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
|Condition or disease|
|Pulmonary Nodule, Solitary Pulmonary Nodule, Multiple Lung Cancer|
|Study Type :||Observational|
|Estimated Enrollment :||1050 participants|
|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|
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).
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).
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).
- 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).
- 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.
- 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.
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
|Contact: Magda Laskawiec-Szkonter, MA||01865227612 ext firstname.lastname@example.org|
|Contact: Fergus Gleeson, Prof||01865235746 ext 35746||Fergus.Gleeson@ouh.nhs.uk|
|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 email@example.com|
|Contact: Magda Laskawiec-Szkonter, MA 01865227612 firstname.lastname@example.org|
|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|
|Principal Investigator:||Fergus Gleeson, Prof||University of Oxford/Oxford University Hospitals NHS Foundation Trust|