scholarly journals Outcome of Pulmonary Spherical Ground-glass Opacities on Ct in Patients With Coronavirus Disease 2019 (Covid-19): A Retrospective Analysis

2020 ◽  
Author(s):  
Wangjia Li ◽  
Liangbo Hu ◽  
Junhao Huang ◽  
Fajin Lv ◽  
Binjie Fu ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Ct Scan ◽  
Retrospective Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Ground Glass ◽  
Direct Absorption ◽  
Simultaneous Decrease ◽  
Chest Ct Scan

Abstract Background: Pulmonary spherical ground-glass opacities (GGOs) are commonly detected on initial chest CT scan in patients with coronavirus disease 2019 (COVID-19).We aimed to investigate the evolution of spherical GGOs to better understand their clinical significance.Materials and Methods:A retrospective study of 33 consecutive patients with confirmed COVID-19 and pulmonary spherical GGOs was performed from January 21, 2020, to March 6, 2020. The initial and follow-up CT images and clinical data were reviewed. The initial CT manifestations of spherical GGOs and their subsequent changes were mainly evaluated. Results:A total of 101 pulmonary spherical GGOs, including 38 with and 63 without consolidation, were found in 33 patients. Of the 101 spherical GGOs, 71 (70.3%) and 30 (29.7%) showed progression and direct absorption on follow-up CT images, respectively. GGOs with consolidation were more likely to progress than those without (84.2% vs. 61.9%, p = 0.017). The 71 progressed lesions mainly showed an increase in size and/or density and most (70.4%) of them extended toward the pleura and developed from spherical to patchy. Internal consolidation appeared and increased in 18 (25.4%) and 22 (31.0%) lesions, respectively. During absorption, all the previous progressed and directly absorbed lesions exhibited a simultaneous decrease in size and density. On each patient’s final CT, more lesions with progression had a residual mixed GGO (40.8% vs. 6.7%, p = 0.002) and fewer had pure GGO (39.4% vs. 60.0%, p = 0.016) than those with direct absorption.Conclusion: In patients with COVID-19, most pulmonary spherical ground-glass opacities would progress, especially those with consolidation, and develop into patchy, subpleural lesions.

scholarly journals Post-COVID-19 pulmonary fibrosis and its predictive factors: a prospective study

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mehrdad Nabahati ◽  
Soheil Ebrahimpour ◽  
Reza Khaleghnejad Tabari ◽  
Rahele Mehraeen
Keyword(s):  
Pulmonary Fibrosis ◽  
Ct Scan ◽  
Predictive Factors ◽  
Lung Fibrosis ◽  
Chest Ct ◽  
Clinical Severity ◽  
Secondary Outcome ◽  
Increased Risk ◽  
Chest Ct Scan

Abstract Background We aimed to prospectively assess the lung fibrotic-like changes, as well as to explore their predictive factors, in the patients who survived Coronavirus Disease 2019 (COVID-19) infection. In this prospective cross-sectional study, we recruited patients who had been treated for moderate or severe COVID-19 pneumonia as inpatients and discharged from Rohani hospital in Babol, northern Iran, during March 2020. The clinical severity of COVID-19 pneumonia was classified as per the definition by World Health Organization. We also calculated the CT severity score (CSS) for all patients at admission. Within the 3 months of follow-up, the next chest CT scan was performed. As the secondary outcome, the patients with fibrotic abnormalities in their second CT scan were followed up in the next 3 months. Results Totally, 173 COVID-19 patients were finally included in the study, of whom 57 (32.9%) were male and others were female. The mean age was 53.62 ± 13.67 years old. At 3-month CT follow-up, evidence of pulmonary fibrosis was observed in 90 patients (52.0%). Consolidation (odds ratio [OR] = 2.84), severe disease (OR 2.40), and a higher CSS (OR 1.10) at admission were associated with increased risk of fibrotic abnormalities found at 3-month CT follow-up. Of 62 patients who underwent chest CT scan again at 6 months of follow-up, 41 patients (66.1%) showed no considerable changes in the fibrotic findings, while the rest of 21 patients (33.9%) showed relatively diminished lung fibrosis. Conclusion Post-COVID-19 lung fibrosis was observed in about half of the survivors. Also, patients with severe COVID-19 pneumonia were at a higher risk of pulmonary fibrosis. Moreover, consolidation, as well as a higher CSS, in the initial chest CT scan, was associated with increased risk of post-COVID-19 lung fibrosis. In addition, some patients experienced diminished fibrotic abnormalities in their chest CT on 6-month follow-up, while some others did not.

scholarly journals Eosinopenia in COVID-19 Patients: A Retrospective Analysis

2020 ◽  
Vol 8 (12) ◽  
pp. 1929
Author(s):  
Narcisse Ndieugnou Djangang ◽  
Lorenzo Peluso ◽  
Marta Talamonti ◽  
Antonio Izzi ◽  
Pierre Alain Gevenois ◽  
...  
Keyword(s):  
Ct Scan ◽  
Retrospective Analysis ◽  
Predictive Accuracy ◽  
Laboratory Finding ◽  
Laboratory Data ◽  
High Body Mass Index ◽  
Chest Ct ◽  
Diagnostic Methods ◽  
Chest Ct Scan ◽  
Pcr Test

Objectives: The aim of this study was to assess the diagnostic role of eosinophils count in COVID-19 patients. Methods: Retrospective analysis of patients admitted to our hospital with suspicion of COVID-19. Demographic, clinical and laboratory data were collected on admission. Eosinopenia was defined as eosinophils < 100 cells/mm3. The outcomes of this study were the association between eosinophils count on admission and positive real-time reverse transcription polymerase chain reaction (rRT-PCR) test and with suggestive chest computerized tomography (CT) of COVID-19 pneumonia. Results: A total of 174 patients was studied. Of those, 54% had positive rRT-PCR for SARS-CoV-2. A chest CT-scan was performed in 145 patients; 71% showed suggestive findings of COVID-19. Eosinophils on admission had a high predictive accuracy for positive rRT-PCR and suggestive chest CT-scan (area under the receiver operating characteristic—ROC curve, 0.84 (95% CIs 0.78–0.90) and 0.84 (95% CIs 0.77–0.91), respectively). Eosinopenia and high LDH were independent predictors of positive rRT-PCR, whereas eosinopenia, high body mass index and hypertension were predictors for suggestive CT-scan findings. Conclusions: Eosinopenia on admission could predict positive rRT-PCR test or suggestive chest CT-scan for COVID-19. This laboratory finding could help to identify patients at high-risk of COVID-19 in the setting where gold standard diagnostic methods are not available.

Postoperative follow-up of lung cancer: Randomized trial comparing two follow-up programs in completely resected non-small cell lung cancer (IFCT-0302).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. TPS7111-TPS7111
Author(s):  
Virginie Westeel ◽  
Fabrice Barlesi ◽  
Jean Domas ◽  
Philippe Girard ◽  
Pascal Foucher ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Physical Examination ◽  
Ct Scan ◽  
Fiberoptic Bronchoscopy ◽  
Chest Ct ◽  
Small Cell Lung ◽  
X Ray ◽  
Chest X Ray ◽  
Chest Ct Scan

TPS7111 Background: There are no robust data published on the follow-up after surgery for non-small cell lung cancer (NSCLC). Current international guidelines are informed by expert opinion. Most of them recommend regular follow-up with clinic visit and thoracic imaging, either chest X-ray of Chest CT-scan. The IFCT-0302 trial addresses the question whether a surveillance program with chest CT-scan and fiberoptic bronchoscopy can improve survival compared to a follow-up only based on physical examination and chest x-ray. There is no such trial ongoing over the world. Methods: The IFCT-0302 trial is a multicenter open-label controlled randomized phase III trial. The objective of the trial is to compare two follow-up programs after surgery for stage I-IIIa NSCLC. The primary endpoint is overall survival. Patients are randomly assigned to arm 1, minimal follow-up, including physical examination and chest x-ray; or arm 2, a follow-up consisting of physical examination and chest x-ray plus chest CT scan and fiberoptic bronchoscopy (optional for adenocarcinomas). In both arms, follow-up procedures are performed every 6 months during the first two postoperative years, and every year between the third and the fifth years. The main eligibility criteria include: completely resected stage I-IIIA (6th UICC TNM classification) or T4 (in case of nodules in the same lobe as the tumor) N0 M0 NSCLC, surgery within the previous 8 weeks. Patients who have received and/or who will receive pre/post-operative chemotherapy and/or radiotherapy are eligible. Statistical considerations: 1,744 patients is required. Accrual status: 1,568 patients from 119 French centers had been included. The end of accrual can be expected for September 2012. Ancillary study: Blood samples are collected in 1000 patients for genomic high density SNP micro-array analysis. This collection will contribute to the French genome wide association study (gwas) of lung cancer gene susceptibility, and the genetic factors predictive of survival and lung cancer recurrence will be analyzed.

scholarly journals Correlations of CT scan with high-sensitivity C-reactive protein and D-dimer in patients with coronavirus disease 2019

2020 ◽  
Vol 36 (6) ◽  
Author(s):  
Jin Zhu ◽  
Cheng Chen ◽  
Rongshu Shi ◽  
Bangguo Li
Keyword(s):  
Ct Scan ◽  
High Sensitivity ◽  
Chest Ct ◽  
Ground Glass ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Group 2 ◽  
Chest Ct Scan ◽  
D Dimer ◽  
Group 1

Objectives: To study the correlations of CT scan with high-sensitivity C-reactive protein (hs-CRP) and D-dimer in patients with coronavirus disease 2019 (COVID-2019). Methods: From January to March 2020, COVID-19 patients were divided into two groups according to the Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (trial version 7), with mild and ordinary cases as Group-1 and critical and severe cases as Group-2. The chest CT scan results, hs-CRP, D-dimmer levels of the two groups from admission to discharge were compared by the c2 test or Fisher’s exact test. The quantitative data were represented as mean ± standard deviation (±s). Intergroup comparisons were performed by the independent samples t test, and the ineligible data were subjected to the nonparametric rank sum test. Binary logistic regression model was used for multivariate correlation analysis, using independent variables that were significant in univariate analysis. The correlations between the above indices were analyzed. Results: In Group-1, there were two cases of normal chest CT scan results, one case of fibrosis, and 25 cases of abnormalities during the first diagnosis, mainly manifested as single or scattered ground-glass shadows. After treatment, the CT scan results became normal. The chest CT scan of Group-2 showed abnormalities, including 21 cases of multiple ground-glass shadows, and six cases of multiple consolidations accompanied by ground-glass shadows, who were critically ill and died. In addition, there were 16 cases of multiple ground glass shadows with partial consolidation, and the CRP and D-dimer levels of Group-2 were significantly higher than those of Group-1. Chest CT scan results were significantly positively correlated with CRP and D-dimer levels (P<0.05). Conclusion: The chest CT scan results of COVID-19 patients are characteristic, being correlated with CRP and D-dimer levels. D-dimer and CRP levels significantly increase in most severe and critical patients, which are closely related to their prognosis. The indices may play predictive roles in clinical treatment and prognosis evaluation. doi: https://doi.org/10.12669/pjms.36.6.2961 How to cite this:Zhu J, Chen C, Shi R, Li B. Correlations of CT scan with high-sensitivity C-reactive protein and D-dimer in patients with coronavirus disease 2019. Pak J Med Sci. 2020;36(6):1397-1401. doi: https://doi.org/10.12669/pjms.36.6.2961 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

COVID-19 image transmission using convolutional neural networks based algorithms for medical applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gangadhar Ch ◽  
Nama Ajay Nagendra ◽  
Syed Mutahar Aaqib ◽  
C.M. Sulaikha ◽  
Shaheena Kv ◽  
...  
Keyword(s):  
Neural Networks ◽  
Information Systems ◽  
Ct Scan ◽  
Health Information ◽  
Convolutional Neural Networks ◽  
Health Information Systems ◽  
Ct Images ◽  
Chest Ct ◽  
Content Type ◽  
Chest Ct Scan

Purpose COVID-19 would have a far-reaching impact on the international health-care industry and the patients. For COVID-19, there is a need for unique screening tests to reliably and rapidly determine who is infected. Medical COVID images protection is critical when data pertaining to computer images are being transmitted through public networks in health information systems. Design/methodology/approach Medical images such as computed tomography (CT) play key role in the diagnosis of COVID-19 patients. Neural networks-based methods are designed to detect COVID patients using chest CT scan images. And CT images are transmitted securely in health information systems. Findings The authors hereby examine neural networks-based COVID diagnosis methods using chest CT scan images and secure transmission of CT images for health information systems. For screening patients infected with COVID-19, a new approach using convolutional neural networks is proposed, and its output is simulated. Originality/value The required patient’s chest CT scan images have been taken from online databases such as GitHub. The experiments show that neural networks-based methods are effective in the diagnosis of COVID-19 patients using chest CT scan images.

scholarly journals Lung Ultrasonography for long-term follow-up of COVID-19 survivors compared to chest CT scan

2021 ◽  
pp. 106384
Author(s):  
Guido Giovannetti ◽  
Lucrezia De Michele ◽  
Michele De Ceglie ◽  
Paola Pierucci ◽  
Alessandra Mirabile ◽  
...  
Keyword(s):  
Ct Scan ◽  
Chest Ct ◽  
Lung Ultrasonography ◽  
Long Term Follow Up ◽  
Chest Ct Scan

scholarly journals The Spectrum of Chest CT-Scans in the Hospitalized Patients with the Coronavirus Disease

2020 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Maria Shirvani ◽  
Alireza Janbakhsh ◽  
Feizollah Mansouri ◽  
Babak Sayad ◽  
Siavash Vaziri ◽  
...  
Keyword(s):  
Ct Scan ◽  
Clinical Symptoms ◽  
Large Family ◽  
Chest Ct ◽  
Hospitalized Patients ◽  
Ct Scans ◽  
Ground Glass ◽  
Sample Population ◽  
Ground Glass Opacification ◽  
Chest Ct Scan

Background: Coronaviruses are a large family of RNA viruses, which range from the common cold virus to the causative agent of more severe diseases. Coronavirus was declared a pandemic in December 2019 in Wuhan, China. Iran has been an endemic zone for the spread of the coronavirus since the outset of this global epidemic and has remained among the countries largely affected by high rates of the disease. Objectives: The present study aimed to investigate the range of the chest computed tomography (CT) scan findings among the hospitalized patients with COVID-19 in Kermanshah, Iran during March-April 2020 to contribute to the accurate diagnosis of the infected patients. Methods: The sample population consisted of 286 hospitalized patients diagnosed with or suspected of the coronavirus disease. Chest CT-scan images and clinical data were reviewed, and their correlation was analyzed. Results: In total, 176 patients (61.53%) were male, and 110 (38.47%) were female. The mean age of the patients was 56 years. Polymerase chain reaction (PCR) results showed that 35.31% of the cases had coronavirus, while the results were negative in 64.69% of the cases. In addition, the CT-scan findings indicated 77.27% abnormal and 22.73% normal chest CT-scans. Among the patients, 75.87% recovered completely, and 18.53% died. The major CT abnormalities were diffuse ground-glass opacification (35.66%), peripheral ground-glass opacification (bilateral; 21.33%), and a combination of diffuse and peripheral ground-glass lesions (18.88%). The consolidation lesion of one lobe was detected in 16 patients, and the consolidation lesion of more than one lobe was observed in 40 patients. Conclusions: According to the results, the most common chest CT-scan findings in COVID-19 include diffuse ground-glass opacification, peripheral ground-glass opacification (bilateral), central ground-glass opacification (bilateral), a combination of diffuse and peripheral ground-glass opacification, a combination of central and peripheral ground-glass opacification, the consolidation lesion of one lobe, and the consolidation lesion of more than one lobe. Furthermore, significant correlations were observed between the CT-scans and the main clinical symptoms, while no significant correlations were denoted between the chest CT-scan and PCR results.

scholarly journals Chest X-Ray Findings and Temporal Lung Changes in Patients with COVID-19 Pneumonia

2020 ◽  
Author(s):  
Liqa A Rousan ◽  
Eyhab Elobeid ◽  
Musaab Karrar ◽  
Yousef Khader
Keyword(s):  
Ct Scan ◽  
Chest Ct ◽  
Common Finding ◽  
X Rays ◽  
Radiographic Findings ◽  
X Ray ◽  
Abnormal Chest ◽  
Chest X Ray ◽  
Chest Ct Scan

Abstract Background: Chest CT scan and chest x-rays show characteristic radiographic findings in patients with COVID-19 pneumonia. Chest x-ray can be used in diagnosis and follow up in patients with COVID-19 pneumonia. The study aims at describing the chest x-ray findings and temporal radiographic changes in COVID-19 patients.Methods: From March 15 to April 20, 2020 patients with positive reverse transcription polymerase chain reaction (RT-PCR) for COVID-19 were retrospectively studied. Patients’ demographics, clinical characteristics, and chest x-ray findings were reported. Radiographic findings were correlated with the course of the illness and patients’ symptoms.Results: A total of 88 patients (50 (56.8%) females and 38 (43.2%) males) were admitted to the hospital with confirmed COVID-19 pneumonia. Their age ranged from 3-80 years (35.2 ±18.2 years). 48/88 (45%) were symptomatic, only 13/88 (45.5%) showed abnormal chest x-ray findings. A total of 190 chest x-rays were obtained for the 88 patients with a total of 59/190 (31%) abnormal chest x-rays. The most common finding on chest x-rays was peripheral ground glass opacities (GGO) affecting the lower lobes. In the course of illness, the GGO progressed into consolidations peaking around 6-11 days (GGO 70%, consolidations 30%). The consolidations regressed into GGO towards the later phase of the illness at 12-17 days (GGO 80%, consolidations 10%). There was increase in the frequency of normal chest x-rays from 9% at days 6- 11 up to 33% after 18 days indicating a healing phase. The majority (12/13, 92.3%) of patients with abnormal chest x-rays were symptomatic (P=0.005).Conclusion: The chest x-ray findings were similar to those reported on chest CT scan in patients with COVID-19, Chest x-ray can be used in diagnosis and follow up in patients with COVID-19 pneumonia.

scholarly journals Spectrum of Ground Glass Opacities in Chest CT of COVID-19 Patients in Karbala, Iraq

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Dhia Mahdey Alghazali ◽  
Maytham A Maamera ◽  
Haider Fadel Alkazraji ◽  
Ali A Abutiheen
Keyword(s):  
Ct Scan ◽  
Cross Sectional Study ◽  
Chest Ct ◽  
Ground Glass ◽  
P Value ◽  
Late Time ◽  
Chi Square ◽  
Cross Sectional ◽  
Male Predominance ◽  
Chest Ct Scan

Objective: To describe the ground-glass opacities (GGO) seen in chest CT scans of COVID-19 patients and to estimate the association between these opacities and the time of clinical presentation. Patients and methods: A cross-sectional study involving 81 COVID-19 confirmed patients in Imam Al-Hussein Medical city in Karbala-Iraq during the period from March 1st to April 20, 2020. Chest CT scan findings were evaluated by 2 radiologists and categorized accordingly. Chi-square test was used for statistical analysis and a P value of less than 0.05 was considered statistically significant. Results: The mean age ± standard deviation of patients was 53.5 ± 17.1 years, with male predominance as 63 (77.8%) of cases were males. Nearly half of the patients were presented within the second week of starting the sign and symptoms. GGO was present in 79 scans (97.5%), followed by consolidation opacity in 29 patients (35.8%). Four types of GGO were described. Bilateral multiple subpleural GGO was the most prevalent type. There was a significant association between late time of patient presentation and more extensive GGO type. Conclusion: Chest CT scan is valuable in the diagnosis and management of COVID-19 cases. The presence of GGO in CT scan of a patient that previously had no chest illness is highly suggestive of COVID-19 disease, different types of GGO were seen. Bilateral confluent type of GGO is associated with more serious and delayed status and warns the need for intensive care unit admission.

CNN-based Prediction of COVID-19 using Chest CT Images

2021 ◽  
pp. 2250039
Author(s):  
Tanvi Arora
Keyword(s):  
Ct Scan ◽  
Good Accuracy ◽  
Ct Images ◽  
Chest Ct ◽  
X Ray ◽  
Infected People ◽  
The World ◽  
Severity Of The Disease ◽  
Application Programs ◽  
Chest Ct Scan

The coronavirus disease (COVID-19) pandemic that is caused by the SARS-CoV2 has spread all over the world. It is an infectious disease that can spread from person to person. The severity of the disease can be categorized into five categories namely asymptomatic, mild, moderate, severe, and critical. From the reported cases thus, it has been seen that 80% of the cases that test positive with COVID-19 infection have less than moderate complications, whereas 20% of the positive cases develop severe and critical complications. The virus infects the lungs of an individual, therefore, it has been observed that the X-ray and computed tomography (CT) scan images of the infected people can be used by the machine learning-based application programs to predict the presence of the infection. Therefore, in the proposed work, a Convolutional Neural Network model based upon the DenseNet architecture is being used to predict the presence of COVID-19 infection using the CT scan images of the chest. The proposed work has been carried out using the dataset of the CT images from the COVID CT Dataset. It has 349 images marked as COVID-19 positive and 397 images have been marked as COVID-19 negative. The proposed system can categorize the test set images with an accuracy of 91.4%. The proposed method is capable of detecting the presence of COVID-19 infection with good accuracy using the chest CT scan images of the humans.

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