<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04441814</url>
  </required_header>
  <id_info>
    <org_study_id>INT 49/20</org_study_id>
    <nct_id>NCT04441814</nct_id>
  </id_info>
  <brief_title>FOUND - Ancillary Study to Smile Protocol NCT03654105</brief_title>
  <acronym>FOUND</acronym>
  <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</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Fondazione IRCCS Istituto Nazionale dei Tumori, Milano</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>University of Parma</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University of Milan</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University of Milano Bicocca</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Fondazione IRCCS Istituto Nazionale dei Tumori, Milano</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      During the current pandemic, in Italy the majority of asymptomatic or pauci-symptomatic&#xD;
      COVID-19 cases were not identified nor diagnosed and this fact caused a decrease in the&#xD;
      effectiveness of the various containment measures implemented. Therefore, in a future&#xD;
      scenario where a new viral swarm is expected, the early identification of all infected cases&#xD;
      becomes essential to plan and activate a containment strategy for the spread of the virus,&#xD;
      given the current absence of vaccines.&#xD;
&#xD;
      The typical radiological finding of COVID-19 is an interstitial pneumonia, which can be&#xD;
      responsible, in a significant portion of patients, of an acute respiratory distress syndrome&#xD;
      (ARDS).&#xD;
&#xD;
      Low-dose chest CT and simple blood tests could identify sub-solid pulmonary nodules (SSNs)&#xD;
      indicative of COVID-19 infection in asymptomatic subjects.&#xD;
&#xD;
      Objectives of this observational study are the early detection of COVID-19 markers indicative&#xD;
      of prior exposure or persisting viral infection in asymptomatic subjects and the assessment&#xD;
      of the frequency and outcome of COVID-19-related SSNs in asymptomatic subjects by time,&#xD;
      domicile, and other individual risk factors.&#xD;
&#xD;
      SMILE lung CT screening program cohort has been considered, based on 960 subjects at high&#xD;
      lung cancer risk for tobacco smoking (≥20 pack/year) and age (50-75 years), together with&#xD;
      inflammatory and respiratory profile. SMILE utilizes a top technology dual-source CT scanner&#xD;
      (Somatom Force) with the lowest radiation dose ever applied to lung screening. All chest CT&#xD;
      images from screening subjects will be re-evaluated by two additional CAD programs,&#xD;
      specifically designed for the analysis of SSNs and quantification of the total volume of lung&#xD;
      parenchyma showing an increased density. This re-evaluation will improve the sensitivity and&#xD;
      specificity of radiomic assessment.&#xD;
&#xD;
      This study cohort, enriched by the already established longitudinal biobank of frozen plasma&#xD;
      samples, represent an ideal opportunity to assess the frequency of SSNs in asymptomatic&#xD;
      subjects, due to the effect of COVID-19, particularly among subjects living in areas at high&#xD;
      risk of viral exposure. It will also be possible to evaluate if COVID-19-related SSNs are&#xD;
      associated with chronic co-morbidity, other individual risk factors, inflammatory (CRP) /&#xD;
      immunomodulatory (25(OH)D) blood profile, and/or can be traced by immune markers such as&#xD;
      IgM/IgG and other cytokines.&#xD;
&#xD;
      Clinical data will be integrated with an analysis of the IgG-IgM profile specific for&#xD;
      covid-19, on the plasma samples taken at the time of the CT scan, or subsequently, in&#xD;
      collaboration with University of Milan, Luigi Devoto Work Clinic.&#xD;
&#xD;
      The lasting collaboration with the Radiological Science Department of the University of Parma&#xD;
      in lung screening also offers the opportunity to validate the results obtained in this cohort&#xD;
      on chest CT performed at the University Parma Hospital during the last two months in&#xD;
      symptomatic subjects for suspected covid-19 pneumonia.&#xD;
&#xD;
      In collaboration with University of Milano Bicocca, Machine Learning (ML) tools will be&#xD;
      applied to predict the clinical relevance, severity and ultimate outcome of SSNs, based on&#xD;
      radiomic CT features, epidemiologic risk, co-morbidity and inflammatory/immune blood&#xD;
      biomarkers. ML analysis will generate a predictive algorithm for clinical outcome of SSNs,&#xD;
      and specifically the risk of COV-I9 infection and unfavorable disease prognosis.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Enrolling by invitation</overall_status>
  <start_date type="Actual">July 23, 2019</start_date>
  <completion_date type="Anticipated">December 2024</completion_date>
  <primary_completion_date type="Anticipated">December 2024</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Temporal variations in the frequency and morphology of SSNs according to the COVID-19 epidemic</measure>
    <time_frame>September 2019 - February 2020</time_frame>
    <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)</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Geographical variations in the frequency and morphology of SSNs according to the COVID-19 epidemic</measure>
    <time_frame>September 2019 - February 2020</time_frame>
    <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 (&lt;100 cases / 100,000)</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Correlation between sub-solid lesions and individual risk</measure>
    <time_frame>September 2019 - February 2020</time_frame>
    <description>Establish whether subjects with a higher level of risk and / or individual damage have a higher frequency and / or severity of sub-solid lung injury.&#xD;
For this purpose, the following parameters will be analyzed: a) inflammatory profile (PCR), b) respiratory capacity (FEV1), c) co-morbidity (e.g. obesity, diabetes, cardio-vascular disease, COPD), d) level of CO; e) age, f) gender.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Correlation between sub-solid lesions and incidence of acute events</measure>
    <time_frame>September 2019 - August 2020</time_frame>
    <description>Establish whether subjects with greater frequency and/or severity of sub-solid lung lesions, associated or not with other individual risk factors, have a higher incidence of acute pathological events, in particular of respiratory nature, in the three (six) months following the CT exam. For this purpose, information on the time of onset and duration of these events will be collected: fever, cough, dyspnoea, hospitalization, positivity for covid-19, treatment in the ICU, and eventual death.</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">960</enrollment>
  <condition>COVID 19</condition>
  <condition>Inflammatory Status</condition>
  <arm_group>
    <arm_group_label>Lung cancer screening subjects</arm_group_label>
    <description>Subjects enrolled in SMILE lung cancer screening trial</description>
  </arm_group>
  <biospec_retention>Samples Without DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      Blood samples to evaluate inflammatory markers&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        Subjects who enrolled in SMILE protocol and signed the SMILE protocol NCT03654105 informed&#xD;
        consent&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Elegibility to annual LDCT screening&#xD;
&#xD;
          -  Absence of tumors for at least 5 years&#xD;
&#xD;
          -  Signed informed consent form meet inclusion criteria outlined in SMILE Protocol&#xD;
             NCT03654105&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Hypersensitivity to acetylsalicylic acid, salicylates or any of the excipients&#xD;
&#xD;
          -  Chronic treatment with acetylsalicylic acid, or other anti-clotting or anti-coagulant&#xD;
             drugs&#xD;
&#xD;
          -  Treatment with methotrexate&#xD;
&#xD;
          -  Existing Mastocytosis&#xD;
&#xD;
          -  History of asthma induced by the administration of salicylates or substances to&#xD;
             similar activity, particularly non-steroidal anti-inflammatory drugs&#xD;
&#xD;
          -  Gastroduodenal ulcer&#xD;
&#xD;
          -  Hemorrhagic diathesis&#xD;
&#xD;
          -  Severe chronic pathology&#xD;
&#xD;
          -  Serious psychiatric problems&#xD;
&#xD;
          -  Previous treatment with Cytisine&#xD;
&#xD;
          -  Abuse of alcohol or other substances&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>50 Years</minimum_age>
    <maximum_age>75 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Ugo Pastorino, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Fondazione IRCCS Istituto Nazionale dei Tumori</name>
      <address>
        <city>Milan</city>
        <zip>20133</zip>
        <country>Italy</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Italy</country>
  </location_countries>
  <link>
    <url>http://www.salute.gov.it/portale/nuovocoronavirus/homeNuovoCoronavirus.jsp?lingua=english</url>
    <description>Data of Italian Ministry of Health</description>
  </link>
  <link>
    <url>https://www.iss.it/en/coronavirus</url>
    <description>Data of &quot;Istituto Superiore di Sanità&quot;</description>
  </link>
  <results_reference>
    <citation>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.</citation>
    <PMID>32053470</PMID>
  </results_reference>
  <results_reference>
    <citation>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. Erratum In: JAMA Intern Med. 2020 Jul 1;180(7):1031.</citation>
    <PMID>32167524</PMID>
  </results_reference>
  <results_reference>
    <citation>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.</citation>
    <PMID>32162702</PMID>
  </results_reference>
  <results_reference>
    <citation>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.</citation>
    <PMID>32252338</PMID>
  </results_reference>
  <results_reference>
    <citation>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.</citation>
    <PMID>31756987</PMID>
  </results_reference>
  <results_reference>
    <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.</citation>
    <PMID>31168572</PMID>
  </results_reference>
  <results_reference>
    <citation>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.</citation>
    <PMID>29543693</PMID>
  </results_reference>
  <results_reference>
    <citation>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.</citation>
    <PMID>3558716</PMID>
  </results_reference>
  <verification_date>April 2022</verification_date>
  <study_first_submitted>June 18, 2020</study_first_submitted>
  <study_first_submitted_qc>June 18, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 22, 2020</study_first_posted>
  <last_update_submitted>March 2, 2023</last_update_submitted>
  <last_update_submitted_qc>March 2, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">March 3, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>COVID</keyword>
  <keyword>Lung cancer screening</keyword>
  <keyword>Subsolid pulmonary nodules</keyword>
  <keyword>Low dose CT screening</keyword>
  <keyword>Blood biomarkers</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04441814
org study id: INT 49/20
nct id: NCT04441814
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
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.
observational model: Cohort
time perspective: Retrospective
measure: Temporal variations in the frequency and morphology of SSNs according to the COVID-19 epidemic
time frame: 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
time frame: 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)
measure: Correlation between sub-solid lesions and individual risk
time frame: September 2019 - February 2020
description: Establish whether subjects with a higher level of risk and / or individual damage have a higher frequency and / or severity of sub-solid lung injury.
For this purpose, the following parameters will be analyzed: a) inflammatory profile (PCR), b) respiratory capacity (FEV1), c) co-morbidity (e.g. obesity, diabetes, cardio-vascular disease, COPD), d) level of CO; e) age, f) gender.
measure: Correlation between sub-solid lesions and incidence of acute events
time frame: September 2019 - August 2020
description: Establish whether subjects with greater frequency and/or severity of sub-solid lung lesions, associated or not with other individual risk factors, have a higher incidence of acute pathological events, in particular of respiratory nature, in the three (six) months following the CT exam. For this purpose, information on the time of onset and duration of these events will be collected: fever, cough, dyspnoea, hospitalization, positivity for covid-19, treatment in the ICU, and eventual death.
arm group label: Lung cancer screening subjects
description: Subjects enrolled in SMILE lung cancer screening trial
Blood samples to evaluate inflammatory markers
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 50 Years
maximum age: 75 Years
healthy volunteers: Accepts Healthy Volunteers
last name: Ugo Pastorino, MD
role: Principal Investigator
affiliation: Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
facility: 
country: Italy
url: http://www.salute.gov.it/portale/nuovocoronavirus/homeNuovoCoronavirus.jsp?lingua=english
description: Data of Italian Ministry of Health
url: https://www.iss.it/en/coronavirus
description: Data of "Istituto Superiore di Sanità"
citation: 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.
PMID: 32053470
citation: 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. Erratum In: JAMA Intern Med. 2020 Jul 1;180(7):1031.
PMID: 32167524
citation: 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.
PMID: 32162702
citation: 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.
PMID: 32252338
citation: 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.
PMID: 31756987
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.
PMID: 31168572
citation: 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.
PMID: 29543693
citation: 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.
PMID: 3558716
responsible party type: Sponsor
mesh term: COVID-19
sharing ipd: Undecided