Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
960 participants
OBSERVATIONAL
2019-07-23
2024-12-31
Brief Summary
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The typical radiological finding of COVID-19 is an interstitial pneumonia, which can be responsible, in a significant portion of patients, of an acute respiratory distress syndrome (ARDS).
Low-dose chest CT and simple blood tests could identify sub-solid pulmonary nodules (SSNs) indicative of COVID-19 infection in asymptomatic subjects.
Objectives of this observational study are the early detection of COVID-19 markers indicative of prior exposure or persisting viral infection in asymptomatic subjects and the assessment of the frequency and outcome of COVID-19-related SSNs in asymptomatic subjects by time, domicile, and other individual risk factors.
SMILE lung CT screening program cohort has been considered, based on 960 subjects at high lung cancer risk for tobacco smoking (≥20 pack/year) and age (50-75 years), together with inflammatory and respiratory profile. SMILE utilizes a top technology dual-source CT scanner (Somatom Force) with the lowest radiation dose ever applied to lung screening. All chest CT images from screening subjects will be re-evaluated by two additional CAD programs, specifically designed for the analysis of SSNs and quantification of the total volume of lung parenchyma showing an increased density. This re-evaluation will improve the sensitivity and specificity of radiomic assessment.
This study cohort, enriched by the already established longitudinal biobank of frozen plasma samples, represent an ideal opportunity to assess the frequency of SSNs in asymptomatic subjects, due to the effect of COVID-19, particularly among subjects living in areas at high risk of viral exposure. It will also be possible to evaluate if COVID-19-related SSNs are associated with chronic co-morbidity, other individual risk factors, inflammatory (CRP) / immunomodulatory (25(OH)D) blood profile, and/or can be traced by immune markers such as IgM/IgG and other cytokines.
Clinical data will be integrated with an analysis of the IgG-IgM profile specific for covid-19, on the plasma samples taken at the time of the CT scan, or subsequently, in collaboration with University of Milan, Luigi Devoto Work Clinic.
The lasting collaboration with the Radiological Science Department of the University of Parma in lung screening also offers the opportunity to validate the results obtained in this cohort on chest CT performed at the University Parma Hospital during the last two months in symptomatic subjects for suspected covid-19 pneumonia.
In collaboration with University of Milano Bicocca, Machine Learning (ML) tools will be applied to predict the clinical relevance, severity and ultimate outcome of SSNs, based on radiomic CT features, epidemiologic risk, co-morbidity and inflammatory/immune blood biomarkers. ML analysis will generate a predictive algorithm for clinical outcome of SSNs, and specifically the risk of COV-I9 infection and unfavorable disease prognosis.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Lung cancer screening subjects
Subjects enrolled in SMILE lung cancer screening trial
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* Absence of tumors for at least 5 years
Exclusion Criteria
* Chronic treatment with acetylsalicylic acid, or other anti-clotting or anti-coagulant drugs
* Treatment with methotrexate
* Existing Mastocytosis
* History of asthma induced by the administration of salicylates or substances to similar activity, particularly non-steroidal anti-inflammatory drugs
* Gastroduodenal ulcer
* Hemorrhagic diathesis
* Severe chronic pathology
* Serious psychiatric problems
* Previous treatment with Cytisine
* Abuse of alcohol or other substances
50 Years
75 Years
ALL
Yes
Sponsors
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University of Parma
OTHER
University of Milan
OTHER
University of Milano Bicocca
OTHER
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
OTHER
Responsible Party
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Principal Investigators
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Ugo Pastorino, MD
Role: PRINCIPAL_INVESTIGATOR
Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
Locations
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Fondazione IRCCS Istituto Nazionale dei Tumori
Milan, , Italy
Countries
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References
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Pan F, Ye T, Sun P, Gui S, Liang B, Li L, Zheng D, Wang J, Hesketh RL, Yang L, Zheng C. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020 Jun;295(3):715-721. doi: 10.1148/radiol.2020200370. Epub 2020 Feb 13.
Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, Huang H, Zhang L, Zhou X, Du C, Zhang Y, Song J, Wang S, Chao Y, Yang Z, Xu J, Zhou X, Chen D, Xiong W, Xu L, Zhou F, Jiang J, Bai C, Zheng J, Song Y. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med. 2020 Jul 1;180(7):934-943. doi: 10.1001/jamainternmed.2020.0994.
Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, Zhang HY, Sun W, Wang Y. COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020 Jun;92(6):577-583. doi: 10.1002/jmv.25757. Epub 2020 Mar 23.
Grant WB, Lahore H, McDonnell SL, Baggerly CA, French CB, Aliano JL, Bhattoa HP. Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths. Nutrients. 2020 Apr 2;12(4):988. doi: 10.3390/nu12040988.
Gallivanone F, Cava C, Corsi F, Bertoli G, Castiglioni I. In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis. Int J Mol Sci. 2019 Nov 20;20(23):5825. doi: 10.3390/ijms20235825.
Pastorino U, Silva M, Sestini S, Sabia F, Boeri M, Cantarutti A, Sverzellati N, Sozzi G, Corrao G, Marchiano A. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Ann Oncol. 2019 Oct 1;30(10):1672. doi: 10.1093/annonc/mdz169. No abstract available.
Silva M, Schaefer-Prokop CM, Jacobs C, Capretti G, Ciompi F, van Ginneken B, Pastorino U, Sverzellati N. Detection of Subsolid Nodules in Lung Cancer Screening: Complementary Sensitivity of Visual Reading and Computer-Aided Diagnosis. Invest Radiol. 2018 Aug;53(8):441-449. doi: 10.1097/RLI.0000000000000464.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. doi: 10.1016/0021-9681(87)90171-8.
Related Links
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Data of Italian Ministry of Health
Data of "Istituto Superiore di Sanità"
Other Identifiers
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INT 49/20
Identifier Type: -
Identifier Source: org_study_id
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