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NCT Number: NCT05368298

SCOOT: Sample Collection for DART

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

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Key information

Conditions

Lung Cancer Disease Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Pathologic Processes Pathological Conditions, Signs and Symptoms Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

55 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Lung Health Check: Oxford - Churchill Hospital, OUH, Oxford, Oxfordshire, United Kingdom

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Inclusion criteria

  • 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
  • Willing and able to give informed consent

Exclusion criteria

  • None -

Treatment and study plan

Primary outcomes

  1. 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

    Time frame: by 30Sep2027

Secondary outcomes

  1. 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

    Time frame: by 30Sep2027

Sponsors and collaborators

Lead sponsor

University of Oxford

Other

Collaborators

  • Cancer Research UK
  • GE Healthcare
  • GlaxoSmithKline
  • Innovate UK
  • National Institute for Health Research, United Kingdom
  • Optellum
  • Prenostics
  • Roche Diagnostics GmbH

Registry information

Official study title

Sample Collection for The Integration and Analysis of Data Using Artificial Intelligence to Improve Patient Outcomes With Thoracic Diseases

Acronym: SCOOT

Important dates

Study start
2022
Primary completion
2027
Study completion
2027
First posted
May 10, 2022
Registry last updated
May 22, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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