Lung Nodule Imaging Biobank for Radiomics and AI Research

• Conditions: Lung Cancer; Pulmonary Nodule, Solitary; Lung Neoplasms; Pulmonary Nodule, Multiple
• Interventions: Diagnostic Test: Machine Learning Classification

• Registration Number: NCT04270799
• Lead Sponsor: Royal Marsden NHS Foundation Trust

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2020-02-12
• Study First Submitted QC: 2020-02-12
• Study First Posted: 2020-02-17
• Study Start: 2020-06-01
• Status Verified: 2021-06
• Last Update Submitted: 2021-06-08
• Last Update Posted: 2021-06-11
• Primary Completion: 2021-08
• Study Completion: 2021-08

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 1000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Lung Nodules	Machine Learning Classification	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.

Primary Outcome Measures

Name	Time	Method
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

Name	Time	Method
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.

Trial Locations

Locations (5)

University College London Hospitals NHS Foundation Trust; 🇬🇧 London, United Kingdom

The Royal Brompton NHS Foundation Trust; 🇬🇧 London, United Kingdom

Royal Marsden - Surrey; 🇬🇧 Sutton, England, United Kingdom

Lewisham and Greenwich NHS Trust; 🇬🇧 London, Greater London, United Kingdom

Epsom and St Helier's Hospitals NHS Trust; 🇬🇧 Carshalton, Surrey, United Kingdom