Lung Nodule Imaging Biobank for Radiomics and AI Research

Royal Marsden NHS Foundation Trust1,000 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

1,000

Conditions

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

Interventions

Machine Learning ClassificationDIAGNOSTIC_TEST

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.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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.

Locations (5)

Royal Marsden - Surrey

Sutton, England, United Kingdom

RECRUITING

Lewisham and Greenwich NHS Trust

London, Greater London, United Kingdom

RECRUITING

Epsom and St Helier's Hospitals NHS Trust

Carshalton, Surrey, United Kingdom

Outcomes

Primary Outcomes

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.

Time frame: 1 year

Secondary Outcomes

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.

Time frame: 1 year

Central Contacts

Richard Lee, MBBS PhD

CONTACT

020 7352 8171 richard.lee@rmh.nhs.uk

Data from ClinicalTrials.gov

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