Lung Nodule Imaging Biobank for Radiomics and AI Research

Study Overview
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Brief Summary
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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.

Official Title
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Lung Nodule Imaging Biobank for Radiomics and AI Research

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

Intervention / Treatment
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* Diagnostic Test: Machine Learning Classification


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Lung Nodules<br>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<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Radiomics, Incidental lung nodules, Artificial Intelligence, Machine Learning