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.

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.

The primary endpoint will be met if we are able to store baseline CT scans and the minimum clinical data set for 1000 patients.

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.