scholarly journals Longitudinal evaluation for COVID ‐19 chest CT disease progression based on Tchebichef moments

2021 ◽  
Author(s):  
Lu Tang ◽  
Chuangeng Tian ◽  
Yankai Meng ◽  
Kai Xu
Keyword(s):  
Disease Progression ◽  
Chest Ct ◽  
Longitudinal Evaluation ◽  
Tchebichef Moments

Study on the Dynamic Changes in Chest CT Findings of COVID-19 Rehabilitated Patients

2020 ◽  
pp. 1-7
Keyword(s):  
Disease Progression ◽  
Onset Time ◽  
Ground Glass Opacity ◽  
Chest Ct ◽  
Ground Glass ◽  
Dynamic Changes ◽  
Course Of Disease ◽  
Pleural Thickening ◽  
Ct Findings ◽  
Interlobular Septal Thickening

Objective: To study the dynamic changes in CT findings in COVID-19 (coronavirus disease-19, COVID-19) rehabilitated patients. Methods: A total of 148 chest CT images of 37 patients with COVID-19 were collected. In the first 21 days of the course of disease, 7 stages were performed every 3 days, and the eighth stage was performed after 21 days. Results: In the first chest CT examination, 19 cases were ground glass opacity, and 18 cases were high-density shadows with consolidation. The lesion shape was flaky and patchy in 33 cases. The percentage of consolidation, air bronchogram, fiber cord, interlobular septal thickening, subpleural line and pleural thickening were the highest on days 4-6, 7-9, 7-9, 10-12, 19-21 and 19-21, respectively. The highest percentage of disease progression was 80.00% on days 4-6, and then the percentage of disease progression gradually decreased with the extension of the onset time. The percentage of patients with improvement gradually increased from days 4-6, reaching 83.33% on days 16-18 and 100.00% on day 21. The percentage of lesion range enlargement and density increase was the highest on days 4-6, both of which were 60.00%，Then the percentage of both decreased gradually. The percentage of patients with lesion range reduction and density absorption dilution increased gradually with the onset time. There was no obvious regularity in the number of lesions. Conclusion: Patients with COVID-19 have regular changes in their lung conditions.

scholarly journals Visual Emphysema at Chest CT in GOLD Stage 0 Cigarette Smokers Predicts Disease Progression: Results from the COPDGene Study

Radiology ◽  
2020 ◽  
Vol 296 (3) ◽  
pp. 641-649 ◽  
Author(s):  
Andrea S. Oh ◽  
Matthew Strand ◽  
Katherine Pratte ◽  
Elizabeth A. Regan ◽  
Stephen Humphries ◽  
...  
Keyword(s):  
Disease Progression ◽  
Chest Ct ◽  
Cigarette Smokers ◽  
Gold Stage

Chest CT Scores Of Severity Predict Future Lung Disease Progression In Children With CF

2011 ◽  
Author(s):  
Don B. Sanders ◽  
Zhanhai Li ◽  
Alan Brody ◽  
Jannette Collins ◽  
Lynn Broderick ◽  
...  
Keyword(s):  
Lung Disease ◽  
Disease Progression ◽  
Chest Ct

scholarly journals The clinical characteristics of COVID-19: a retrospective analysis of 104 patients from the outbreak on board the Diamond Princess cruise ship in Japan

2020 ◽  
Author(s):  
Sakiko Tabata ◽  
Kazuo Imai ◽  
Shuichi Kawano ◽  
Mayu Ikeda ◽  
Tatsuya Kodama ◽  
...  
Keyword(s):  
Disease Progression ◽  
Symptom Onset ◽  
Clinical Characteristics ◽  
Serum Lactate ◽  
Chest Ct ◽  
Older Age ◽  
Cruise Ship ◽  
Radiological Findings ◽  
Novel Coronavirus ◽  
Observation Period

AbstractBackgroundThe ongoing outbreak of the coronavirus disease 2019 (COVID-19) is a global threat. Identification of markers for symptom onset and disease progression is a pressing issue. We compared the clinical features on admission among patients who were diagnosed with asymptomatic, mild, and severe COVID-19 at the end of observation.MethodsThis retrospective, single-center study included 104 patients with laboratory-confirmed COVID-19 from the mass infection on the Diamond Princess cruise ship from February 11 to February 25, 2020. Clinical records, laboratory data, and radiological findings were analyzed. Clinical outcomes were followed up until February 26, 2020. Clinical features on admission were compared among those with different disease severity at the end of observation. Univariate analysis identified factors associated with symptom onset and disease progression.FindingsThe median age was 68 years, and 54 patients were male. Briefly, 43, 41, and 20 patients on admission and 33, 43, and 28 patients at the end of observation had asymptomatic, mild, and severe COVID-19, respectively. Serum lactate hydrogenase levels were significantly higher in 10 patients who were asymptomatic on admission but developed symptomatic COVID-19 compared with 33 patients who remained asymptomatic throughout the observation period. Older age, consolidation on chest computed tomography, and lymphopenia on admission were more frequent in patients with severe COVID-19 than those with mild COVID-19 at the end of observation.InterpretationLactate dehydrogenase level is a potential predictor of symptom onset in COVID-19. Older age, consolidation on chest CT images, and lymphopenia might be risk factors for disease progression of COVID-19 and contribute to the clinical management.FundingNot applicable.Research in contextEvidence before this studyWe searched the PubMed database from its inception until March 1, 2020, for articles published in English using the keywords “novel coronavirus,” “2019 novel coronavirus,” “2019-nCoV,” “Severe acute respiratory syndrome coronavirus 2,” “SARS-CoV2,” “COVID-19,” “mass infection,” “herd infection,” “cruise ship,” “Diamond Princess,” “asymptomatic,” and “subclinical.” There were no published clinical studies featuring COVID-19 as a result of mass infection on board a cruise ship. We found published articles entitled “Characteristics of COVID-19 infection in Beijing” and “Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study,” which compared patients with asymptomatic, mild, and severe COVID-19. However, none of the studies described potential markers for symptom onset or disease progression in patients with COVID-19.Added value of this studyWe present the differences in clinical characteristics of 104 patients with laboratory-confirmed COVID-19 as a result of mass infection on the Diamond Princess cruise ship who were treated at Self-Defense Forces Central Hospital, Japan from February 11 to February 25, 2020. On admission, 43, 41, and 20 patients had asymptomatic, mild, and severe COVID-19, respectively, whereas 33, 43, and 28 patients were determined to have asymptomatic, mild, and severe COVID-19, respectively, at the end of observation. During the observation period, 10 of the 43 (23.3%) asymptomatic patients on admission developed symptoms of COVID-19. Conversely, eight of the 84 (9.5%) patients with asymptomatic and mild COVID-19 on admission developed severe disease during the observation period. The serum lactate dehydrogenase (LDH) levels were significantly higher in 10 patients who were initially asymptomatic on admission to the hospital and developed symptomatic COVID-19 during the observation period compared with 33 patients who remained asymptomatic throughout the observation period. The prevalence rates of consolidation on chest computed tomography (CT) images and lymphopenia were significantly higher in eight patients who developed severe COVID-19 during the observation period compared with the 76 patients with asymptomatic or mild disease at the end of the observation. Older age, consolidation on chest CT, and lymphopenia on admission were more frequent in patients with severe COVID-19 (n = 28) than those with mild COVID-19 (n = 43) at the end of observation. LDH level might be marker for symptom onset in patients with COVID-19, whereas older age, consolidation on chest CT imaging, and lymphopenia are potential risk factors for disease progression. The current report findings will contribute to the improvement of clinical management in patients with COVID-19.Implications of all the available evidenceSerum LDH level is a potential predictor of symptom onset of COVID-19, whereas older age, consolidation on chest CT imaging, and lymphopenia have potential utility as markers for disease progression.

scholarly journals Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT

2020 ◽  
Author(s):  
Youzhen Feng ◽  
Sidong Liu ◽  
Zhongyuan Cheng ◽  
Juan Quiroz ◽  
Data Rezazadegan ◽  
...  
Keyword(s):  
Disease Progression ◽  
Lung Lesion ◽  
Chest Ct ◽  
Ct Scans ◽  
Severity Assessment ◽  
Prognostic Information ◽  
Feature Vectors ◽  
Progression Prediction ◽  
The Relationship ◽  
Potential Use

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. [Manuscript last updated on 20 May, 2020.]

scholarly journals Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuehua Li ◽  
Kai Shang ◽  
Wei Bian ◽  
Li He ◽  
Ying Fan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Outcome ◽  
Disease Progression ◽  
Multivariate Regression ◽  
Ground Glass Opacity ◽  
Chest Ct ◽  
Imaging Analysis ◽  
Unfavorable Clinical Outcome ◽  
Severe Group ◽  
Severe Type

AbstractTo investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm3 versus 101.12 ± 127 cm3, p = 0.013) as well as consolidation volume (40.85 ± 60.4 cm3 versus 6.63 ± 14.91 cm3, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.

scholarly journals Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 471
Author(s):  
You-Zhen Feng ◽  
Sidong Liu ◽  
Zhong-Yuan Cheng ◽  
Juan C. Quiroz ◽  
Dana Rezazadegan ◽  
...  
Keyword(s):  
Disease Progression ◽  
Lung Lesion ◽  
Chest Ct ◽  
Ct Scans ◽  
Severity Assessment ◽  
Prognostic Information ◽  
Feature Vectors ◽  
Progression Prediction ◽  
The Relationship ◽  
Potential Use

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT (Preprint)

2021 ◽  
Author(s):  
You-Zhen Feng ◽  
Sidong Liu ◽  
Zhong-Yuan Cheng ◽  
Juan C. Quiroz ◽  
Dana Rezazadegan ◽  
...  
Keyword(s):  
Disease Progression ◽  
Lung Lesion ◽  
Chest Ct ◽  
Ct Scans ◽  
Severity Assessment ◽  
Prognostic Information ◽  
Feature Vectors ◽  
Institutional Review ◽  
Retrospective Nature ◽  
Progression Prediction

BACKGROUND Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. OBJECTIVE This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. METHODS A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. RESULTS Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. CONCLUSIONS The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. CLINICALTRIAL we performed a retrospective in China. This multicentre study was approved by the institutional review board of the principal investigator’s hospital. Informed consent from patients was exempted due to the retrospective nature of this study.

scholarly journals Clinical indications and scanning protocols for chest CT in children with cystic fibrosis: a survey of UK tertiary centres

2018 ◽  
Vol 2 (1) ◽  
pp. e000367
Author(s):  
Francis J Gilchrist ◽  
Richard Buka ◽  
Mary Jones ◽  
Sheng Ang Ho ◽  
Warren Lenney ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Disease Progression ◽  
Allergic Bronchopulmonary Aspergillosis ◽  
Chest Ct ◽  
National Guideline ◽  
General Anaesthetic ◽  
Intravenous Contrast ◽  
Clinical Scenarios ◽  
Cystic Changes ◽  
Chest X Ray

ObjectivesChest CT is increasingly used to monitor disease progression in children with cystic fibrosis (CF) but there is no national guideline regarding its use. Our objective was to assess the indications for undertaking chest CT and the protocols used to obtain scans.Design, Setting and participantsAn electronic questionnaire was developed to assess clinicians views on chest CT in children with CF. It included general questions on perceived benefits and specific questions about its role in five clinical scenarios. It was sent to the clinical lead in 27 UK paediatric CF centres. A separate questionnaire was developed to collect the technical details of chest CT in children with CF. It was sent to the superintendent radiographer at each of the 27 centres.ResultsResponses were obtained from 27 (100%) clinical leads and 22 (81%) superintendent radiographers. 93% clinicians reported chest CT useful in monitoring disease progression and 70% said it frequently altered management. Only 5 (19%) undertook routine scans. To aid diagnosis, 81% performed chest CT in non-tuberculous mycobacterial disease and 15% in allergic bronchopulmonary aspergillosis. There was wide variation in the perceived need for and/or timing of chest CT in children with reduced lung function with no benefit from intravenous antibiotics, new cystic changes on chest X-ray, and lobar collapse. The radiographers reported using a mixture of helical (volumetric) and axial scans depending on the clinical question, the age and the cooperation of the child. When indicated, 6 (27%) used sedation and 16 (73%) general anaesthetic. Only 1 (5%) used intravenous contrast routinely and 3 (14%) obtained expiratory images routinely.ConclusionsThere is marked variation in the use of chest CT in children with CF and in the scan protocols. The lack of a national guideline is likely to be contributing to this lack of standardisation.

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