Personalised lung cancer treatment through outcomes predictions and patient stratification

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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.