HRCT findings predict 1-year mortality in patients with acute exacerbation of idiopathic inflammatory myopathies-associated interstitial lung disease.

Background: Acute exacerbation of idiopathic inflammatory myopathies-associated interstitial lung disease (AE-IIM-ILD) is a significant event associated with increased morbidity and mortality. However, few studies investigated the potential prognostic factors contributing to mortality in patients who experience AE-IIM-ILD. Objectives: The purpose of our study was to comprehensively investigate whether high-resolution computed tomography (HRCT) findings predict the 1-year mortality in patients who experience AE-IIM-ILD. Methods: A cohort of 69 patients with AE-IIM-ILD was retrospectively created. The cohort was 79.7 % female, with a mean age of 50.7. Several HRCT features, including total interstitial lung disease extent (TIDE), distribution patterns, and radiologic ILD patterns, were assessed. A directed acyclic graph (DAG) was used to evaluate the statistical relationship between variables. The Cox regression method was performed to identify potential prognostic factors associated with mortality. Results: The HRCT findings significantly associated with AE-IIM-ILD mortality include TIDE (HR per 10%-increase, 1.64; 95%CI, 1.29-2.1, p < 0.001; model 1: C-index, 0.785), diffuse distribution pattern (HR, 3.75, 95%CI, 1.5-9.38, p = 0.005; model 2: C-index, 0.737), and radiologic diffuse alveolar damage (DAD) pattern (HR, 6.37, 95 % CI, 0.81-50.21, p = 0.079; model 3: C-index, 0.735). TIDE greater than 58.33 %, diffuse distribution pattern, and radiologic DAD pattern correlate with poor prognosis. The 90-day, 180-day, and 1-year survival rates of patients who experience AE-IIM-ILD were 75.3 %, 66.3 %, and 63.3 %, respectively. Conclusion: HRCT findings, including TIDE, distribution pattern, and radiological pattern, are predictive of 1-year mortality in patients who experience AE-IIM-ILD.

Determination of pulmonary vessel alteration in Chinese male smokers by quantitative computed tomography measurements: a retrospective study.

Background: The blood volume of intraparenchymal vessels is reported to be increased in smokers. However, the blood volume can be affected by many confounders besides tobacco exposure. This study aimed to investigate the association between cigarette smoking and pulmonary blood volume after adjusting the related factors in a large cohort of Chinese males. Methods: In this retrospective study, male participants admitted to the First Affiliated Hospital of Xi'an Jiaotong University for annual health assessment between February 2017 and February 2018 were enrolled. All subjects underwent non-contrast chest computed tomography (CT) scans, and 152 subjects underwent a review CT scan 2-3 years later. A three-dimensional approach was employed to segment the lung and intrapulmonary vessels and quantitative CT (QCT) measurements, including lung volume (LV), intrapulmonary vessel volume (IPVV), low-attenuation area <-950 Hounsfield unit (LAA-950 and LAA-950%), and mean lung density (MLD). Linear regression was used to estimate the association between IPVV and the smoking index (SI). A paired t-test was used to compare the QCT parameters between the initial and follow-up CT scans. Results: A total of 656 male participants were enrolled and classified into three subgroups: non-smokers (n=311), current smokers (n=267), and former smokers (n=78). The IPVV of current smokers (134.62±23.96 vs. 120.76±25.52 mL) and former smokers (130.79±25.13 vs. 120.76±25.52 mL) were significantly larger than that of non-smokers (P<0.05). A higher SI was associated with greater IPVV [non-standardized coefficient: 0.167, 95% confidence interval (CI): 0.086-0.248]. For current smokers, the IPVV of the follow-up scan significantly increased compared to its baseline scan (135.49±28.60 vs. 129.73±29.75 mL, t=-2.326, P=0.02), but for the non-smokers and former smokers, the IPVV of the follow-up scan did not increase or decrease compared to the baseline scan (P>0.05). Conclusions: Pulmonary vascular volumes detectable on non-contrast CT are associated with cigarette exposure, and smoking cessation may prevent pulmonary vasculature remodeling.

Differentiating malignant and benign pleural effusion in patients with lung cancer: an 18F-FDG PET/CT retrospectively study.

Rationale: To explore the clinical role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in differentiating malignant pleural effusion (MPE) from benign pleural effusion (BPE) in patients with lung cancer. Methods: Over a 8-year period, we retrospectively reviewed PET/CT data of lung cancer patients with pleural effusion, with 237 participants enrolled for analysis. The nature of pleural effusion was confirmed using pleural cytology or biopsy. MPE versus BPE comparison and multiple regression analysis were performed. Receiver operating characteristic (ROC) curve analysis was used for evaluating the diagnostic performance. Results: Of the 237 participants, 170 had MPEs and 67 had BPEs. Compared with BPEs, MPEs had higher pleural SUVmax and thicker pleura and were more common among non-small cell lung cancers, peripheral tumors, and women (p < 0.05). BPEs had larger and higher 18F-FDG uptake thoracic lymph nodes and more complications of pneumonia (p < 0.05) than MPEs. Multiple regression analysis was used to identify the pleural SUVmax (odds ratio, OR = 38.8), sex (OR = 0.033), and mediastinal lymphoid node size (OR = 0.86) as independent risk factors for MPEs. The sensitivity, specificity, and area under the ROC curve (AUC) in the combined ROC curve analysis by using the three factors were 95.3%, 95.5%, and 0.989, respectively. Conclusion: 18F-FDG PET/CT integrated imaging is an effective non-invasive method for differential diagnosis of MPE in patients with lung cancer. Pleural SUVmax combined with thoracic lymph nodes and sex has high diagnostic value.

Rapid Identification Method for CH4/CO/CH4-CO Gas Mixtures Based on Electronic Nose.

The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH4, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH4, CO, and their mixed gases.

Combination of clinical and MRI features in diagnosing ovarian granulosa cell tumor: A comparison with other ovarian sex cord-gonadal stromal tumors.

PURPOSE: To evaluate the combination of magnetic resonance imaging (MRI) findings and clinical features in diagnosing ovarian granulosa cell tumor (OGCT) and comparing OGCTs with other ovarian sex cord-gonadal stromal tumors (OSGTs). METHODS: Women who underwent MRI and were surgically confirmed with OSGTs between January 2015 and January 2022 were included in the study. Histology was used as a primary method of diagnosis. T1WI, T2WI, and DWI MR scans were performed for all patients. All MR images were reviewed by two radiologists. The clinic baseline characteristics of all patients were recorded. RESULTS: A total of 58 patients were enrolled, with 21 OGCTs found in 20 patients and 39 other OSGTs found in 38 patients. In terms of clinical, the proportion of vaginal discharge/bleeding and menstrual abnormalities were significantly higher in OGCTs than in the control group. A multivariate analysis of the combined clinical MRI revealed that symptomatic, T2 signals of the solid component, Honeycomb-sign, Swiss cheese-sign, and ADC values were independent features for discriminating between OGCTs and other OSGTs. Clinical features, MRI features, and a combined model were established; the areas under the curve of the three models in predicting OGCTs and other OSGTs were 0.694, 0.852, and 0.927, respectively. The DeLong test showed that the combined model had the highest efficiency in predicting OGCTs (p < 0.05), which was significantly different from the AUC of the other two models (p < 0.05). CONCLUSIONS: Combining clinic and MRI findings helps differentiate OGCTs from other OSGTs. These results help optimize clinical management and indicate that radiologists should focus on clinical information to help improve diagnostic accuracy.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology.

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

Contributions of Emphysema and Functional Small Airway Disease on Intrapulmonary Vascular Volume in COPD.

Background: Previous studies have demonstrated that there is a certain correlation between emphysema and changes in pulmonary small blood vessels in patients with chronic obstructive pulmonary disease (COPD), but most of them were limited to the investigation of the inspiratory phase. The emphysema indicators need to be further optimized. Based on the parametric response mapping (PRM) method, this study aimed to investigate the effect of emphysema and functional small airway disease on intrapulmonary vascular volume (IPVV). Methods: This retrospective study enrolled 63 healthy subjects and 47 COPD patients, who underwent both inspiratory and expiratory CT scans of the chest and pulmonary function tests (PFTs). Inspiratory and expiratory IPVV were measured by using an automatic pulmonary vessels integration segmentation approach, the ratio of emphysema volume (Emph%), functional small airway disease volume (fsAD%), and normal areas volume (Normal%) were quantified by the PRM method for biphasic CT scans. The participants were grouped according to PFTs. Analysis of variance (ANOVA) and Kruskal-Wallis H-test were used to analyze the differences in indicators between different groups. Then, Spearman's rank correlation coefficients were used to analyze the correlation between Emph%, fsAD%, Normal%, PFTs, and IPVV. Finally, multiple linear regression was applied to analyze the effects of Emph% and fsAD% on IPVV. Results: Differences were found in age, body mass index (BMI), smoking index, FEV1%, FEV1/forced vital capacity (FVC), expiratory IPVV, IPVV relative value, IPVV difference value, Emph%, fsAD%, and Normal% between the groups (P<0.05). A strong correlation was established between the outcomes of PFTs and quantitative CT indexes. Finally, the effect of Emph% was more significant than that of fsAD% on expiratory IPVV, IPVV difference value, and IPVV relative value. Conclusion: IPVV may have a potential value in assessing COPD severity and is significantly affected by emphysema.

Classification of Thyroid Nodules by Using Deep Learning Radiomics Based on Ultrasound Dynamic Video.

OBJECTIVES: We aimed to design a radiomics model for differential diagnosis of thyroid carcinoma based on dynamic ultrasound video, and compare its diagnostic performance with that of radiomics model based on static ultrasound images. METHODS: Between January 2019 and May 2021, 890 patients with 1015 thyroid nodules (775 for training, 240 for validation) were prospectively enrolled. In total 890 patients underwent thyroidectomy within 1 month, and ultrasound dynamic video and static images were both acquired. Two deep learning (DL) models, namely DL-video and DL-image models, were proposed to diagnose thyroid nodules by analyzing ultrasound video and static images respectively. The performance of models was assessed by areas under the receiver operating characteristic curve (AUC). The DL model on ultrasound cines was re-visualized to help radiologists understand its potential working mechanism. RESULTS: The AUC of DL-video were 0.947 (95% CI: 0.931-0.963) and 0.923 (95% CI: 0.892-0.955) in training and validation cohorts, respectively. For DL-image model, the AUC were 0.928 (95% CI: 0.910-0.945) and 0.864 (95% CI: 0.819-0.910), respectively. The diagnosis performance of the DL-video was superior to that of DL-image, and there was significant difference between the AUC of DL-video and DL-image model in validation cohort (P = .028). The visualization demonstrated certain important ultrasound features that could be recognized by human eyes. CONCLUSIONS: The proposed DL radiomics model based on dynamic ultrasound video can accurately and individually classified thyroid nodules. The constructed DL-video model combining ultrasound video holds good potential for benefiting the management of patients with thyroid nodules.

Different Smoking Statuses on Survival and Emphysema in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

Objective: To investigate the association between different smoking statuses and survival and emphysema in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods: This retrospective study included patients admitted from October 2014 to September 2017. Demographic, clinical, laboratory, imaging, impulse oscillometry, and traditional pulmonary function data were collected. The relationship between smoking and EI was analyzed via binary logistic regression after adjusting for other factors. Survival was analyzed using the Kaplan-Meier method and the log rank test. Results: The patients with AECOPD (357 cases) were identified (and stratified into three groups: never smoked (NS; n=83), former smokers (FS, n=118), and current smokers (CS; n=156). Compared with CS, NS were older and predominantly female. No differences were observed in respiratory symptoms and acute exacerbation between CS and NS. NS had higher resistance and reaction in the central and peripheral airways, while CS exhibited more severe diffuse dysfunction. CS demonstrated more severe and extensive emphysema. Smoking was an independent risk factor for emphysema after adjusting for age, forced expiratory volume in the first second over predicted value, BMI, leukocyte count, and carbon monoxide transfer coefficient. No difference in 5-year survival rates between NS and CS was established. Conclusion: CS has the worst pulmonary function, suggesting a more important destruction of the lung parenchyma, while AECOPD without smoking risk factors mostly affects the airways. Impulse oscillometry can be used for imaging airway-dominant AECOPD. There was no difference in the 5-year survival rate.

Diagnosis of Active Pulmonary Tuberculosis and Community Acquired Pneumonia Using Convolution Neural Network Based on Transfer Learning.

RATIONALE AND OBJECTIVES: A convolutional neural network (CNN) model for the diagnosis of active pulmonary tuberculosis (APTB) and community-acquired pneumonia (CAP) using chest radiographs (CRs) was constructed and verified based on transfer learning. MATERIALS AND METHODS: CRs of 1247 APTB cases, 1488 CAP cases and 1247 normal cases were collected. All CRs were randomly divided into training set (1992 cases), validation set (1194 cases) and test set (796 cases) by stratified sampling in 5:3:2 radio. After normalization of CRs, the convolution base of pre-trained CNN (VGG16) model on ImageNet dataset was used to extract features, and the grid search was used to determine the optimal classifier module, which was added to the convolution base for transfer learning. After the training, the model with the highest accuracy of the validation set was selected as the optimal model to verify in the test set and calculate the accuracy of the model. RESULTS: The accuracy of validation set in the 63rd epochs was the highest, which was 0.9430, and the corresponding Categorical crossentropy was 0.1742. The accuracy of the training set was 0.9428, and the Categorical crossentropy was 0.1545. When the optimal model was applied to the test set, the accuracy was 0.9447, and the Categorical crossentropy was 0.1929. CONCLUSION: The transfer learning-based CNN model has good classification performance in the diagnosis of APTB, CAP and normal patients using CRs.

Artificial intelligence X-ray measurement technology of anatomical parameters related to lumbosacral stability.

PURPOSE: To develop a deep learning-based model for measuring automatic lumbosacral anatomical parameters from lateral lumbar radiographs and compare its performance to that of attending-level radiologists. METHODS: A total of 1791 lateral lumbar radiographs were collected through the PACS system and used to develop the deep learning-based model. Landmarks for the four used parameters, including the lumbosacral lordosis angle (LSLA), lumbosacral angle (LSA), sacral horizontal angle (SHA), and sacral inclination angle (SIA), were identified and automatically labeled by the model. At the same time, the measurement results were obtained through landmarks on the test set compared to manual measurements as the reference standard. Statistical analyses of the Percentage of Correct Key Points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots were performed to evaluate the performance of the model. RESULTS: The mean differences between the reference standard and the model for LSLA, LSA, SHA, and SIA, were 0.39°, 0.09°, 0.13°, and 0.12°, respectively. A strong correlation and consistency between the four parameters were found between the model and reference standard (ICC = 0.92-0.98, r = 0.92-0.97, MAE = 1.35-1.84, RMSE = 1.82-2.51), while with statistically significant difference for LSLA (p = 0.02). CONCLUSIONS: The presented model revealed clinically equivalent measurements in terms of accuracy, while superior measurements were obtained in terms of cost-effectiveness, reliability, and reproducibility. The model may help clinicians improve their understanding and evaluation of lumbar diseases and LBP from a quantitative perspective in practical work. (ChiCTR2100048250).

Ru-incorporated Co3O4 nanoparticles from self-sacrificial ZIF-67 template as efficient bifunctional electrocatalysts for rechargeable metal-air battery.

Ru-incorporated Co3O4 nanoparticles have been synthesized from self-sacrificial ZIF-67 template and utilized as efficient electrocatalysts towards oxygen reduction and evolution reactions (ORR and OER). Amongst, Ru@Co3O4-1.0 exhibited the optimum electrocatalytic behavior with an ultra-low potential gap (0.84 V) between the OER potential (1.61 V at 10 mA cm-2) and ORR half-wave potential (0.77 V). The zinc-air battery using Ru@Co3O4-1.0 as a cathode presented high specific capacity (788.1 mAh g-1) and power density (101.2 mW cm-2). Meanwhile, this battery possessed relatively lower voltage gap and higher cycling stability compared with the commercial Pt/C-based one. Ruthenium incorporation induced remarkable lattice expansion of Co3O4 and engineered more oxygen vacancies, promoting the lattice oxygen mobility from the subsurface/bulk phase onto surface. All these properties were recognized to be the crucial parameters for electrocatalytic activity improvement. This work provided a facile approach to design highly active metal oxide with broad potentiality for rechargeable metal-air batteries.

Potential Value of Expiratory CT in Quantitative Assessment of Pulmonary Vessels in COPD.

Objective: To investigate the associations between intrapulmonary vascular volume (IPVV) depicted on inspiratory and expiratory CT scans and disease severity in COPD patients, and to determine which CT parameters can be used to predict IPVV. Methods: We retrospectively collected 89 CT examinations acquired on COPD patients from an available database. All subjects underwent both inspiratory and expiratory CT scans. We quantified the IPVV, airway wall thickness (WT), the percentage of the airway wall area (WA%), and the extent of emphysema (LAA%-950) using an available pulmonary image analysis tool. The underlying relationship between IPVV and COPD severity, which was defined as mild COPD (GOLD stage I and II) and severe COPD (GOLD stage III and IV), was analyzed using the Student's t-test (or Mann-Whitney U-test). The correlations of IPVV with pulmonary function tests (PFTs), LAA%-950, and airway parameters for the third to sixth generation bronchus were analyzed using the Pearson or Spearman's rank correlation coefficients and multiple stepwise regression. Results: In the subgroup with only inspiratory examinations, the correlation coefficients between IPVV and PFT measures were -0.215 ~ -0.292 (p < 0.05), the correlation coefficients between IPVV and WT3-6 were 0.233 ~ 0.557 (p < 0.05), and the correlation coefficient between IPVV and LAA%-950 were 0.238 ~ 0.409 (p < 0.05). In the subgroup with only expiratory scan, the correlation coefficients between IPVV and PFT measures were -0.238 ~ -0.360 (p < 0.05), the correlation coefficients between IPVV and WT3-6 were 0.260 ~ 0.566 (p < 0.05), and the correlation coefficient between IPVV and LAA%-950 were 0.241 ~ 0.362 (p < 0.05). The multiple stepwise regression analyses demonstrated that WT were independently associated with IPVV (P < 0.05). Conclusion: The expiratory CT scans can provide a more accurate assessment of COPD than the inspiratory CT scans, and the airway wall thickness maybe an independent predictor of pulmonary vascular alteration in patients with COPD.

Role of the Emphysema Index Combined with the Chronic Obstructive Pulmonary Disease Assessment Test Score in the Evaluation of Chronic Obstructive Pulmonary Disease.

Background: This study aimed to evaluate the efficacy of the emphysema index (EI) in distinguishing chronic bronchitis (CB) from chronic obstructive pulmonary disease (COPD) and its role, combined with the COPD Assessment Test (CAT) score, in the evaluation of COPD. Methods: A total of 92 patients with CB and 277 patients with COPD were enrolled in this study. Receiver operating characteristic (ROC) curves were analyzed to evaluate whether the EI can preliminarily distinguish chronic bronchitis from COPD. Considering the heterogeneity of COPD, there might be missed diagnosis of some patients with bronchitis type when differentiating COPD patients only by EI. Therefore, patients with COPD were classified according to the CAT score and EI into four groups: Group 1 (EI < 16%, CAT < 10), Group 2 (EI < 16%, CAT ≥ 10), Group 3 (EI ≥ 16%, CAT < 10), and Group 4 (EI ≥ 16%, CAT ≥ 10). The records of pulmonary function and quantitative computed tomography findings were retrospectively analyzed. Results: ROC curve analysis showed that EI = 16.2% was the cutoff value for distinguishing COPD from CB. Groups 1 and 2 exhibited significantly higher maximal voluntary ventilation (MVV) percent predicted (pred), forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC), maximal midexpiratory flow of 25-75% pred, carbon monoxide-diffusing capacity (DLCO)/alveolar ventilation (VA), FEV1 % pred (p ≤ 0.013), and maximal expiratory flow 50% pred (all p < 0.05) than Group 4. FEV1/FVC and DLCO/VA were significantly lower in Group 3 than in Group 2 (p=0.002 and p < 0.001, respectively). The residual volume/total lung capacity was higher in Group 3 than in Groups 1 and 2 (p < 0.05). Conclusions: The combination of EI and CAT was effective in the evaluation of COPD.

Assessment of radiation doses and image quality of multiple low-dose CT exams in COVID-19 clinical management.

Purpose: The Corona Virus Disease 2019 (COVID-19) was first reported in December 2019 from an outbreak of unexplained pneumonia in Wuhan (Hubei, China) that subsequently spread rapidly around the world. Because of the public health emergency, chest CT has been widely used for sensitive detection and diagnosis, monitoring the changes of lesions and also for treatment evaluation. The purpose of this study was to investigate radiation dose and image quality of chest CT scans received by COVID-19 patients and to evaluate the oncogenic risk of multiple chest CT examinations. Methods: A retrospective review of 33 patients with RT-PCR confirmed COVID-19 infection was performed from January 31, 2020 to February 19, 2020. The date of each CT exam and respective radiation dose for each exam was recorded for all patients. Multiple pulmonary CT scans were obtained during diagnosis and treatment procedure. Scan frequency, total scan times, radiation dose, and image quality were determined. Results: Thirty-three patients (15 males and 18 females, age 21-82 years) with confirmed COVID-19 pneumonia underwent a total of 143 chest CT scans. The number of CT scans per patient was 4 ± 1, with a range of 2-6. The time interval between two consecutive chest CT scans was 3 ± 1 days. The average effective dose from a single chest CT scan was 1.21 ± 0.10 mSv, with a range of 1.02-1.44 mSv. The average cumulative effective dose per patient was 5.25 ± 1.52 mSv, with a range of 2.24-7.48 mSv. The maximum cumulative effective dose was 7.48 mSv for six CT examinations during COVID-19 treatment. Based on subjective image quality analysis, the visual scoring of CT findings was 11.23 ± 1.35 points out of 15 points. Conclusions: The frequency, total number and image quality of chest CT scans should be reviewed carefully to guarantee minimally required CT scans during the COVID-19 management.

Sex-related Differences in Airway Dimensions: A Study Based on Quantitative Computed Tomography among Chinese Population.

ABSTRACT: Sex-dependent radiation injury may be related to the differences in physiological characteristics between the sexes. This study aimed to better understand variations in airway dimensions among male and female Chinese non-smokers. This study included 970 adults and 45 children who underwent chest CT. All participants were non-smokers, without current or former chronic pulmonary disease, and all underwent CT examination. The CT images were quantitatively assessed, providing airway dimensions. The differences in inner diameter, wall thickness, wall area (WA), and WA% for each airway were compared between male and female patients. Sex is an important influencing factor in airway morphological parameters. These parameters are different between men and women: men have a larger airway diameter (P < 0.05) and smaller wall area (WA%, P < 0.05) compared with women. Younger women (<35 years) have a greater diameter and smaller WA% compared with older women (P < 0.05). Sex-related differences in airway morphology were not observed in pediatric participants. Significant differences were found in quantitative CT measures of WA% and an internal diameter among non-smokers of varying sex. The differences found in this study might explain, in part, sex-dependency of radiation injury and a possible radiological protection scheme.

Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint.

In transthoracic echocardiographic (TTE) examination, it is essential to identify the cardiac views accurately. Computer-aided recognition is expected to improve the accuracy of cardiac views of the TTE examination, particularly when obtained by non-trained providers. A new method for automatic recognition of cardiac views is proposed consisting of three processes. First, a spatial transform network is performed to learn cardiac shape changes during a cardiac cycle, which reduces intra-class variability. Second, a channel attention mechanism is introduced to adaptively recalibrate channel-wise feature responses. Finally, the structured signals by the similarities among cardiac views are transformed into the graph-based image embedding, which acts as unsupervised regularization constraints to improve the generalization accuracy. The proposed method is trained and tested in 171792 cardiac images from 584 subjects. The overall accuracy of the proposed method on cardiac image classification is 99.10%, and the mean AUC is 99.36%, better than known methods. Moreover, the overall accuracy is 97.73%, and the mean AUC is 98.59% on an independent test set with 37,883 images from 100 subjects. The proposed automated recognition model achieved comparable accuracy with true cardiac views, and thus can be applied clinically to help find standard cardiac views.

Computer-aided quantitative MSCT measurements may be useful for congenital lung malformations surgical approach selection.

PURPOSE: To examine the association between the MSCT quantitative measurements of congenital lung malformations (CLM) and the selection of surgical approaches (lobectomy vs. lung-sparing surgery). METHODS: This retrospective study evaluated CLM surgical cases at our institution from 2016 to 2018. MSCT quantitative measurements were generated by a semi-automated approach: the volume of the lesion (Vlesion), the volume of the lesion-involved lobe (Vlobe), the volume of the lesion-involved lung (Vlung) and the volume of the total lung (Vtotal lung). The proportions of Vlesion to Vlobe (Plesion/lobe), Vlesion to Vlung (Plesion/lung), and Vlesion to V total lung (Plesion/total lung) were calculated. We used Logistics regression to examine whether quantitative measurements were associated with the selection of surgical approaches. RESULTS: 151 patients were included (median age at surgery 6 months). 82 patients underwent lung-sparing surgery, and 69 patients underwent lobectomy. Vlesion (OR 1.51, 95% CI 1.09-2.07), Plesion/lobe (OR 1.78, 95% CI 1.16-2.72), Plesion/lung (OR 1.63, 95% CI 1.13-2.35), and Plesion/total lung (OR 1.58, 95% CI 1.12-2.22) were positively associated with the selection of lobectomy. CONCLUSION: The application of quantified MSCT analysis may provide insight into the quantitative characteristics of CLM, which could be potentially useful for surgical approach selection.

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy.

BACKGROUND: The successful recognition of benign and malignant breast nodules using ultrasound images is based mainly on supervised learning that requires a large number of labeled images. However, because high-quality labeling is expensive and time-consuming, we hypothesized that semi-supervised learning could provide a low-cost and powerful alternative approach. This study aimed to develop an accurate semi-supervised recognition method and compared its performance with supervised methods and sonographers. METHODS: The faster region-based convolutional neural network was used for nodule detection from ultrasound images. A semi-supervised classifier based on the mean teacher model was proposed to recognize benign and malignant nodule images. The general performance of the proposed method on two datasets (8,966 nodules) was reported. RESULTS: The detection accuracy was 0.88±0.03 and 0.86±0.02, respectively, on two testing sets (1,350 and 2,220 nodules). When 800 labeled training nodules were available, the proposed semi-supervised model plus 4,396 unlabeled nodules performed better than the supervised learning model (area under the curve (AUC): 0.934±0.026 vs. 0.83±0.050; 0.916±0.022 vs. 0.815±0.049). The performance of the semi-supervised model trained on 800 labeled and 4,396 unlabeled nodules was close to that of the supervised learning model trained on a massive number of labeled nodules (n=5,196) (AUC: 0.934±0.026 vs. 0.952±0.027; 0.916±0.022 vs. 0.918±0.017). Moreover, the semi-supervised model was better than the average accuracy of five human sonographers (AUC: 0.922 vs. 0.889). CONCLUSIONS: The semi-supervised model can achieve excellent performance for nodule recognition and be useful for medical sciences. The method reduced the number of labeled images required for training, thus significantly alleviating the difficulty in data preparation of medical artificial intelligence.

Discriminating Small-Sized (2 cm or Less), Noncalcified, Solitary Pulmonary Tuberculoma and Solid Lung Adenocarcinoma in Tuberculosis-Endemic Areas.

BACKGROUND: Pulmonary tuberculoma can mimic lung malignancy and thereby pose a diagnostic dilemma to clinicians. The purpose of this study was to establish an accurate, convenient, and clinically practical model for distinguishing small-sized, noncalcified, solitary pulmonary tuberculoma from solid lung adenocarcinoma. METHODS: Thirty-one patients with noncalcified, solitary tuberculoma and 30 patients with solid adenocarcinoma were enrolled. Clinical characteristics and CT morphological features of lesions were compared between the two groups. Multivariate logistic regression analyses were applied to identify independent predictors of pulmonary tuberculoma and lung adenocarcinoma. Receiver operating characteristic (ROC) analysis was performed to investigate the discriminating efficacy. RESULTS: The mean age of patients with tuberculoma and adenocarcinoma was 46.8 ± 12.3 years (range, 28-64) and 61.1 ± 9.9 years (range, 41-77), respectively. No significant differences were observed concerning smoking history and smoking index, underlying disease, or tumor markers between the two groups. Univariate and multivariate analyses showed age and lobulation combined with pleural indentation demonstrated excellent discrimination. The sensitivity, specificity, accuracy, and the area under the ROC curve were 87.1%, 93.3%, 90.2%, and 0.956 (95% confidence interval (CI), 0.901-1.000), respectively. CONCLUSION: The combination of clinical characteristics and CT morphological features can be used to distinguish noncalcified, solitary tuberculoma from solid adenocarcinoma with high diagnostic performance and has a clinical application value.