Lung Nodule Imaging Biobank for Radiomics and AI Research

Lung Cancer Clinical Trial

— LIBRA  

Official title:

Lung Nodule Imaging Biobank for Radiomics and AI Research

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04270799
• Other study ID #: CCR5215
• Status: Recruiting
• Start date: June 1, 2020
• Est. completion date: August 2021

Study information

• Verified date: June 2021
• Source: Royal Marsden NHS Foundation Trust
• Contact: Richard Lee, MBBS PhD
• Phone: 020 7352 8171
• Email: richard.lee[@]rmh.nhs.uk
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: August 2021
• Est. primary completion date: August 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Lung Cancer
• Lung Neoplasms
• Multiple Pulmonary Nodules
• Pulmonary Nodule, Multiple
• Pulmonary Nodule, Solitary
• Solitary Pulmonary Nodule

Intervention

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.

Locations

Country	Name	City	State
United Kingdom	Epsom and St Helier's Hospitals NHS Trust	Carshalton	Surrey
United Kingdom	Lewisham and Greenwich NHS Trust	London	Greater London
United Kingdom	The Royal Brompton NHS Foundation Trust	London
United Kingdom	University College London Hospitals NHS Foundation Trust	London
United Kingdom	Royal Marsden - Surrey	Sutton	England

Sponsors (11)

Lead Sponsor

Collaborator

Royal Marsden NHS Foundation Trust

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

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Discovery of a CT-thorax based radiomics profile to predict cancer risk.	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.	1 year