<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/NCT03753724</url>
  </required_header>
  <id_info>
    <org_study_id>13489</org_study_id>
    <nct_id>NCT03753724</nct_id>
  </id_info>
  <brief_title>IDEAL: Artificial Intelligence and Big Data for Early Lung Cancer Diagnosis Study</brief_title>
  <acronym>IDEAL</acronym>
  <official_title>IDEAL: Artificial Intelligence and Big Data for Early Lung Cancer Diagnosis Prospective Study (Phase 2)</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Oxford</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Optellum Ltd.</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>University of Oxford</source>
  <oversight_info>
    <has_dmc>No</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>
      This study aims to test the use of novel CT image analysis techniques to enable a better&#xD;
      characterisation of small pulmonary nodules. The study will incorporate solid and&#xD;
      predominantly solid nodules of 5-15 mm scanned using a variety of scanner types, imaging&#xD;
      protocols and patient populations. The investigators hope that the new image processing&#xD;
      techniques will improve the accuracy of lung nodule analysis which will in turn reduce the&#xD;
      number of unnecessary investigations for benign nodules and may increase the accuracy of the&#xD;
      early diagnosis of lung cancer in malignant nodules. This study aims to test this novel&#xD;
      analysis software to subsequently allow validation.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      As the commonest cancer with 1.8 million cases diagnosed each year worldwide, early diagnosis&#xD;
      of lung cancer is important to reduce mortality. The early diagnosis of lung cancer is&#xD;
      contingent on both the detection of a small lung nodule and determining whether it is&#xD;
      malignant. Whilst computed tomography (CT) has proven to be a robust way of detecting lung&#xD;
      nodules, they are often discovered on routine scanning as an incidental finding or as part of&#xD;
      a lung cancer screening program. Hence, determining whether they are benign or malignant is&#xD;
      challenging.&#xD;
&#xD;
      Up to 75% of smokers scanned either as part of their clinical care or in lung cancer&#xD;
      screening trials have subcentimetre pulmonary nodules detected. This places a substantial&#xD;
      burden on scanning facilities, staff and patients. Current methods of determining if lung&#xD;
      nodules are benign or malignant are not standardised and unproven. The US National Lung&#xD;
      Screening Trial (NLST) showed that up to 95% of lung nodules detected on CT scans of the&#xD;
      chest were false positives i.e. they were not malignant. Detection of non-malignant nodules&#xD;
      have the unwanted consequences of unnecessary cost as they require follow-up scanning or&#xD;
      alternative methods of investigation, cause patient anxiety, may result in increased&#xD;
      morbidity potentially by biopsy or resection, and result in increased patient radiation&#xD;
      exposure due to follow-up CT scans or from PET-CT scans.&#xD;
&#xD;
      As part of standard care at present, patients with lung nodules greater than 4mm and&#xD;
      sub-centimetre are followed up with CT scan(s), up to 5 scans, for up to 24 months, according&#xD;
      to the internationally accepted Fleischner guidelines. Additional investigations, such as a&#xD;
      positron-emission tomography (PET-CT) scan and biopsy or resection may also be performed&#xD;
      based on the size and clinical risk profile of the patient.&#xD;
&#xD;
      Recent studies have shown that incorporating lung nodule characteristics such as size,&#xD;
      texture, growth rate, contrast enhancement can improve the accuracy of predicting the risk of&#xD;
      malignancy. This allows the stratification of lung nodules into different investigations&#xD;
      and/or follow-up pathways based on the predicted risk of malignancy.&#xD;
&#xD;
      This study aims to test the use of novel CT image analysis techniques incorporated into a&#xD;
      clinical risk model to characterise small pulmonary nodules. The study will incorporate solid&#xD;
      and predominantly solid nodules of 5-15mm scanned using a variety of scanner types, imaging&#xD;
      protocols and patient populations.&#xD;
&#xD;
      CT scans from patients referred to the Lung Nodule Clinic or for review by a specialist in&#xD;
      nodule assessment as per current clinical practice will be reviewed. Patients who meet the&#xD;
      inclusion/exclusion criteria will be contacted by a member of the clinical care team and&#xD;
      study participation will be discussed.&#xD;
&#xD;
      Of the nodules detected, their management will be categorised as per standard care into the&#xD;
      following groups:&#xD;
&#xD;
      GROUP 1. On review of the scans by an expert, no further follow up or investigation is&#xD;
      required, as the nodule(s) has been categorised as benign. As the patient was unaware the&#xD;
      scan was being reviewed and no further investigations are required. Patient is invited to&#xD;
      take part in the study (by telephone).&#xD;
&#xD;
      GROUP 2. On review, the nodule is indeterminate and further scanning at a later date is&#xD;
      required. The patient is informed of this, usually via a telephone call from either the&#xD;
      doctor or nurse specialist working in the Lung Nodule Clinic (LNC) or site equivalent. This&#xD;
      LNC is usually a virtual clinic - no physical interaction with the patient - and the follow&#xD;
      up scan is reviewed and the patient contacted again via the virtual clinic. Patient is&#xD;
      invited to take part in the study (by telephone, by post or in clinic if appropriate).&#xD;
&#xD;
      GROUP 3. On review, the nodule is regarded as potentially malignant and further scans and a&#xD;
      clinic appointment is made for the patient. Patient is invited to take part in the study (in&#xD;
      clinic if appropriate).&#xD;
&#xD;
      Apart from CT images routine care clinical information about the patient (e.g. age, sex,&#xD;
      smoking status, family history of lung cancer) and data on nodule characteristics derived&#xD;
      from the identified CT scan (and/or prior and subsequent) will be collected and used to test&#xD;
      the new software program that will stratify pulmonary nodules in terms of their probability&#xD;
      of being malignant or benign. All this data is routine and is collected as part of standard&#xD;
      care therefore no study visits will need to be arranged.&#xD;
&#xD;
      As part of the health economics assessment patients will be invited to complete the EQ-5D-5L&#xD;
      (quality of life), GAD-7 (anxiety), and productivity loss questionnaires. Patients will also&#xD;
      be asked questions about their health utilisation. This data may also be collected from&#xD;
      medical notes/electronic patient records.&#xD;
&#xD;
      The initial questionnaires will be completed within 2 weeks (if possible) of having the first&#xD;
      scan and a follow-up will be carried out after 1 year for group 1 participants. Group 2 and 3&#xD;
      participants will also be invited to complete these questionnaires 3 months after their&#xD;
      initial scan as they will have undergone further investigations during that time. The&#xD;
      questionnaires will be completed over the phone for patients in groups 1 and over the phone&#xD;
      or face to face when attending routine clinic appointments for groups 2 and 3.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">August 29, 2018</start_date>
  <completion_date type="Actual">March 31, 2022</completion_date>
  <primary_completion_date type="Actual">March 31, 2022</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>The overall diagnostic performance of a new computer aided prediction (CAP) model for malignancy in small pulmonary nodules (% diagnostic accuracy).</measure>
    <time_frame>Up to 1 year.</time_frame>
    <description>Area Under the Receiver Operator Characteristic Curve (AUC).</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>The health economic benefits of the CAP model.</measure>
    <time_frame>At 2 weeks, 3 months (group 2 &amp; 3 only) and year 1.</time_frame>
    <description>Measured reduction in health care related costs using the CAP model algorithm compared to the current standard of care.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The diagnostic performance of the CAP model for malignancy in small pulmonary nodules at a specific operating point relevant to clinical practice.</measure>
    <time_frame>Up to 1 year.</time_frame>
    <description>Measure of diagnostic performance of the risk model.</description>
  </secondary_outcome>
  <number_of_groups>3</number_of_groups>
  <enrollment type="Actual">1293</enrollment>
  <condition>Pulmonary Nodule, Solitary</condition>
  <condition>Pulmonary Nodule, Multiple</condition>
  <condition>Lung Cancer</condition>
  <arm_group>
    <arm_group_label>Group 1</arm_group_label>
    <description>On review of the scans by an expert, no further follow up or investigation is required, as the nodule(s) has been categorised as benign. As the patient was unaware the scan was being reviewed and no further investigations are required. Patient is invited to take part in the study (by telephone).</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Group 2</arm_group_label>
    <description>On review, the nodule is indeterminate and further scanning at a later date is required. The patient is informed of this, usually via a telephone call from either the doctor or nurse specialist working in the Lung Nodule Clinic (LNC) or site equivalent. This LNC is usually a virtual clinic - no physical interaction with the patient - and the follow up scan is reviewed and the patient contacted again via the virtual clinic. Patient is invited to take part in the study (by telephone, by post or in clinic if appropriate).</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Group 3</arm_group_label>
    <description>On review, the nodule is regarded as potentially malignant and further scans and a clinic appointment is made for the patient. Patient is invited to take part in the study (in clinic if appropriate).</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        Patients aged 18 years old or older with CT scans reported as having pulmonary nodule(s) of&#xD;
        5-15mm who meet the inclusion criteria and who can be placed into groups 1, 2 or 3.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Male or Female, aged 18 years or above&#xD;
&#xD;
          -  CT scans identified as having pulmonary nodule(s) of 5-15mm&#xD;
&#xD;
          -  Patients with solid or predominantly solid nodules referred to the pulmonary nodule&#xD;
             clinic or for CT scan review by a specialist&#xD;
&#xD;
          -  CT scan section thickness of 3mm and less&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  The CT scans are technically inadequate&#xD;
&#xD;
          -  Having received treatment for cancer in the last 5 years&#xD;
&#xD;
          -  Patient has more than five reported qualifying nodules&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <overall_official>
    <last_name>Fergus Gleeson, Prof</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Oxford/Oxford University Hospitals NHS Foundation Trust</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Royal Berkshire Nhs Foundation Trust</name>
      <address>
        <city>Reading</city>
        <state>Berkshire</state>
        <zip>RG1 5AN</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Nottingham University Hospitals Nhs Trust</name>
      <address>
        <city>Nottingham</city>
        <state>Nottinghamshire</state>
        <zip>NG7 2UH</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Oxford University Hospitals NHS Foundation Trust</name>
      <address>
        <city>Oxford</city>
        <state>Oxfordshire</state>
        <zip>OX3 7LE</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Leeds Teaching Hospitals Nhs Trust</name>
      <address>
        <city>Leeds</city>
        <state>West Yorkshire</state>
        <zip>LS9 7TF</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United Kingdom</country>
  </location_countries>
  <verification_date>July 2022</verification_date>
  <study_first_submitted>August 15, 2018</study_first_submitted>
  <study_first_submitted_qc>November 22, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">November 27, 2018</study_first_posted>
  <last_update_submitted>July 28, 2022</last_update_submitted>
  <last_update_submitted_qc>July 28, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">July 29, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>pulmonary nodules</keyword>
  <keyword>lung cancer</keyword>
  <keyword>artificial intelligence</keyword>
  <keyword>CAP model</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Lung Neoplasms</mesh_term>
    <mesh_term>Multiple Pulmonary Nodules</mesh_term>
    <mesh_term>Solitary Pulmonary Nodule</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
    <ipd_description>To be confirmed.</ipd_description>
  </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/NCT03753724
org study id: 13489
nct id: NCT03753724
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
This study aims to test the use of novel CT image analysis techniques to enable a better
      characterisation of small pulmonary nodules. The study will incorporate solid and
      predominantly solid nodules of 5-15 mm scanned using a variety of scanner types, imaging
      protocols and patient populations. The investigators hope that the new image processing
      techniques will improve the accuracy of lung nodule analysis which will in turn reduce the
      number of unnecessary investigations for benign nodules and may increase the accuracy of the
      early diagnosis of lung cancer in malignant nodules. This study aims to test this novel
      analysis software to subsequently allow validation.
As the commonest cancer with 1.8 million cases diagnosed each year worldwide, early diagnosis
      of lung cancer is important to reduce mortality. The early diagnosis of lung cancer is
      contingent on both the detection of a small lung nodule and determining whether it is
      malignant. Whilst computed tomography (CT) has proven to be a robust way of detecting lung
      nodules, they are often discovered on routine scanning as an incidental finding or as part of
      a lung cancer screening program. Hence, determining whether they are benign or malignant is
      challenging.

      Up to 75% of smokers scanned either as part of their clinical care or in lung cancer
      screening trials have subcentimetre pulmonary nodules detected. This places a substantial
      burden on scanning facilities, staff and patients. Current methods of determining if lung
      nodules are benign or malignant are not standardised and unproven. The US National Lung
      Screening Trial (NLST) showed that up to 95% of lung nodules detected on CT scans of the
      chest were false positives i.e. they were not malignant. Detection of non-malignant nodules
      have the unwanted consequences of unnecessary cost as they require follow-up scanning or
      alternative methods of investigation, cause patient anxiety, may result in increased
      morbidity potentially by biopsy or resection, and result in increased patient radiation
      exposure due to follow-up CT scans or from PET-CT scans.

      As part of standard care at present, patients with lung nodules greater than 4mm and
      sub-centimetre are followed up with CT scan(s), up to 5 scans, for up to 24 months, according
      to the internationally accepted Fleischner guidelines. Additional investigations, such as a
      positron-emission tomography (PET-CT) scan and biopsy or resection may also be performed
      based on the size and clinical risk profile of the patient.

      Recent studies have shown that incorporating lung nodule characteristics such as size,
      texture, growth rate, contrast enhancement can improve the accuracy of predicting the risk of
      malignancy. This allows the stratification of lung nodules into different investigations
      and/or follow-up pathways based on the predicted risk of malignancy.

      This study aims to test the use of novel CT image analysis techniques incorporated into a
      clinical risk model to characterise small pulmonary nodules. The study will incorporate solid
      and predominantly solid nodules of 5-15mm scanned using a variety of scanner types, imaging
      protocols and patient populations.

      CT scans from patients referred to the Lung Nodule Clinic or for review by a specialist in
      nodule assessment as per current clinical practice will be reviewed. Patients who meet the
      inclusion/exclusion criteria will be contacted by a member of the clinical care team and
      study participation will be discussed.

      Of the nodules detected, their management will be categorised as per standard care into the
      following groups:

      GROUP 1. On review of the scans by an expert, no further follow up or investigation is
      required, as the nodule(s) has been categorised as benign. As the patient was unaware the
      scan was being reviewed and no further investigations are required. Patient is invited to
      take part in the study (by telephone).

      GROUP 2. On review, the nodule is indeterminate and further scanning at a later date is
      required. The patient is informed of this, usually via a telephone call from either the
      doctor or nurse specialist working in the Lung Nodule Clinic (LNC) or site equivalent. This
      LNC is usually a virtual clinic - no physical interaction with the patient - and the follow
      up scan is reviewed and the patient contacted again via the virtual clinic. Patient is
      invited to take part in the study (by telephone, by post or in clinic if appropriate).

      GROUP 3. On review, the nodule is regarded as potentially malignant and further scans and a
      clinic appointment is made for the patient. Patient is invited to take part in the study (in
      clinic if appropriate).

      Apart from CT images routine care clinical information about the patient (e.g. age, sex,
      smoking status, family history of lung cancer) and data on nodule characteristics derived
      from the identified CT scan (and/or prior and subsequent) will be collected and used to test
      the new software program that will stratify pulmonary nodules in terms of their probability
      of being malignant or benign. All this data is routine and is collected as part of standard
      care therefore no study visits will need to be arranged.

      As part of the health economics assessment patients will be invited to complete the EQ-5D-5L
      (quality of life), GAD-7 (anxiety), and productivity loss questionnaires. Patients will also
      be asked questions about their health utilisation. This data may also be collected from
      medical notes/electronic patient records.

      The initial questionnaires will be completed within 2 weeks (if possible) of having the first
      scan and a follow-up will be carried out after 1 year for group 1 participants. Group 2 and 3
      participants will also be invited to complete these questionnaires 3 months after their
      initial scan as they will have undergone further investigations during that time. The
      questionnaires will be completed over the phone for patients in groups 1 and over the phone
      or face to face when attending routine clinic appointments for groups 2 and 3.
observational model: Other
time perspective: Prospective
measure: The overall diagnostic performance of a new computer aided prediction (CAP) model for malignancy in small pulmonary nodules (% diagnostic accuracy).
time frame: Up to 1 year.
description: Area Under the Receiver Operator Characteristic Curve (AUC).
measure: The health economic benefits of the CAP model.
time frame: At 2 weeks, 3 months (group 2 & 3 only) and year 1.
description: Measured reduction in health care related costs using the CAP model algorithm compared to the current standard of care.
measure: The diagnostic performance of the CAP model for malignancy in small pulmonary nodules at a specific operating point relevant to clinical practice.
time frame: Up to 1 year.
description: Measure of diagnostic performance of the risk model.
arm group label: Group 1
description: On review of the scans by an expert, no further follow up or investigation is required, as the nodule(s) has been categorised as benign. As the patient was unaware the scan was being reviewed and no further investigations are required. Patient is invited to take part in the study (by telephone).
arm group label: Group 2
description: On review, the nodule is indeterminate and further scanning at a later date is required. The patient is informed of this, usually via a telephone call from either the doctor or nurse specialist working in the Lung Nodule Clinic (LNC) or site equivalent. This LNC is usually a virtual clinic - no physical interaction with the patient - and the follow up scan is reviewed and the patient contacted again via the virtual clinic. Patient is invited to take part in the study (by telephone, by post or in clinic if appropriate).
arm group label: Group 3
description: On review, the nodule is regarded as potentially malignant and further scans and a clinic appointment is made for the patient. Patient is invited to take part in the study (in clinic if appropriate).
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
last name: Fergus Gleeson, Prof
role: Principal Investigator
affiliation: University of Oxford/Oxford University Hospitals NHS Foundation Trust
facility: 
facility: 
facility: 
facility: 
country: United Kingdom
responsible party type: Sponsor
mesh term: Lung Neoplasms
mesh term: Multiple Pulmonary Nodules
mesh term: Solitary Pulmonary Nodule
sharing ipd: Undecided
ipd description: To be confirmed.