Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis.

Tuberculosis (TB) remains one of the major infectious diseases in the world with a high incidence rate. Drug-resistant tuberculosis (DR-TB) is a key and difficult challenge in the prevention and treatment of TB. Early, rapid, and accurate diagnosis of DR-TB is essential for selecting appropriate and personalized treatment and is an important means of reducing disease transmission and mortality. In recent years, imaging diagnosis of DR-TB has developed rapidly, but there is a lack of consistent understanding. To this end, the Infectious Disease Imaging Group, Infectious Disease Branch, Chinese Research Hospital Association; Infectious Diseases Group of Chinese Medical Association of Radiology; Digital Health Committee of China Association for the Promotion of Science and Technology Industrialization, and other organizations, formed a group of TB experts across China. The conglomerate then considered the Chinese and international diagnosis and treatment status of DR-TB, China's clinical practice, and evidence-based medicine on the methodological requirements of guidelines and standards. After repeated discussion, the expert consensus of imaging diagnosis of DR-PB was proposed. This consensus includes clinical diagnosis and classification of DR-TB, selection of etiology and imaging examination [mainly X-ray and computed tomography (CT)], imaging manifestations, diagnosis, and differential diagnosis. This expert consensus is expected to improve the understanding of the imaging changes of DR-TB, as a starting point for timely detection of suspected DR-TB patients, and can effectively improve the efficiency of clinical diagnosis and achieve the purpose of early diagnosis and treatment of DR-TB.

Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features.

Objectives: Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia. Methods: In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals. Patients were randomly grouped into a primary cohort and a validation cohort using a seven-to-three ratio, and their clinicoradiologic data on admission were compared. The clinicoradiologic features were optimized by the least absolute shrinkage and selection operator (LASSO) logistic regression analysis to generate a model for differential diagnosis. Receiver operating characteristic (ROC) curves were plotted for assessing the performance of the model in the primary and validation cohorts. Results: The COVID-19 pneumonia mainly presented a peripheral distribution pattern (262/291, 90.0%); in contrast, H1N1 pneumonia most commonly presented a peribronchovascular distribution pattern (52/97, 53.6%). In LASSO logistic regression, peripheral distribution patterns, older age, low-grade fever, and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia, whereas, a peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia, and a higher level of AST were associated with H1N1 pneumonia. For the primary and validation cohorts, the LASSO model containing above eight clinicoradiologic features yielded an area under curve (AUC) of 0.963 and 0.943, with sensitivity of 89.7 and 86.2%, specificity of 89.7 and 89.7%, and accuracy of 89.7 and 87.1%, respectively. Conclusions: Combination of distribution pattern and category of pulmonary opacity on chest CT with clinical features facilitates the differentiation of COVID-19 pneumonia from H1N1 pneumonia.

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter.

PURPOSE: To develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter. MATERIALS AND METHODS: This retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts. RESULTS: Five radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSION: This study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.

A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.

BACKGROUND: The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19. METHODS: One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared. RESULTS: In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOICT) and the percentage of infection (POICT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOICT and clinical features, including age, cluster of differentiation 4 (CD4)+ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOICT, POICT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively). CONCLUSIONS: Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.

Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction.

OBJECTIVE: Computed tomography (CT) provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study was to develop a deep learning (DL)-based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. METHODS: The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each training case. To evaluate the performance of the DL-based segmentation system, three metrics, that is, Dice similarity coefficient, the differences of volume, and percentage of infection (POI), are calculated between automatic and manual segmentations on the validation set. Then, a clinical study on severity prediction is reported based on the quantitative infection assessment. RESULTS: The proposed DL-based segmentation system yielded Dice similarity coefficients of 91.6% ± 10.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases with fully manual delineation that often takes hours, the proposed HIMI training strategy can dramatically reduce the delineation time to 4 min after three iterations of model updating. Besides, the best accuracy of severity prediction was 73.4% ± 1.3% when the mass of infection (MOI) of multiple lung lobes and bronchopulmonary segments were used as features for severity prediction, indicating the potential clinical application of our quantification technique on severity prediction. CONCLUSIONS: A DL-based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID-19 patients. Quantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction.

M 3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging.

To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M 3Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks. The former aims to seek the feature representations from abundant CT slices instead of limited CT volumes, and for the overall pneumonia screening, the latter one could recover the temporal information by feature refinement and aggregation between different slices. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3Lung-Sys also be able to locate the areas of relevant lesions, without any pixel-level annotation. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and 133 CAP patients). The quantitative results with plenty of metrics indicate the superiority of our proposed model on both slice- and patient-level classification tasks. More importantly, the generated lesion location maps make our system interpretable and more valuable to clinicians.

An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing.

BACKGROUND: Nowadays, computer technology is getting popular for clinical aided diagnosis, especially in the direction of medical images. It makes physician diagnosis of lung nodules more efficient by providing them with reliable and accurate segmentation. METHODS: A region growing based semi-automated pulmonary nodule segmentation algorithm (ReGANS) was developed with three improvements: an automatic threshold calculation method, a lesion area pre-projection method, and an optimized region growing method. The algorithm can quickly and accurately segment a whole lung nodule in a set of computed tomography (CT) images based on an initial manual point. RESULTS: The average time taken for ReGANS to segment 1 pulmonary nodule was 0.83s, and the probability rand index (PRI), global consistency error (GCE), and variation of information (VoI) from a comparison between the algorithm and the radiologist's 2 manual results were 0.93, 0.06, and 0.3 for the boundary range (BR), and 0.86, 0.06, 0.3 for the precise range (PR). The number of images covered by one pulmonary nodule in a CT image set was also evaluated to compare the segmentation algorithm with the radiologist's results, with an error rate of 15%. At the same time, the results were verified in multiple data sets to validate the robustness. CONCLUSIONS: Compared with other algorithms, ReGANS can segment the lung nodule image region more quickly and more precisely. The experimental results show that ReGANS can assist medical imaging diagnosis and has good clinical application value. It also provides a faster and more convenient method for pre-data preparation of intelligent algorithms.

HIV-infected patients with opportunistic pulmonary infections misdiagnosed as lung cancers: the clinicoradiologic features and initial application of CT radiomics.

BACKGROUND: To characterize clinicoradiologic and radiomic features for identifying opportunistic pulmonary infections (OPIs) misdiagnosed as lung cancers in patients with human immunodeficiency virus (HIV). METHODS: Twenty-four HIV-infected patients who were misdiagnosed with lung cancers on CT images and had OPIs confirmed by pathological examination or integration of clinical and laboratory findings and 49 HIV-infected patients with lung cancers confirmed pathologically were included. Semiautomated segmentation of the lesion was implemented with an in-house software. The lesion boundary was adjusted manually by radiologists. A total of 99 nonenhanced-CT-based radiomic features were then extracted with PyRadiomics. The clinicoradiologic and radiomic features were compared between the OPI and cancer groups. RESULTS: In the OPI group, 19 patients (79.2%) had tuberculosis (TB) infections, 2 (8.3%) had nontuberculosis mycobacterium (NTM) infections, 2 (8.3%) had cryptococcus infections and 1 (4.2%) had a mixed infection of TB and NTM. There were significant differences in age, proportion of smokers, smoking index, highly active antiretroviral therapy (HAART) duration, CD4+ counts and CD4+/CD8+ ratio between the two groups (P=0.000, 0.012, 0.007, 0.002, 0.000, and 0.000, respectively). In peripheral-type lesions, the presence of pleural indentation was less common, and the presence of satellite lesions was more common in the OPI group (P=0.016 and 0.020, respectively). Four radiomic parameters of central-type lesions were significantly different, including large dependence high gray level emphasis (LDHGLE), skewness, inverse difference normalized (IDN) and kurtosis (P=0.008, 0.017, 0.017, and 0.017, respectively). However, neither CT features of central-type lesions nor radiomic parameters of peripheral-type lesions were significantly different between the two groups. CONCLUSIONS: Clinicoradiologic features together with radiomics may help identify OPIs mimicking lung cancers in HIV-infected patients.

The Association Between Endocan Levels and Subclinical Atherosclerosis in Patients With Type 2 Diabetes Mellitus.

BACKGROUND: Proinflammatory conditions induced by circulating factors in diabetes play a pivotal role in endothelial dysfunction and related vascular complications. Endothelial cell-specific molecule-1 or endocan is a dermatan sulfate proteoglycan secreted primarily by the vascular endothelium. Although endocan has been shown to be a potential biomarker in coronary heart disease, its role in the pathogenesis of atherosclerosis (AS) in diabetes remains unclear. In this study, we investigated the correlation between serum endocan levels and subclinical AS in patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: Patients (n = 69) with T2DM were included. All the patients were stratified based on the absence (n = 42) or presence (n = 27) of subclinical AS. Healthy subjects (n = 28) served as controls. Serum levels of endocan, fasting blood glucose, glycosylated hemoglobin A1, high-sensitivity C-reactive protein and carotid intima-media thickness (cIMT) were measured. RESULTS: Endocan levels were significantly elevated in both the T2DM (0.89 ± 0.28ng/mL) and T2DM with subclinical AS (1.20 ± 0.33ng/mL) groups relative to the control group (0.68 ± 0.24ng/mL) (P < 0.05 for all). Endocan levels were also positively correlated with glycosylated hemoglobin A1, fasting blood glucose and cIMT (r = 0.292, P = 0.004; r = 0.224, P = 0.027 and r = 0.496, P < 0.001, respectively). In addition, endocan levels were independently associated with cIMT (ß = 0.220, t = 5.816, P = 0.000) and were a significant risk factor for T2DM with subclinical AS (odds ratio = 1.98, 95% CI: 1.43-2.73, P < 0.001). CONCLUSIONS: These findings suggest that serum endocan levels may be a useful biomarker for the early diagnosis of subclinical AS in patients with T2DM.

Efficient detection of internal infestation in wheat based on biophotonics.

In the process of grain storage, there are many losses of grain quantity and quality for the sake of insects. As a result, it is necessary to find a rapid and economical method for detecting insects in the grain. The paper innovatively proposes a model of detecting internal infestation in wheat by combining pattern recognition and BioPhoton Analytical Technology (BPAT). In this model, the spontaneous ultraweak photons emitted from normal and insect-contaminated wheat are firstly measured respectively. Then, position, distribution and morphological characteristics can be extracted from the measuring data to construct wheat feature vector. Backpropagation (BP) neural network based on genetic algorithm is employed to take decision on whether wheat kernel has contaminated by insects. The experimental results show that the proposed model can differentiate the normal wheat from the insect-contaminated one at an average accuracy of 95%. The model can also offer a novel thought for detecting internal infestation in the wheat.

[Electrophysiologic study of the biphasic effects of cyclovirobuxine D on arrhythmias].

OBJECTIVE: To explore the possible mechanism of cyclovirobuxine D (CVB-D) in countering and inducing arrhythmia, by way of studying its electro-physiological effect on ventricular papillary muscles of rats in vitro. METHODS: The transmembrane potential of rat's isolated right ventricular papillary muscles were recorded using conventional glass micro-electrode technique. RESULTS: (1) CVB-D in concentration of 13.3-63.3 micromol/L, showed prolonging effect on the action potential repolarization time, mainly the action potential duration 50 (APD50), APD70 and APD90, in dose-dependent manner, in concentration of 33.3-63.3 micromol/L, it could inhibit the resting potential, action potential amplitude (APA) and maximum depolarization velocity (Vmax) in dose-dependent manner. (2) CVB-D also showed time-dependent effect, the effect initiated 10 min after 20 micromol/L was perfused in ventricular muscle, the APD50, APD70 and APD90 were potentiated gradually along with prolongation of action time and reached the peak at 30-40 min, without any potentiation thereafter. (3) CVB-D could markedly prolong the effective refractory period (ERP) of action potential, increase the ratio of ERP/APD. (4) CVB-D in concentration of 33.3 micromol/L could induce frequent, multifocal spontaneous arrhythmia in some cells when the action time was longer than 45 min. CONCLUSION: CVB-D has the action of anti-ventricular arrhythmia, the mechanism is correlated with the prolongation of APD and ERP of ventricular muscle as well as the increase of ERP/APD ratio, while it also has the effect of inducing arrhythmia, the mechanism might be concerned with excessive prolongation of APD and the inhibition on RP, APA and Vmax.