Clinical trial NCT05368298: SCOOT: Sample Collection for DART
Official title: Sample Collection for The Integration and Analysis of Data Using Artificial Intelligence to Improve Patient Outcomes With Thoracic Diseases
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Lung Cancer
Keywords: screening
lung health check
artificial intelligence
algorithm
diagnosis
detection
Lead sponsor: University of Oxford
Sponsor class: OTHER
Collaborators: name: Innovate UK
name: Optellum
name: National Institute for Health Research, United Kingdom
name: Cancer Research UK
name: GE Healthcare
name: Prenostics
name: Roche Diagnostics GmbH
First posted: 2022-05-10
Enrollment: 5000
Sex: ALL
Minimum age (years): 55
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who have been offered follow up appointments after their Lung Health Check. Patients may be invited to attend a Lung Health Check if they have ever smoked and are aged 55 - 75 years old.

Brief summary: The study will use a blood sample collected from participants to:

* Develop new ways of finding and diagnosing lung health problems, such as lung cancer.
* Develop tools which make it easier to screen people with possible lung health problems, diagnose problems earlier and with fewer tests, and start the best treatment faster.
* Help improve the early diagnosis of lung cancer, as finding lung cancer early means that it can be treated more easily and successfully.

Detailed description: The results from this study will be linked with the data from the DART study (also collecting data through the Lung Health Check programme) to develop new ways of using computer technology (artificial intelligence) to improve lung health care. The studies use computer programs (called 'algorithms') which can be trained to analyse medical samples. Once developed, these algorithms can be used to support doctors by increasing their speed and accuracy of diagnosing issues.

Eligibility criteria: Patient suitability will be assessed against the below criteria by the clinical teams managing the patients.

Inclusion Criteria:

1. Patients with a pulmonary nodule or nodule(s) detected on a CT scan performed as part of Lung Cancer Screening from the Lung Health Check centres, that require further investigation with a PET-CT scan, and / or biopsy, and / or resection
2. Willing and able to give informed consent

Exclusion Criteria:

* None -

Primary outcomes:
measure: To develop an algorithm that gives a greater than chance improved ability to diagnose lung cancer using the blood biomarkers with or without the AI CT algorithm compared to not using them
timeFrame: by February 2025
Secondary outcomes:
measure: To develop an algorithm that gives a greater than chance improved ability to diagnose lung cancer using the blood biomarkers with or without the AI CT and blood markers algorithm compared to not using them
timeFrame: by February 2025
Other outcomes:
Locations:
city: Brighton
country: United Kingdom
facility: Lung Health Check Centre: Royal Sussex
city: Bristol
country: United Kingdom
facility: Lung Health Check Centre: North Bristol
city: Gateshead
country: United Kingdom
facility: Lung Health Check Centre: Gateshead
city: Kettering
country: United Kingdom
facility: Lung Health Check Centre: Corby & Kettering
city: Liverpool
country: United Kingdom
facility: Lung Health Check Centre: Cheshire & Merseyside
city: London
country: United Kingdom
facility: Lung Health Check Centre: RM Partners
city: Newcastle
country: United Kingdom
facility: Lung Health Check Centre: Newcastle & Gateshead
city: Plymouth
country: United Kingdom
facility: Lung Health Check Centre: Plymouth
city: Stoke
country: United Kingdom
facility: Lung Health Check Centre: Stoke
city: Sunderland
country: United Kingdom
facility: Lung Health Check Centre: South Tyneside and Sunderland
Study officials:
affiliation: Royal Marsden NHS Foundation Trust
name: Richard Lee
role: PRINCIPAL_INVESTIGATOR
References: