NCT04270799

Brief Summary

This study will collect retrospective CT scan images and clinical data from participants with incidental lung nodules seen in hospitals across London. The investigators will research whether machine learning can be used to predict which participants will develop lung cancer, to improve early diagnosis.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
1,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jun 2020

Geographic Reach
1 country

5 active sites

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

February 12, 2020

Completed
5 days until next milestone

First Posted

Study publicly available on registry

February 17, 2020

Completed
4 months until next milestone

Study Start

First participant enrolled

June 1, 2020

Completed
1.2 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

August 1, 2021

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

August 1, 2021

Completed
Last Updated

June 11, 2021

Status Verified

June 1, 2021

Enrollment Period

1.2 years

First QC Date

February 12, 2020

Last Update Submit

June 8, 2021

Conditions

Lung CancerPulmonary Nodule, MultiplePulmonary Nodule, SolitaryLung Neoplasms

Keywords

Incidental lung nodulesRadiomicsArtificial IntelligenceMachine Learning

Outcome Measures

Primary Outcomes (1)

  • Development of an imaging biobank

    The primary endpoint will be met if we are able to store baseline CT scans and the minimum clinical data set for 1000 patients.

    1 year

Secondary Outcomes (1)

  • Discovery of a CT-thorax based radiomics profile to predict cancer risk.

    1 year

Study Arms (1)

Lung Nodules

A cohort of 1000 patients with incidental lung nodules will be identified using clinical records at participating NHS sites. Link-anonymised CT scan images and data will be stored using a central database for radiomics and artificial intelligence research, to predict the risk of malignancy.

Diagnostic Test: Machine Learning Classification

Interventions

Machine Learning ClassificationDIAGNOSTIC_TEST

Patient's scans will be used as input into in-house software to extract multiple radiomics features. These features will be used to develop a risk-signature which can predict malignancy risk. Patient scans will also be used as input into deep learning/convolutional neural network models to perform automated imaging classification.

Lung Nodules

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients with previously identified lung nodules.

You may qualify if:

  • Age \> 18
  • Baseline CT thorax imaging reported as having pulmonary nodule(s) between 5 and 30mm in the last 10 years.
  • Ground truth known (either scan data showing stability for 2 years (based on diameter) or one year (based on volumetry), complete resolution, or biopsy-proven malignancy.
  • Slice thickness \< 2.5mm.

You may not qualify if:

  • Absence of at least one technically adequate CT thorax imaging series (defined by visual inspection of presence of imaging data of the thorax in the DICOM record).
  • Slice thickness \> 2.5mm.
  • Imaging \> 10 years old.
  • Ground truth unknown.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (5)

Royal Marsden - Surrey

Sutton, England, SM2 5PT, United Kingdom

RECRUITING

Lewisham and Greenwich NHS Trust

London, Greater London, SE6 4JH, United Kingdom

RECRUITING

Epsom and St Helier's Hospitals NHS Trust

Carshalton, Surrey, SM5 1AA, United Kingdom

NOT YET RECRUITING

University College London Hospitals NHS Foundation Trust

London, NW1 2PG, United Kingdom

RECRUITING

The Royal Brompton NHS Foundation Trust

London, SW3 6NP, United Kingdom

RECRUITING

Biospecimen

Retention: SAMPLES WITHOUT DNA

CT scan images.

MeSH Terms

Conditions

Lung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary Nodule

Condition Hierarchy (Ancestors)

Respiratory Tract NeoplasmsThoracic NeoplasmsNeoplasms by SiteNeoplasmsLung DiseasesRespiratory Tract Diseases

Study Officials

  • Richard Lee, MBBS PhD

    The Royal Marsden Hospital

    STUDY CHAIR

Central Study Contacts

Richard Lee, MBBS PhD

CONTACT

020 7352 8171richard.lee@rmh.nhs.uk

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

February 12, 2020

First Posted

February 17, 2020

Study Start

June 1, 2020

Primary Completion

August 1, 2021

Study Completion

August 1, 2021

Last Updated

June 11, 2021

Record last verified: 2021-06

Locations

GB(5)