LIBRA: Lung Nodule Imaging Biobank for Radiomics and AI Research

• Sponsor: Royal Marsden NHS Foundation Trust (Other)
• Overall Status: Recruiting
• CT.gov ID: NCT04270799
• Collaborator: RM Partners West London Cancer Alliance (Other), Royal Brompton & Harefield NHS Foundation Trust (Other), University College London Hospitals (Other), Imperial College Healthcare NHS Trust (Other), Lewisham and Greenwich NHS Trust (Other), King's College Hospital NHS Trust (Other), Epsom and St Helier University Hospitals NHS Trust (Other), Institute of Cancer Research, United Kingdom (Other), Guy's and St Thomas' NHS Foundation Trust (Other), UCLH Biomedical Research Centre (Other)
• Enrollment: 1,000
• Locations: 5
• Anticipated Duration (Months): 14
• Patients Per Site: 200
• Patients Per Site Per Month: 14.3

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Lung Cancer; Pulmonary Nodule, Multiple; Pulmonary Nodule, Solitary; Lung Neoplasms	Diagnostic Test: Machine Learning Classification

Study Design

• Study Type: Observational
• Anticipated Enrollment: 1000 participants
• Observational Model: Cohort
• Time Perspective: Retrospective
• Official Title: Lung Nodule Imaging Biobank for Radiomics and AI Research
• Actual Study Start Date: Jun 1, 2020
• Anticipated Primary Completion Date: Aug 1, 2021
• Anticipated Study Completion Date: Aug 1, 2021

Arms and Interventions

Arm	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.

Outcome Measures

Primary Outcome Measures

1. Development of an imaging biobank [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. [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.

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years and Older
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

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.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Royal Marsden - Surrey	Sutton	England	United Kingdom	SM2 5PT
2	Lewisham and Greenwich NHS Trust	London	Greater London	United Kingdom	SE6 4JH
3	Epsom and St Helier's Hospitals NHS Trust	Carshalton	Surrey	United Kingdom	SM5 1AA
4	University College London Hospitals NHS Foundation Trust	London		United Kingdom	NW1 2PG
5	The Royal Brompton NHS Foundation Trust	London		United Kingdom	SW3 6NP

Sponsors and Collaborators

• Royal Marsden NHS Foundation Trust
• RM Partners West London Cancer Alliance
• Royal Brompton & Harefield NHS Foundation Trust
• University College London Hospitals
• Imperial College Healthcare NHS Trust
• Lewisham and Greenwich NHS Trust
• King's College Hospital NHS Trust
• Epsom and St Helier University Hospitals NHS Trust
• Institute of Cancer Research, United Kingdom
• Guy's and St Thomas' NHS Foundation Trust
• UCLH Biomedical Research Centre

Investigators

• Study Chair: Richard Lee, MBBS PhD, The Royal Marsden Hospital

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Royal Marsden NHS Foundation Trust
• ClinicalTrials.gov Identifier: NCT04270799
• Other Study ID Numbers: CCR5215
• First Posted: Feb 17, 2020
• Last Update Posted: Jun 11, 2021
• Last Verified: Jun 1, 2021

• Individual Participant Data (IPD) Sharing Statement: Undecided
• Plan to Share IPD: Undecided
• 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: Lung Neoplasms; Multiple Pulmonary Nodules; Solitary Pulmonary Nodule

Study Results

No Results Posted as of Jun 11, 2021