Application of Texture Analysis to Identify Pprognostic Biomarkers for the Optimisatin of Radiotherapy Treatments

EPSRC Portfolio: Healthcare Technologies Theme, specifically Clinical Technologies and Medical Imaging research, both 'Maintain' research areas.

Finding the right treatment for the right patient is essential to improve on the delivery of high-quality, personalised radiation therapy. Medical imaging has a fundamental role in informing diagnosis and treatment and it is a field of rapid development with the introduction of new agents that can picture, mong others, functional micro environmental factors such as metabolic activity, tumour cell proliferation, vascular structure and oxygenation status. Imaging can provide a comprehensive, personalised, non invasive view of tumour characteristics and can be used to monitor tumour progression and response to therapy. Computed Tomography (CT) is an anatomical imaging modality which is widely used in radiotherapy. CT is regularly used to assess the tumour shape and tissue densities and has a key role in the preparation of a radiotherapy plan. Every cancer patient is likely to receive multiple CT scans before, during and after radiotherapy. However, the information provided by this large amount of imaging data, regularly collected and available for each patient, is currently not used at its full potential.
Recent research has shown that computer algorithms and advanced image processing techniques, can be used to extract texture imaging features such as tumour heterogeneity, and entropy that can be associated with morphologic tumour response and overall survival. Indeed, these new image feature extraction techniques offer the opportunity to mine the large amount of image data already collected as standard of care for cancer patients and analyse them in an entire new way, providing more data that can be used to identify prognostic biomarkers for the optimisation and personalisation of radiotherapy treatments.
The main objective of this project is to investigate the feasibility of optimising definitive radiotherapy treatments through the identification of prognostic biomarkers that can can be associated with treatment outcome. The research will develop and implement methods to extract imaging features from oesophageal, head & neck and bladder cancer patients using imaging data acquired throughout the course of treatments carried out at our Centre. The availability of repeated on-treatment imaging scans will allow us to quantify and assess how image characteristics change during radiotherapy delivery, and confirm if this information can be used for individual treatment adaptation.

The project will develop a platform for (a) the automated and reproducible extraction of image-based features from all CT scans acquired for patients undergoing radiotherapy treatment of oesophageal, head and neck cancer and bladder cancer at Velindre (b) the quantitative assessment of the extracted tumour characteristics and (c) the identification of predictive biomarkers through correlation of the image descriptors with clinical outcomes. A workflow will be developed and software will be written to extract imaging data from the radiotherapy Picture Archive and Communication System. Analysis will be carried out enhancing existing open source programs actively developed by our research team.

We expect the technology and methodology developed for this project to be applied to other imaging modalities such as Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) increasing its predictive power. Furthermore, the model could be extended to other tumour types such as lung and anal cancer, and to diagnostic and planning data from other radiotherapy centres in Wales and beyond. The research team has indeed the right national and international links to make this happen. There is a huge opportunity for the findings of this research to be translated into clinical practice and that a further research grant application is made to main Research Councils before the end of the project.