Clinical trial NCT04441814: FOUND - Ancillary Study to Smile Protocol NCT03654105
Official title: Frequency and OUtcome of Sub-solid Pulmonary Nodules in Asymptomatic Subjects Tested by Chest CT Screening Versus COVID-19 Patients (FOUND) - Ancillary Study to Smile Protocol NCT03654105
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: COVID 19
Inflammatory Status
Keywords: COVID
Lung cancer screening
Subsolid pulmonary nodules
Low dose CT screening
Blood biomarkers
Lead sponsor: Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Sponsor class: OTHER
Collaborators: name: University of Parma
name: University of Milan
name: University of Milano Bicocca
First posted: 2020-06-22
Enrollment: 960
Sex: ALL
Minimum age (years): 50
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Subjects who enrolled in SMILE protocol and signed the SMILE protocol NCT03654105 informed consent

Brief summary: During the current pandemic, in Italy the majority of asymptomatic or pauci-symptomatic COVID-19 cases were not identified nor diagnosed and this fact caused a decrease in the effectiveness of the various containment measures implemented. Therefore, in a future scenario where a new viral swarm is expected, the early identification of all infected cases becomes essential to plan and activate a containment strategy for the spread of the virus, given the current absence of vaccines.

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.


Eligibility criteria: Inclusion Criteria:

* Elegibility to annual LDCT screening
* Absence of tumors for at least 5 years
* Signed informed consent form meet inclusion criteria outlined in SMILE Protocol NCT03654105

Exclusion Criteria:

* Hypersensitivity to acetylsalicylic acid, salicylates or any of the excipients
* 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

Primary outcomes:
measure: Temporal variations in the frequency and morphology of SSNs according to the COVID-19 epidemic
timeFrame: September 2019 - February 2020
measure: Geographical variations in the frequency and morphology of SSNs according to the COVID-19 epidemic
timeFrame: September 2019 - February 2020
measure: Correlation between sub-solid lesions and individual risk
timeFrame: September 2019 - February 2020
measure: Correlation between sub-solid lesions and incidence of acute events
timeFrame: September 2019 - August 2020
Secondary outcomes:
Other outcomes:
Locations:
city: Milan
country: Italy
facility: Fondazione IRCCS Istituto Nazionale dei Tumori
Study officials:
affiliation: Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
name: Ugo Pastorino, MD
role: PRINCIPAL_INVESTIGATOR
References:
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citation: 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.
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