT) calculations indicate that the bio-inspired FeMo2S4 with a unique electron structure possesses the optimal hydrogen adsorption energy and enhanced adsorption of hydroxyl intermediates, which accelerates the potential-determining Volmer step, thus promoting the HOR and HER performance. This work provides a new pathway for designing efficient noble-metal-free electrocatalysts for the hydrogen economy.

Three-Dimensional Silver-Containing Polyoxotungstate Frameworks for Photocatalytic Aerobic Oxidation of Benzyl Alcohol.

Photocatalytic organic transformation derived by functionalized polyoxometalate (POM)-based metal-organic frameworks provides a feasible route for fine chemical synthesis. Herein, three kinds of photoactive three-dimensional silver-containing polyoxotungstate frameworks are synthesized with the formulas [Ag3L2(OH)][Na(H2O)0.5][PW12O40]·H2O (1), [Ag4L3][SiW12O40] (2), and [Ag(H2O)][Ag4L3][BW12O40]·9H2O (3) (L = 1,4-di(4H-1,2,4-triazol-4-yl)benzene). In compounds 1-3, the cationic Ag-triazole clusters with diverse nuclei serve as nodes to assemble with rigid bridging ligands (L) and polyoxoanions to extend into stable three-dimensional frameworks, in which Keggin-type anions act as guests or pendants. When using them as heterogeneous photocatalysts, compounds 1-3 show high catalytic activity and selectivity for the photocatalytic aerobic oxidation of benzyl alcohol to benzoic acid under 10 W 365 nm light irradiation. Among them, compound 1 exhibits the highest performance with ca. 99% benzyl alcohol conversion and 99% selectivity of benzoic acid in 9 h. Compounds 2 and 3 show ca. 79 and 88% conversions of benzyl alcohol, respectively, which are higher than those of the individual Keggin-type precursors. Moreover, mechanism investigation suggests that the synergistic cooperation occurring between cationic Ag-triazole clusters and Keggin-type polyoxoanions modulates the energy band structures of compounds 1-3, resulting in the efficient separation of photogenerated carriers and accelerating the aerobic oxidation of benzyl alcohol. This work provides some important guidance for the design and development of efficient POM-based photocatalysts for practical organic transformation.

Applications of machine learning in tumor-associated macrophages.

Evaluation of tumor-host interaction and intratumoral heterogeneity in the tumor microenvironment (TME) is gaining increasing attention in modern cancer therapies because it can reveal unique information about the tumor status. As tumor-associated macrophages (TAMs) are the major immune cells infiltrating in TME, a better understanding of TAMs could help us further elucidate the cellular and molecular mechanisms responsible for cancer development. However, the high-dimensional and heterogeneous data in biology limit the extensive integrative analysis of cancer research. Machine learning algorithms are particularly suitable for oncology data analysis due to their flexibility and scalability to analyze diverse data types and strong computation power to learn underlying patterns from massive data sets. With the application of machine learning in analyzing TME, especially TAM's traceable status, we could better understand the role of TAMs in tumor biology. Furthermore, we envision that the promotion of machine learning in this field could revolutionize tumor diagnosis, treatment stratification, and survival predictions in cancer research. In this article, we described key terms and concepts of machine learning, reviewed the applications of common methods in TAMs, and highlighted the challenges and future direction for TAMs in machine learning.

A personalized DVH prediction model for HDR brachytherapy in cervical cancer treatment.

Purpose: Although the knowledge-based dose-volume histogram (DVH) prediction has been largely researched and applied in External Beam Radiation Therapy, it is still less investigated in the domain of brachytherapy. The purpose of this study is to develop a reliable DVH prediction method for high-dose-rate brachytherapy plans. Method: A DVH prediction workflow combining kernel density estimation (KDE), k-nearest neighbor (kNN), and principal component analysis (PCA) was proposed. PCA and kNN were first employed together to select similar patients based on principal component directions. 79 cervical cancer patients with different applicators inserted was included in this study. The KDE model was built based on the relationship between distance-to-target (DTH) and the dose in selected cases, which can be subsequently used to estimate the dose probability distribution in the validation set. Model performance of bladder and rectum was quantified by |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the form of mean and standard deviation. The model performance between KDE only and the combination of kNN, PCA, and KDE was compared. Result: 20, 30 patients were selected for rectum and bladder based on KNN and PCA, respectively. The absolute residual between the actual plans and the predicted plans were 0.38 ± 0.29, 0.4 ± 0.32, 0.43 ± 0.36, 0.97 ± 0.66, and 0.13 ± 0.99 for |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the bladder, respectively. For rectum, the corresponding results were 0.34 ± 0.27, 0.38 ± 0.33, 0.63 ± 0.57, 1.41 ± 0.99 and 0.23 ± 0.17, respectively. The combination of kNN, PCA, and KDE showed a significantly better prediction performance than KDE only, with an improvement of 30.3% for the bladder and 33.3% for the rectum. Conclusion: In this study, a knowledge-based machine learning model was proposed and verified to accurately predict the DVH for new patients. This model is proved to be effective in our testing group in the workflow of HDR brachytherapy.

A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy.

PURPOSE: Fast and accurate outlining of the organs at risk (OARs) and high-risk clinical tumor volume (HRCTV) is especially important in high-dose-rate brachytherapy due to the highly time-intensive online treatment planning process and the high dose gradient around the HRCTV. This study aims to apply a self-configured ensemble method for fast and reproducible auto-segmentation of OARs and HRCTVs in gynecological cancer. MATERIALS AND METHODS: We applied nnU-Net (no new U-Net), an automatically adapted deep convolutional neural network based on U-Net, to segment the bladder, rectum and HRCTV on CT images in gynecological cancer. In nnU-Net, three architectures, including 2D U-Net, 3D U-Net and 3D-Cascade U-Net, were trained and finally ensembled. 207 cases were randomly chosen for training, and 30 for testing. Quantitative evaluation used well-established image segmentation metrics, including dice similarity coefficient (DSC), 95% Hausdorff distance (HD95%), and average surface distance (ASD). Qualitative analysis of automated segmentation results was performed visually by two radiation oncologists. The dosimetric evaluation was performed by comparing the dose-volume parameters of both predicted segmentation and human contouring. RESULTS: nnU-Net obtained high qualitative and quantitative segmentation accuracy on the test dataset and performed better than previously reported methods in bladder and rectum segmentation. In quantitative evaluation, 3D-Cascade achieved the best performance in the bladder (DSC: 0.936 ± 0.051, HD95%: 3.503 ± 1.956, ASD: 0.944 ± 0.503), rectum (DSC: 0.831 ± 0.074, HD95%: 7.579 ± 5.857, ASD: 3.6 ± 3.485), and HRCTV (DSC: 0.836 ± 0.07, HD95%: 7.42 ± 5.023, ASD: 2.094 ± 1.311). According to the qualitative evaluation, over 76% of the test data set had no or minor visually detectable errors in segmentation. CONCLUSION: This work showed nnU-Net's superiority in segmenting OARs and HRCTV in gynecological brachytherapy cases in our center, among which 3D-Cascade shows the highest accuracy in segmentation across different applicators and patient anatomy.

Revisiting the target-masker linguistic similarity hypothesis.

The linguistic similarity hypothesis states that it is more difficult to segregate target and masker speech when they are linguistically similar. For example, recognition of English target speech should be more impaired by the presence of Dutch masking speech than Mandarin masking speech because Dutch and English are more linguistically similar than Mandarin and English. Across four experiments, English target speech was consistently recognized more poorly when presented in English masking speech than in silence, speech-shaped noise, or an unintelligible masker (i.e., Dutch or Mandarin). However, we found no evidence for graded masking effects-Dutch did not impair performance more than Mandarin in any experiment, despite 650 participants being tested. This general pattern was consistent when using both a cross-modal paradigm (in which target speech was lipread and maskers were presented aurally; Experiments 1a and 1b) and an auditory-only paradigm (in which both the targets and maskers were presented aurally; Experiments 2a and 2b). These findings suggest that the linguistic similarity hypothesis should be refined to reflect the existing evidence: There is greater release from masking when the masker language differs from the target speech than when it is the same as the target speech. However, evidence that unintelligible maskers impair speech identification to a greater extent when they are more linguistically similar to the target language remains elusive.

Experimental study of the dispersion of cough-generated droplets from a person going up- or downstairs.

The dispersion of cough-generated droplets from a person going up- or downstairs was investigated through a laboratory experiment in a water tunnel. This experiment was carried out with a manikin mounted at inclination angles facing the incoming flow to mimic a person going up or down. Detailed velocity measurements and flow visualization were conducted in the water tunnel experiments. To investigate the influence of the initial position on the motion of particles, a virtual particle approach was adopted to simulate the dispersion of particles using the measured velocity field. Particle clustering, which is caused by the unsteadiness of the flow, was observed in both flow visualization and virtual particle simulation. For the case of going upstairs, particles are concentrated below the person's shoulder and move downward with a short travel distance. For the case of going downstairs, particles dispersing over the person's head advect over for a long distance. We also found that the motion of the particles is closely related to the initial position. According to the results in this study, suggestions for the prevention of respiratory infectious disease are made.

LexFindR: A fast, simple, and extensible R package for finding similar words in a lexicon.

Language scientists often need to generate lists of related words, such as potential competitors. They may do this for purposes of experimental control (e.g., selecting items matched on lexical neighborhood but varying in word frequency), or to test theoretical predictions (e.g., hypothesizing that a novel type of competitor may impact word recognition). Several online tools are available, but most are constrained to a fixed lexicon and fixed sets of competitor definitions, and may not give the user full access to or control of source data. We present LexFindR, an open-source R package that can be easily modified to include additional, novel competitor types. LexFindR is easy to use. Because it can leverage multiple CPU cores and uses vectorized code when possible, it is also extremely fast. In this article, we present an overview of LexFindR usage, illustrated with examples. We also explain the details of how we implemented several standard lexical competitor types used in spoken word recognition research (e.g., cohorts, neighbors, embeddings, rhymes), and show how "lexical dimensions" (e.g., word frequency, word length, uniqueness point) can be integrated into LexFindR workflows (for example, to calculate "frequency-weighted competitor probabilities"), for both spoken and visual word recognition research.

Effects of slope and speed of escalator on the dispersion of cough-generated droplets from a passenger.

During the pandemic of COVID-19, the public is encouraged to take stairs or escalators instead of elevators. However, the dispersion of respiratory droplets in these places, featured by slopes and human motion, is not well understood yet. It is consequently unclear whether the commonly recommended social-distancing guidelines are still appropriate in these scenarios. In this work, we analyze the dispersion of cough-generated droplets from a passenger riding an escalator with numerical simulations, focusing on the effects of the slope and speed of the escalator on the droplet dispersion. In the simulations, a one-way coupled Eulerian-Lagrangian approach is adopted, with the air-flow solved using the Reynolds-averaged Navier-Stokes method and the droplets modeled as passive Lagrangian particles. It is found that the slope alters the vertical concentration of the droplets in the passenger's wake significantly. The deflection of cough-generated jet and the wake flow behind the passenger drive the cough-generated droplets upwards when descending an escalator and downwards when ascending, resulting in both higher suspension height and larger spreading range of the viral droplets on a descending escalator than on an ascending one. These findings suggest that the present social-distancing guidelines may be inadequate on descending escalators and need further investigation.

Effects of space sizes on the dispersion of cough-generated droplets from a walking person.

The dispersion of viral droplets plays a key role in the transmission of COVID-19. In this work, we analyze the dispersion of cough-generated droplets in the wake of a walking person for different space sizes. The air flow is simulated by solving the Reynolds-averaged Navier-Stokes equations, and the droplets are modeled as passive Lagrangian particles. Simulation results show that the cloud of droplets locates around and below the waist height of the manikin after 2 s from coughing, which indicates that kids walking behind an infectious patient are exposed to higher transmission risk than adults. More importantly, two distinct droplet dispersion modes occupying significantly different contamination regions are discovered. A slight change of space size is found being able to trigger the transition of dispersion modes even though the flow patterns are still similar. This shows the importance of accurately simulating the air flow in predicting the dispersion of viral droplets and implies the necessity to set different safe-distancing guidelines for different environments.

The motion of respiratory droplets produced by coughing.

Coronavirus disease 2019 has become a global pandemic infectious respiratory disease with high mortality and infectiousness. This paper investigates respiratory droplet transmission, which is critical to understanding, modeling, and controlling epidemics. In the present work, we implemented flow visualization, particle image velocimetry, and particle shadow tracking velocimetry to measure the velocity of the airflow and droplets involved in coughing and then constructed a physical model considering the evaporation effect to predict the motion of droplets under different weather conditions. The experimental results indicate that the convection velocity of cough airflow presents the relationship t -0.7 with time; hence, the distance from the cougher increases by t 0.3 in the range of our measurement domain. Substituting these experimental results into the physical model reveals that small droplets (initial diameter D ≤ 100 µm) evaporate to droplet nuclei and that large droplets with D ≥ 500 µm and an initial velocity u 0 ≥ 5 m/s travel more than 2 m. Winter conditions of low temperature and high relative humidity can cause more droplets to settle to the ground, which may be a possible driver of a second pandemic wave in the autumn and winter seasons.

The importance of CT quantitative evaluation of emphysema in lung cancer screening cohort with negative findings by visual evaluation.

INTRODUCTION: One-stop quantitative evaluation of emphysema and lung nodule in lung cancer screening is very important for patient. OBJECTIVE: To evaluate the quantitative emphysema in the large-sample low-dose CT lung cancer screening cohort with negative CT findings by subjective visual assessment. METHODS: One thousand, two hundred and thirty-one participants with negative visual evaluation were included in this retrospective study. The lungs were automatically segmented and the following were calculated: total lung volume (TLV), total emphysema volume (TEV), emphysema index (EI), 15th percentile lung density and mean lung density. EI ≥6% was defined as emphysema. The quantitative parameters were compared between different genders and ages. The quantitative parameters and risk factors were compared between emphysema and non-emphysema groups. RESULTS: The proportion of smokers, TLV, TEV and EI of men were greater than that of women (P < 0.001). No correlation was found between age and volumes; the TEV and EI of people older than 60 years were greater than those younger than 60 years (P < 0.05) by age categorisation. One hundred and two participants showed emphysema, accounting for 8.29%. The incidence of emphysema in men was greater than that in women in total (P < 0.05). All the CT quantitative parameters were significantly different between emphysema and non-emphysema groups. The ratio of male, secondhand smoke exposure and chronic bronchitis history was greater in emphysema than that in the non-emphysema group (P < 0.05). CONCLUSION: CT quantitative emphysema evaluation is recommended in people older than 60 years, especially in males, providing more precise information, aiding the early diagnosis of emphysema and informing early intervention.

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.