Various imaging modalities are used in medical diagnosis such as MRI, CT and PET. The images are sometimes acquired at different times and in different body positions, and thus need to be aligned for precise diagnosis and treatment planning. Different image modalities provide complementary information about the anatomical structure under study. Image registration techniques enable multimodality images to be projected onto a common coordinate system, so that these images can be aligned and spatial correspondences can be established between the images.

This research project aims to investigate the information provided by functional PET and CT images about the tumour environment in lung cancer patients by registering functional PET and CT images with the pathology images acquired from the same patient. On identification of specific region of interest on the functional imaging the investigators will then be able to interrogate the tumour biology. In many cancers, the tumour environment is usually composed of a heterogeneous mass of tissue. The discrimination and classification of the carcinoma substructures is of paramount importance in the radiotherapy planning stage, as a given treatment may be more or less suitable depending on the local characteristics of the tumour. For instance, in hypoxic regions (areas inside the tumour with very low oxygen supply), radiotherapy performs poorly and strategies to intensify treatment to those areas could be investigated.

This project will develop a framework for automatic registration of pathology images, which are taken from a surgically extracted lung tumour, with the corresponding PET/CT scan acquired from the patient before surgery. The registration of these images is essential for the evaluation of the performance of different PET radiotracers.

Inclusion Criteria:

• Patient diagnosed with primary lung cancer and are due to undergo curative surgical resection of the tumour
• At least one of the tumour's major axes is larger than 30mm.
• The main volume of the tumour is located within the lung tissue.
• Tumour is identified primarily as single mass lesion.

Exclusion Criteria:

• Patient is under 18 years old.

There is no plan to share patient data.