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IDEAL: Artificial Intelligence and Big Data for Early Lung Cancer Diagnosis Study (IDEAL)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03753724
Recruitment Status : Recruiting
First Posted : November 27, 2018
Last Update Posted : November 27, 2018
Sponsor:
Collaborator:
Optellum Ltd.
Information provided by (Responsible Party):
University of Oxford

Brief Summary:
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.

Condition or disease
Pulmonary Nodule, Solitary Pulmonary Nodule, Multiple Lung Cancer

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Study Type : Observational
Estimated Enrollment : 1050 participants
Observational Model: Other
Time Perspective: Prospective
Official Title: IDEAL: Artificial Intelligence and Big Data for Early Lung Cancer Diagnosis Prospective Study (Phase 2)
Actual Study Start Date : August 29, 2018
Estimated Primary Completion Date : September 1, 2019
Estimated Study Completion Date : September 1, 2020

Resource links provided by the National Library of Medicine


Group/Cohort
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).



Primary Outcome Measures :
  1. 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. ]
    Area Under the Receiver Operator Characteristic Curve (AUC).


Secondary Outcome Measures :
  1. The health economic benefits of the CAP model. [ Time Frame: At 2 weeks, 3 months (group 2 & 3 only) and year 1. ]
    Measured reduction in health care related costs using the CAP model algorithm compared to the current standard of care.

  2. 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. ]
    Measure of diagnostic performance of the risk model.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Sampling Method:   Non-Probability Sample
Study Population
Patients aged 18 years old or older with CT scans reported as having pulmonary nodule(s) of 5-15mm who meet the inclusion criteria and who can be placed into groups 1, 2 or 3.
Criteria

Inclusion Criteria:

  • Male or Female, aged 18 years or above
  • CT scans identified as having pulmonary nodule(s) of 5-15mm
  • Patients with solid or predominantly solid nodules referred to the pulmonary nodule clinic or for CT scan review by a specialist
  • CT scan section thickness of 3mm and less

Exclusion Criteria:

  • The CT scans are technically inadequate
  • Having received treatment for cancer in the last 5 years
  • Patient has more than five reported qualifying nodules

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03753724


Contacts
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Contact: Magda Laskawiec-Szkonter, MA 01865227612 ext 27612 magda.laskawiec@ouh.nhs.uk
Contact: Fergus Gleeson, Prof 01865235746 ext 35746 Fergus.Gleeson@ouh.nhs.uk

Locations
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United Kingdom
Royal Berkshire Nhs Foundation Trust Not yet recruiting
Reading, Berkshire, United Kingdom, RG1 5AN
Principal Investigator: Tara Barton, MD         
Nottingham University Hospitals Nhs Trust Recruiting
Nottingham, Nottinghamshire, United Kingdom, NG7 2UH
Principal Investigator: David Baldwin, Prof         
Oxford University Hospitals NHS Foundation Trust Recruiting
Oxford, Oxfordshire, United Kingdom, OX3 7LE
Contact: Fergus Gleeson, Prof    01865235746 ext 35746    fergus.gleeson@ouh.nhs.uk   
Contact: Magda Laskawiec-Szkonter, MA    01865227612    magda.laskawiec@ouh.nhs.uk   
Principal Investigator: Fergus Gleeson, Prof         
Leeds Teaching Hospitals Nhs Trust Not yet recruiting
Leeds, West Yorkshire, United Kingdom, LS9 7TF
Principal Investigator: Matthew Callister, MD         
Sponsors and Collaborators
University of Oxford
Optellum Ltd.
Investigators
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Principal Investigator: Fergus Gleeson, Prof University of Oxford/Oxford University Hospitals NHS Foundation Trust

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Responsible Party: University of Oxford
ClinicalTrials.gov Identifier: NCT03753724    
Other Study ID Numbers: 13489
First Posted: November 27, 2018    Key Record Dates
Last Update Posted: November 27, 2018
Last Verified: November 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided
Plan Description: To be confirmed.

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by University of Oxford:
pulmonary nodules
lung cancer
artificial intelligence
CAP model
Additional relevant MeSH terms:
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Lung Neoplasms
Multiple Pulmonary Nodules
Solitary Pulmonary Nodule
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site
Neoplasms
Lung Diseases
Respiratory Tract Diseases