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Lung Nodule Imaging Biobank for Radiomics and AI Research (LIBRA)

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: NCT04270799
Recruitment Status : Not yet recruiting
First Posted : February 17, 2020
Last Update Posted : February 17, 2020
Sponsor:
Collaborators:
Royal Marsden Partners
The Royal Brompton and Harefield NHS Foundation Trust
University College London Hospitals
Guys and St Thomas' NHS Foundation Trust
Imperial College NHS Foundation Trust
Lewisham and Greenwich NHS Trust
King's College Hospital NHS Trust
Epsom and St Helier University Hospitals NHS Trust
The Institute of Cancer Research, London
Biomedical Research Centre, London
Information provided by (Responsible Party):
Royal Marsden NHS Foundation Trust

Brief Summary:
This study will collect retrospective CT scan images and clinical data from patients with incidental lung nodules seen in hospitals across London. We will research whether we can use machine learning to predict which patients will develop lung cancer, to improve early diagnosis.

Condition or disease Intervention/treatment
Lung Cancer Pulmonary Nodule, Multiple Pulmonary Nodule, Solitary Lung Neoplasms Diagnostic Test: Machine Learning Classification

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Study Type : Observational
Estimated Enrollment : 1000 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Lung Nodule Imaging Biobank for Radiomics and AI Research
Estimated Study Start Date : April 2020
Estimated Primary Completion Date : August 2021
Estimated Study Completion Date : August 2021

Resource links provided by the National Library of Medicine


Group/Cohort Intervention/treatment
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
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.




Primary Outcome Measures :
  1. Development of an imaging biobank [ Time Frame: 1 year ]
    The primary endpoint will be met if we are able to store baseline CT scans and the minimum clinical data set for 1000 patients.


Secondary Outcome Measures :
  1. Discovery of a CT-thorax based radiomics profile to predict cancer risk. [ Time Frame: 1 year ]
    We aim to identify distinct clusters of radiomics variables to generate a radiomics predictive vector (RPV), which can be used to stratify patients according to malignancy risk. This vector will be used in multivariate analysis and compared to existing risk models.


Biospecimen Retention:   Samples Without DNA
CT scan images.


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
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Patients with previously identified lung nodules.
Criteria

Inclusion Criteria:

  • 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.

Exclusion Criteria:

  • • 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.

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): NCT04270799


Contacts
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Contact: Richard Lee, MBBS PhD 020 7352 8171 richard.lee@rmh.nhs.uk

Sponsors and Collaborators
Royal Marsden NHS Foundation Trust
Royal Marsden Partners
The Royal Brompton and Harefield NHS Foundation Trust
University College London Hospitals
Guys and St Thomas' NHS Foundation Trust
Imperial College NHS Foundation Trust
Lewisham and Greenwich NHS Trust
King's College Hospital NHS Trust
Epsom and St Helier University Hospitals NHS Trust
The Institute of Cancer Research, London
Biomedical Research Centre, London
Investigators
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Study Chair: Richard Lee, MBBS PhD The Royal Marsden Hospital
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Responsible Party: Royal Marsden NHS Foundation Trust
ClinicalTrials.gov Identifier: NCT04270799    
Other Study ID Numbers: CCR5215
First Posted: February 17, 2020    Key Record Dates
Last Update Posted: February 17, 2020
Last Verified: February 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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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 Royal Marsden NHS Foundation Trust:
Incidental lung nodules
Radiomics
Artificial Intelligence
Machine Learning
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