Pulmonary CT Images Research Articles

An end-to-end multi-scale airway segmentation framework based on pulmonary CT image

Objective. Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D + 3D framework for multi-scale airway tree segmentation. Approach. In stage 1, we use a 2D full airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale atros spatial pyramid and Atros Residual Skip connection modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 as a priori information. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added false positive losses and false negative losses to improve the segmentation performance of airway branches within the lungs. Main results. We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest dice similarity coefficient (DSC) of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other state-of-the-art methods to increase detected tree length rate by up to 46.33% and detected tree branch rate by up to 42.97%. Significance. The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales.

A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics

Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. For patients with advanced NSCLC that do not have oncogene addiction, the preferred treatment approach is a combination of immunotherapy and chemotherapy. However, the progression-free survival (PFS) typically ranges only from about 6 to 8 months, accompanied by certain adverse events. In order to carry out individualized treatment more effectively, it is urgent to accurately screen patients with PFS for more than 12 months under this treatment regimen. Therefore, this study undertook a retrospective collection of pulmonary CT images from 60 patients diagnosed with NSCLC treated at the First Affiliated Hospital of Wenzhou Medical University. It developed a machine learning model, designated as bSGSRIME-SVM, which integrates the rime optimization algorithm with self-adaptive Gaussian kernel probability search (SGSRIME) and support vector machine (SVM) classifier. Specifically, the model initiates its process by employing the SGSRIME algorithm to identify pivotal image features. Subsequently, it utilizes an SVM classifier to assess these features, aiming to enhance the model's predictive accuracy. Initially, the superior optimization capability and robustness of SGSRIME in IEEE CEC 2017 benchmark functions were validated. Subsequently, employing color moments and gray-level co-occurrence matrix methods, image features were extracted from images of 60 NSCLC patients undergoing immunotherapy combined with chemotherapy. The developed model was then utilized for analysis. The results indicate a significant advantage of the model in predicting the efficacy of immunotherapy combined with chemotherapy for NSCLC, with an accuracy of 92.381% and a specificity of 96.667%. This lays the foundation for more accurate PFS predictions and personalized treatment plans.

Idiopathic dendriform pulmonary ossification as the phenotype of interstitial lung abnormalities: CT–pathologic correlation and prevalence

Background and purposeIdiopathic dendriform pulmonary ossification (DPO) is mostly asymptomatic, and detected incidentally in lung CT. There have been no reports on the precise CT–pathologic correlation and the prevalence of idiopathic DPO. This study aimed to clarify the histological background and prevalence of idiopathic DPO.Materials and methodsSixteen patients with histologically confirmed idiopathic DPO (12 men and 4 women; mean age, 38.8 years; range 22–56 years) were identified in a nationwide epidemiological survey. Local HRCT findings of pre-biopsy examinations, such as branching, round, linear structures with or without high attenuation were compared side by side with histological findings. The attenuation of branching, round, and linear structures was classified into three-point levels on bone window images (width, 2500 HU; level, 500 HU). Furthermore, we collected continuous pulmonary CT images of 8111 cases for checking up metastasis from extrathoracic malignancy at a single institution, and evaluated the prevalence of interstitial lung abnormalities (ILAs) and DPO.ResultsIn all 16 cases, branching (n = 15, 93%), round (n = 5, 31%), or linear (n = 5, 31%) structures were identified, histologically corresponding to dendriform ossification and cicatricial organizing pneumonia (OP)/fibrosis. Histologically, ossification was confirmed in all the 16 patients. However, in two cases, a highly attenuated structure could not be detected on the pre-biopsy CT of the same area. Regarding the prevalence of idiopathic DPO, 283 (3.5%) of 8111 patients had ILAs, of which a total of 26 (0.3% of all cases, 9.2% of ILAs cases) had DPO.ConclusionIdiopathic DPO showed linear or branching structures with or without high attenuation on CT, corresponded to ossification, cicatricial OP/fibrosis. DPO was seen in 9.2% of ILAs cases. Idiopathic DPO is one of pathologic phenotypes of ILAs.

Characteristics and determinants of pulmonary long COVID.

BACKGROUNDPersistent cough and dyspnea are prominent features of postacute sequelae of SARS-CoV-2 (also termed "long COVID"); however, physiologic measures and clinical features associated with these pulmonary symptoms remain poorly defined. Using longitudinal pulmonary function testing (PFT) and CT imaging, this study aimed to identify the characteristics and determinants of pulmonary long COVID.METHODSThis single-center retrospective study included 1,097 patients with clinically defined long COVID characterized by persistent pulmonary symptoms (dyspnea, cough, and chest discomfort) lasting for 1 or more months after resolution of primary COVID infection.RESULTSAfter exclusion, a total of 929 patients with post-COVID pulmonary symptoms and PFTs were stratified as diffusion impairment and pulmonary restriction, as measured by percentage predicted diffusion capacity for carbon monoxide (DLCO) and total lung capacity (TLC). Longitudinal evaluation revealed diffusion impairment (DLCO ≤ 80%) and pulmonary restriction (TLC ≤ 80%) in 51% of the cohort overall (n = 479). In multivariable modeling regression analysis, invasive mechanical ventilation during primary infection conferred the greatest increased odds of developing pulmonary long COVID with diffusion impairment and restriction (adjusted odds ratio [aOR] = 9.89, 95% CI 3.62-26.9]). Finally, a subanalysis of CT imaging identified radiographic evidence of fibrosis in this patient population.CONCLUSIONLongitudinal PFTs revealed persistent diffusion-impaired restriction as a key feature of pulmonary long COVID. These results emphasize the importance of incorporating PFTs into routine clinical practice for evaluation of long COVID patients with prolonged pulmonary symptoms. Subsequent clinical trials should leverage combined symptomatic and quantitative PFT measurements for more targeted enrollment of pulmonary long COVID patients.FUNDINGNational Institute of Allergy and Infectious Diseases (AI156898, K08AI129705), National Heart, Lung, and Blood Institute (HL153113, OTA21-015E, HL149944), and the COVID-19 Urgent Research Response Fund at the University of Alabama at Birmingham.

Development and Validation of an Algorithm Model for Predicting Heat Sink Effects during Pulmonary Thermal Ablation

The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols. The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time. To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and vascular structure and measure the distance between the nodule and vascular. First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the difference between the two results. In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink effect prediction, the positive rate of the algorithm group was 28.3%, and that of the expert group was 32.1%, with no significant difference between the two groups (p=0.687). The algorithm model developed in this study shows good performance in predicting the heat sink effect during pulmonary thermal ablation. It can improve the speed and accuracy of nodule and vessel segmentation, save ablation planning time, reduce the interference of human factors, and provide more reference information for surgeons to make ablation plans to improve the ablation effect.

Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.

SARS-CoV-2 is a novel virus that has been affecting the global population by spreading rapidly and causing severe complications, which require prompt and elaborate emergency treatment. Automatic tools to diagnose COVID-19 could potentially be an important and useful aid. Radiologists and clinicians could potentially rely on interpretable AI technologies to address the diagnosis and monitoring of COVID-19 patients. This paper aims to provide a comprehensive analysis of the state-of-the-art deep learning techniques for COVID-19 classification. The previous studies are methodically evaluated, and a summary of the proposed convolutional neural network (CNN)-based classification approaches is presented. The reviewed papers have presented a variety of CNN models and architectures that were developed to provide an accurate and quick automatic tool to diagnose the COVID-19 virus based on presented CT scan or X-ray images. In this systematic review, we focused on the critical components of the deep learning approach, such as network architecture, model complexity, parameter optimization, explainability, and dataset/code availability. The literature search yielded a large number of studies over the past period of the virus spread, and we summarized their past efforts. State-of-the-art CNN architectures, with their strengths and weaknesses, are discussed with respect to diverse technical and clinical evaluation metrics to safely implement current AI studies in medical practice.

Fractal Analysis in Pulmonary CT Images of COVID-19-Infected Patients

In this paper, we propose to quantitatively compare the loss of human lung health under the influence of the illness with COVID-19, based on the fractal-analysis interpretation of the chest-pulmonary CT pictures, in the case of small datasets, which are usually encountered in medical applications. The fractal analysis characteristics, such as fractal dimension and lacunarity measured values, have been utilized as an effective advisor to interpretation of pulmonary CT picture texture.

The Outcome of COVID-19 Infection in Patients With Gastrointestinal Diseases: An Experience at a Tertiary Center.

Observing the impact of the coronavirus disease 2019 (COVID-19) pandemic on digestive diseases in hospitalized patients at the Department of Gastroenterology-Hepatology in "Mother Teresa" University Hospital Center (UHC),Tirana. This retrospective study was carried out from June 2020 to December 2021 involving 41 cases of patients >18 years who were positive for COVID-19 infection detected by RT-PCR (Reverse Transcription-Polymerase Chain Reaction) assays of nasopharyngeal swab specimens. The severity of COVID-19 infection was evaluated by hematological/biochemical parameters, blood oxygenation/need for oxygen, radiological data on pulmonary CT imaging. Out of 2527 hospitalized cases, 1.6% (41) were positive for the infection. The average age was 60.05 +/- 15.008 years. The group of age with more patients (48.8%) was 41-60 years. Infected males were higher than females (p<0.001). Out of the total, 21% were vaccinated at the diagnosis. Most patients came from urban areas, more than a half from the capital. Frequency of the digestive diseases was: cirrhosis 31.7%, pancreatitis 21.9%, alcoholic liver disease 21.9%, gastrointestinal hemorrhage 19.5%, digestive cancer 14.6%, biliary diseases 7.3%, inflammatory bowel disease (IBD) 2.4%, other digestive diseases 4.8%. Fever (90%) and fatigue (78.04%) were the dominant clinical signs. Biochemical and hematological parameters showed elevation of average value of aspartate amino transferase (AST), alanine transaminase (ALT) (AST>ALT, p<0.001), and bilirubin in all the patients. Higher levels of creatinine andsignificantly predictive value of systemic inflammation indices NLR (neutrophil to lymphocyte ratio) and MLR (monocyte to lymphocyte ratio) were found in the fatality cases. Patients with cirrhosis had more severe form of COVID-19, lower blood oxygenationand needed treatment by O2-therapy (p<0.046). Death rate was 12%. A strong correlation was found between the need for O2-therapy and deaths (p<0.001) and between characteristic findings for COVID-19 in pulmonary CT imaging and low blood oxygenation (p<0.003). Comorbiditywith chronic diseases, such as liver cirrhosis, has an important impact on the severity and mortality of the patients with COVID-19 infection. Inflammatory indices, such as NLR (neutrophil to lymphocyte ratio) and MLR (monocyte to lymphocyte ratio), are useful tools in predicting the evolution toward severe forms of the disease.

An Analysis of Two Aged Patients with Pulmonary Tuberculosis

What methods would you choose apart from purified protein derivative (PPD) skin test and culture for acid-fasting bacilli (AFB) to make a clinical diagnosis of pulmonary tuberculosis (TB) in an aged patient with possible TB? These are two cases of pulmonary TB, that occurred with persistent low-grade fever, fatigue and anorexia due to a mild toxemia of tuberculosis. A final diagnosis of pulmonary tuberculosis was established on the basis of mild toxemia(low-grade fever), debilitation and characteristic pulmonary CT imaging, free of PPD and interferon-gamma release assay (T-spot). The authors realized that the dosage of anti-tuberculosis drugs should be carefully controlled and the improvement of their overall nutritional status to gain better efficacy is much more needed.

Value of transbronchial needle aspiration combined with a rapid on-site evaluation of cytology in the diagnosis of pulmonary lesions.

BackgroundThe diagnostic value of rapid on-site evaluation (ROSE) of cytology during endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) remains controversial. The purpose of this study was to validate the value of ROSE during the EUBS-TBNA procedure in the diagnosis of pulmonary lesions (PLs).MethodsEnrolled in this study were 260 patients with nodules, masses, cavities, or inflammatory lesions on pulmonary CT images. They were assigned to undergo EBUS-TBNA with ROSE (n = 134) or without ROSE (n = 126). The diagnostic results of ROSE during EBUS-TBNA and the final pathologic reports were analyzed and compared by utilizing SPSS21.0 software to evaluate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, we further explored whether the ROSE method during EBUS-TBNA would improve the diagnostic yield and reduce the incidence of complications.ResultsThe overall diagnostic yield of EBUS-TBNA for malignant diseases in the ROSE and the non-ROSE group were 29.9 and 11.1%, respectively. The sensitivity, specificity, PPV and NPV of the ROSE method during EBUS-TBNA were 97.4, 96.9, 92.5, and 98.90%, respectively. The result of the chi-square test effectively proved that ROSE operation during EBUS-TBNA contributes to the diagnosis of malignancy compared with the non-ROSE group (χ2 = 13.858, P < 0.001). The number of punctures in the ROSE group was significantly lower than that in the non-ROSE group (P < 0.001).ConclusionROSE examination during EBUS-TBNA could effectively improve the diagnostic yield of malignant diseases compared with the non-ROSE group and reduce the number of intraoperative punctures, which is a clinical application worth popularizing.

Virtual autopsy in SARS-CoV-2 breakthrough infection: a case report

It is well documented that COVID-19 vaccines are effective tools for limiting the pandemic. Unfortunately, as is true for all vaccines, SARS-CoV-2 infection in vaccinated individuals is still possible.We present an autopsy case of SARS-CoV-2 infection after vaccination (“breakthrough infection”) in an elderly man with several comorbidities where post-mortem CT scan was performed. The death was histologically attributed to cardio-respiratory arrest due to ischemic heart failure related to superinfected COVID-19 pneumonia and pre-existing comorbidities. For the first time in the literature, PMCT imaging related to a fatal, autopsy case of breakthrough SARS-CoV-2 infection is reported. PMCT of the lungs, in accordance with histopathological results, showed few signs of COVID-19 pneumonia, large area of consolidation in the right lower lobe, interpreted as bronco-pneumonic focus, and hypostasis.These findings were well-correlated with the previously reported literature about both PMCT and clinical CT imaging of the lungs in non-vaccinated individuals with early COVID-19 pneumonia and about pulmonary clinical CT imaging in COVID-19 pneumonia in breakthrough SARS-COV-2 infections.Further studies are needed to cover the whole spectrum of PMCT lung imaging in fatal breakthrough SARS-CoV-2 infection, however, this case represent a first step for exploring this difficult challenge during SARS-CoV-2 pandemic using virtual autopsy.

A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks.

Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and malignant of pulmonary nodules. The spiculation of pulmonary nodules is one of the main signs. Pulmonary nodules are small in volume, so they are difficult to extract accurately. Moreover, the number of spiculation samples is limited, so it is difficult to build a stable network structure. Thus, a novel pulmonary nodule spiculation recognition algorithm is proposed. MCA (morphological component analysis) model is built to segment pulmonary nodules in accordance with the composition of pulmonary CT images. Subsequently, the maximum density projection mechanism is introduced to characterize the boundary features of pulmonary nodules to the maximum extent. Inspired by time series dynamic programming, this paper proposes DTW (dynamic time warping) distance to measure data similarity. Lastly, a semisupervised generative adversarial network is built to solve the problem of insufficient positive samples, and it is capable of recognizing pulmonary nodule spiculation. As revealed by the experimental result, the proposed algorithm exhibited strong robustness.

Spectrum of Typical and Atypical Pulmonary CT Imaging Findings of COVID-19 Infection: A Retrospective Study.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. It was declared a public health emergency by WHO in January 2020. The definitive diagnostic test for COVID-19 is a real time polymerase chain reaction test (RT-PCR) which is highly specific, but sensitivity is variable. COVID-19 typically presents clinically with respiratory and systemic symptoms. The majority of the infected patients are asymptomatic during the course of the disease, which we have not included in our study. Imaging findings on high-resolution computed tomography (HRCT) chest are important to diagnose the disease in early stage, for treatment planning and to predict the patient prognosis. The purpose of our study was to characterize typical and atypical pulmonary and extra-pulmonary HRCT findings in patients with COVID-19 infection and to help in the management of patients. In this retrospective study, we have included 70 patients who had undergone HRCT examination of the chest in the Radiodiagnosis Department, Maharishi Markandeshwar Medical College, Kumarhatti, Solan, Himachal Pradesh, India. The HRCT findings of the chest of these patients in the study will be evaluated and data will be statistically analyzed.

THE UTILITY OF RAPID ON-SITE CYTOLOGIC EVALUATION FOR DIAGNOSING PULMONARY NODULES: A RETROSPECTIVE STUDY

TOPIC: Lung Cancer TYPE: Original Investigations PURPOSE: Background: The usefulness of rapid on-site cytologic evaluation (ROSE) as a means of on-site assessment materials for improving diagnostic rates during endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) remains controversial. Objectives: The purpose of this study was to investigate the effect of using ROSE during EBUS-TBNA on the diagnostic yield of pulmonary nodules and postoperative complications. METHODS: Methods: 260 patients with pulmonary CT imaging showing nodules, masses, cavities or inflammatory lesions were enrolled in this study and randomized to undergo EBUS-TBNA with or without ROSE. We compared and analyzed the effect of ROSE operation during EBUS-TBNA on the incidence of postoperative complications such as pneumothorax and hemoptysis in both groups, and evaluated the sensitivity, specificity, positive predictive value(PPV), negative predictive value(NPV) and agreement rate of the ROSE. RESULTS: Results: The number of puncture in ROSE group was significantly reduced than that in non-ROSE group (2.07±0.26 vs 3.23±0.42; p<0.001). 2 pneumothorax cases were found in non-ROSE group, while there was no pneumothorax case in ROSE group. No significant statistical difference in hemoptysis between two groups (P=0.402). The sensitivity, specificity, PPV, NPV and agreement rate of ROSE during EBUS-TBNA were 97.4%, 96.9%, 92.5%, 98.9% and 97.0% respectively. The area under the ROC curve (AUC) of ROSE diagnosis of pulmonary nodules was 0.971, which was significant statistical differences(P<0.001). CONCLUSIONS: Conclusions: ROSE during EBUS-TBNA is closely associated with puncture numbers and higher diagnostic yield. CLINICAL IMPLICATIONS: The incidence and mortality of lung cancer have been at the level of high risk among all kinds of cancers1. The traditional pathological result is the gold standard for accurate diagnosis of the lesion. The difficulty of sampling and the time consuming of the diagnosis process are the shortcomings of the traditional examination, which may delay the patient's health status and the length of hospitalization, and increase the patient's economic burden. While, ROSE examination during the EBUS-TBNA period can realize the rapid diagnosis of the lesion on the spot, avoiding the defects of traditional pathological diagnosis. EBUS-TBNA, as a minimally invasive procedure, has been widely used in the early diagnosis of lung cancer in recent years due to it provides real-time visualization of lymph nodes and surrounding structures, which can point the needle tip to the target region 2,3,4. ROSE, as an immediate method to explain the pathological properties of puncture objects, has also been tried in EBUS-TBNA procedure. DISCLOSURES: No relevant relationships by changhui wang, source=Web Response no disclosure on file for shuanshuan xie

Dense GAN and multi-layer attention based lesion segmentation method for COVID-19 CT images

As the COVID-19 virus spreads around the world, testing and screening of patients have become a headache for governments. With the accumulation of clinical diagnostic data, the imaging big data features of COVID-19 are gradually clear, and CT imaging diagnosis results become more important. To obtain clear lesion information from the CT images of patients' lungs is helpful for doctors to adopt effective medical methods, and at the same time, is helpful to screen the patients with real infection. Deep learning image segmentation is widely used in the field of medical image segmentation. However, there are some challenges in using deep learning to segment the lung lesions of COVID-19 patients. Since image segmentation requires the labeling of lesion information on a pixel by pixel basis, most professional radiologists need to screen and diagnose patients on the front line, and they do not have enough energy to label a large amount of image data. In this paper, an improved Dense GAN to expand data set is developed, and a multi-layer attention mechanism method, combined with U-Net's COVID-19 pulmonary CT image segmentation, is proposed. The experimental results showed that the segmentation method proposed in this paper improved the segmentation accuracy of COVID-19 pulmonary medical CT image by comparing with other image segmentation methods.

Data That Support Removing the Mysticism for Radiologist's Performance of Cardiac CT and MRI.

Data That Support Removing the Mysticism for Radiologist's Performance of Cardiac CT and MRI.

Analysis of clinical features and pulmonary CT features of coronavirus disease 2019 (COVID-19) patients with diabetes mellitus

The objective of this paper was to investigate the clinical features and pulmonary CT imaging features of COVID-19 patients with diabetes mellitus. From January 16, 2020 to March 28, 2020, among the 568 cases of COVID-19 patients diagnosed in Xiaogan Central Hospital, 64 cases of COVID-19 patients with diabetes were selected as the diabetic group, and 64 cases of COVID-19 patients with age and gender matching without diabetes were selected as the non-diabetic group, and their clinical data and pulmonary CT characteristics were retrospectively analysed. Compared with the non-diabetic group, the proportion of patients in the diabetic group with chronic underlying disease was higher, and they were in more a serious condition at admission. Inflammation index and characteristics of glycolipid metabolism results showed that COVID-19 patients with diabetes mellitus were more likely to have elevated inflammatory markers and hypercoagulability, accompanied by hypoproteinaemia and glucose and lipid metabolism disorders. Treatment and clinic outcome results showed that the time of nucleic acid turning negative in the diabetic group was significantly longer than that in the non-diabetic group. Radiological data showed that COVID-19 combined with diabetes prolonged the time of detoxification in patients. COVID-19 patients with diabetes mellitus and chronic hypertension are associated with increased inflammatory markers and disorders of glucose and lipid metabolism. These patients tend to develop serious diseases, especially the rapid progression of CT lesions in the lungs of patients with a wide range ofinvolvement, and prolonged absorption and detoxification time.

Clinical and imaging features of pediatric COVID-19

BackgroundPediatric COVID-19 is relatively mild and may vary from that in adults. This study was to investigate the epidemic, clinical, and imaging features of pediatric COVID-19 pneumonia for early diagnosis and treatment.MethodsForty-one children infected with COVID-19 were analyzed in the epidemic, clinical and imaging data.ResultsAmong 30 children with mild COVID-19, seven had no symptoms, fifteen had low or mediate fever, and eight presented with cough, nasal congestion, diarrhea, headache, or fatigue. Among eleven children with moderate COVID-19, nine presented with low or mediate fever, accompanied with cough and runny nose, and two had no symptoms. Significantly (P < 0.05) more children had a greater rate of cough in moderate than in mild COVID-19. Thirty children with mild COVID-19 were negative in pulmonary CT imaging, whereas eleven children with moderate COVID-19 had pulmonary lesions, including ground glass opacity in ten (90.9%), patches of high density in six (54.5%), consolidation in three (27.3%), and enlarged bronchovascular bundles in seven (63.6%). The lesions were distributed along the bronchus in five patients (45.5%). The lymph nodes were enlarged in the pulmonary hilum in two patients (18.2%). The lesions were presented in the right upper lobe in two patients (18.1%), right middle lobe in one (9.1%), right lower lobe in six (54.5%), left upper lobe in five (45.5%), and left lower lobe in eight (72.7%).ConclusionsChildren with COVID-19 have mild or moderate clinical and imaging presentations. A better understanding of the clinical and CT imaging helps ascertaining those with negative nucleic acid and reducing misdiagnosis rate for those with atypical and concealed symptoms.

Clinical analysis of ten pregnant women with COVID-19 in Wuhan, China: A retrospective study

BackgroundCOVID-19 is spreading globally. This study aims to evaluate the clinical characteristics and outcomes of pregnant women confirmed with COVID-19 to provide reference for clinical work. MethodsThe clinical features and outcomes of 10 pregnant women confirmed with COVID-19 at Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, a tertiary- care teaching hospital in Hubei province, Wuhan, China from January 23 to February 23, 2020 were retrospectively analyzed. ResultsAll the 10 observed pregnant women including 9 singletons and 1 twin were native people in Wuhan. All of them were diagnosed mild COVID-19, and none of the patients developed severe COVID-19 or died. Among the 10 patients, two patients underwent vaginal delivery, two patients underwent intrapartum cesarean section, and the remaining six patients underwent elective cesarean section. All of 10 patients showed lung abnormalities by pulmonary CT images after delivery. Their eleven newborns were recorded and no neonatal asphyxia was observed. ConclusionsPulmonary CT screening on admission may be necessary to reduce the risk of nosocomial transmission of COVID-19 during the outbreak period. And COVID-19 is not an indication of cesarean section.

Multistage CT features of coronavirus disease 2019.

To determine imaging features of coronavirus disease 2019 (COVID-19) in different stages, and to provide foundations for early diagnosis and treatment. CT image data of 187 COVID-19 patients were analyzed in the period of hospitalization. CT scanning was performed on admission and repeated every 3 days. The improvement time of clinical symptoms and the image changes of follow-up CT were statistically analyzed. All 187 patients' nucleic acid test were positive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The early CT images of lung in 187 cases (100%) showed multiple patchy and ground-glass opacities with fine mesh and consolidation shade, which mainly distributed in pulmonary band or under the pleura. In the progressive stage, the pulmonary lesions in 146 cases (78.1%) were mainly consolidation, accompanied by air bronchogram, thickening of blood vessels, and interstitial changes. Severe pulmonary CT images in 15 cases (8%) showed diffuse lesions in both lungs, displaying consolidation, or "white lung". The CT imaging features in 185 cases (98.9%) at the absorptive period showed that the lesions diminished and fibrogenesis. The imaging features of 6 times of lung CT examination in one case showed continuous progress. The original lesion in one case was obviously absorbed, but new lesions appeared under the pleura of both lungs at the third review of CT scanning. The changes of CT imaging lesions during follow-up were significantly different in different clinical symptoms improvement time (P< 0.01). Images of COVID-19 in various stages have special characteristics. The change of clinical symptoms is synchronous with the change of reexamination CT. Follow-up CT can reflect the trend of clinical changes. Repeat CT examination plays an important role in the early clinical diagnosis and the evaluation for the therapeutic effect on COVID-19 patient.