Lung Nodule Imaging Biobank for Radiomics and AI Research (LIBRA)
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|ClinicalTrials.gov Identifier: NCT04270799|
Recruitment Status : Not yet recruiting
First Posted : February 17, 2020
Last Update Posted : February 17, 2020
|Condition or disease||Intervention/treatment|
|Lung Cancer Pulmonary Nodule, Multiple Pulmonary Nodule, Solitary Lung Neoplasms||Diagnostic Test: Machine Learning Classification|
|Study Type :||Observational|
|Estimated Enrollment :||1000 participants|
|Official Title:||Lung Nodule Imaging Biobank for Radiomics and AI Research|
|Estimated Study Start Date :||April 2020|
|Estimated Primary Completion Date :||August 2021|
|Estimated Study Completion Date :||August 2021|
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
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.
- Development of an imaging biobank [ Time Frame: 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.
- Discovery of a CT-thorax based radiomics profile to predict cancer risk. [ Time Frame: 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.
Biospecimen Retention: Samples Without DNA
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04270799
|Contact: Richard Lee, MBBS PhD||020 7352 email@example.com|
|Study Chair:||Richard Lee, MBBS PhD||The Royal Marsden Hospital|