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

Royal Marsden NHS Foundation Trust

Status: Unknown

Conditions: Pulmonary Nodule, Multiple; Lung Neoplasms; Pulmonary Nodule, Solitary; Lung Cancer

Treatments: Diagnostic Test: Machine Learning Classification

Study type: Observational

Funder types: Other

Identifiers

NCT04270799

CCR5215

Details and patient eligibility

About

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.

Enrollment: 1,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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.

Trial design

1,000 participants in 1 patient group

Lung Nodules

Description:

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.

Treatment:

Diagnostic Test: Machine Learning Classification