Lung cancer is the third most frequently diagnosed cancer type in the UK and is the most common cause of cancer death. Radiotherapy is a standard treatment but can cause damage to the lungs (Radiotherapy Induced Lung Damage, RILD) and other side effects. Historically, the poor survival rate of the lung cancer patients has meant that while acute-RILD (pneumonitis) has been widely studied, chronic-RILD (fibrosis) has received far less attention. Recent trials of novel radiotherapy treatment regimens have reported improved local control and longer survival times. Consequently, there is growing interest in better characterising RILD and understanding the relationship and progression between acute- and chronic-RILD. This can ultimately help to optimise radiotherapy plans and improve lung cancer patient’s quality of life.
During the course of this project, the participants will have the opportunity to investigate methods dedicated for lung image analysis. In particular this includes the analysis of the changes to the lungs of patients who underwent radiotherapy treatment. They will apply these methods to Computed Tomography (CT) images acquired during 24 months of follow up. With the use of the dedicated set of imaging RILD biomarkers, which objectively measure changes to the anatomy and shape of the lungs, the participant will assess them and track their evolution across different time points. After the project the participants will be familiar with tools applicable to lung, airway and vessel segmentation. They will learn how these segmentations can be further used for the extraction of the RILD biomarkers. They will explore different paths of the RILD biomarkers changes observed for individual patients and compare the results. The lung image analysis methods will be demonstrated on the example of lung cancer patients, however their potential application extends to other lung diseases, in particular to idiopathic pulmonary fibrosis (IPF). For IPF patients the analysis of changes in airways and vessels can play the crucial role in better understanding of the disease.