Clinical trial · Observational
FOUND - Ancillary Study to Smile Protocol NCT03654105
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| COVID 19 | — | UNRESOLVED | — |
| Inflammatory Status | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- Lung cancer screening subjects
- description
- Subjects enrolled in SMILE lung cancer screening trial
Primary outcomes (4)
- measure
- Temporal variations in the frequency and morphology of SSNs according to the COVID-19 epidemic
- timeFrame
- September 2019 - February 2020
- description
- Evaluate the increase of frequency and severity of SSNs according to the COVID-19 epidemic. The results of chest CT performed in different periods will be compared (December 2019 -February 2020; September 2019 - November 2019)
- measure
- Geographical variations in the frequency and morphology of SSNs according to the COVID-19 epidemic
- timeFrame
- September 2019 - February 2020
- description
- To this end, subjects domiciled in different geographical areas will be compared: a) high risk (≥ 200 covid cases / 100,000 inhabitants); b) medium risk (100-200 cases / 100,000); low risk (\<100 cases / 100,000)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 50 Years
- Maximum age
- 75 Years
Show eligibility criteria text
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
References
Publications (8)
- RESULTPan 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. PMID 32053470
- RESULTWu 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. PMID 32167524
- RESULTLi 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. PMID 32162702
- RESULTGrant 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. PMID 32252338
- RESULTGallivanone 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. PMID 31756987
- RESULTPastorino 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. PMID 31168572