Personalised lung cancer treatment through outcomes predictions and patient stratification

In this fellowship, I will use a radical new approach to improve the radiotherapy treatment of patients suffering from inoperable non-small cell lung cancer (NSCLC). NSCLC is a cancer of unmet need for which the actual chemo-radiotherapy treatment has remained mostly unchanged for more than 30 years, with a poor 16.4% 5-year survival. This poor survival is caused by the limitation of the 'one-dose-fits-all' paradigm which neglects the diverse spectrum of clinical presentation in NSCLC. To improve the treatment, my group and I will harness the capacities of novel cutting-edge artificial intelligence techniques combined with a massive retrospective database of patients data to answer a fundamental question about lung cancer which is "How will the disease progress?". More precisely, the deep learning approach will be used to extract general trends relating patient's data features (histopathology, anatomy, tumour stage, tumour activity, treatment plan) to an outcome (death, recurrence, secondary fibrosis, heart failure and success). The methodology output will then be used for two endpoints of the study. It will first be directly used to inform and personalise the radiotherapy treatment planning strategy to improve patient survival. It will also serve as a basis to define a new stratification procedure for lung cancer patients to refine the clinical trials selection system. This framework will enact a paradigm change in treatment planning for radiotherapy and has the potential to enable a jump in performance of the treatment by tailoring the dose to the patient; thereby lowering the secondary effects and improving overall survival.

The primary beneficiaries of this project are people suffering from inoperable stage III NSCLC, through the hypothesised jump in survival enabled by the personalised therapy. Specific steps and objectives have been outlined in this fellowship to reach the patient which includes the development of a robust Code of Practice and a clinical trial. Furthermore, by directly involving patients, clinicians, and end-users in the development of the framework and the design of the clinical trial, I will ensure that the framework reaches the beneficiaries.

Additional beneficiaries include 1) academics in the fields of medical physics, radiotherapy and oncology who will benefit from the research advance in each of these specific fields. High-impact open-access publication and conference presentation will be sought in the field of diffeomorphism, machine learning, and personalised dosimetry. An international workshop will be organised to disseminate our results and to gather experts in the field who will provide critical feedback. Although every aspect of this projects is covered by the established collaborations, I will work with any interested new collaborators, as long as appropriate protection of the intellectual property on the developed framework is defined, which I will seek in collaboration with the UCL Business office.

Health services will benefit from the reduced cost of treatment, and both clinicians and carers will benefit from the increased efficiency of the treatment. To ensure that the technique developed aligns with the need and tolerance of the patients and carers, I will circulate the ideas and consult a patient and public involvement group, who will be selected with the UCL Medical Research Council Clinical Trials Units PPI Groups, that will help transition the framework to enable personalisation and collaboration in care decision making agenda. This group will include patients, carers and the general public interested in radiation oncology who will provide feedback on the framework, including advice on the research idea, study design, developing accessible information sheets and dissemination.