Personalised lung cancer treatment through outcomes predictions and patient stratification

在这个奖学金，我将使用一个激进的新方法，以改善放射治疗的患者患有不可手术的非小细胞肺癌（NSCLC）。NSCLC是一种需求未得到满足的癌症，其实际放化疗治疗在30多年来基本保持不变，5年生存率为16.4%。这种较差的生存率是由“一剂适用”模式的局限性引起的，该模式忽视了NSCLC临床表现的多样性。为了改善治疗，我和我的团队将利用新的尖端人工智能技术的能力，结合大量的患者数据回顾性数据库，来回答关于肺癌的一个基本问题，即“疾病将如何发展？".更准确地说，深度学习方法将用于提取患者数据特征（组织病理学、解剖学、肿瘤分期、肿瘤活动性、治疗计划）与结果（死亡、复发、继发性纤维化、心力衰竭和成功）之间的一般趋势。然后将方法学输出用于研究的两个终点。它将首先直接用于通知和个性化放射治疗计划策略，以提高患者生存率。它还将作为定义肺癌患者新分层程序的基础，以完善临床试验选择系统。该框架将在放射治疗计划中制定一个范式变化，并有可能通过为患者量身定制剂量来实现治疗性能的跳跃;从而降低继发效应并提高总生存率。

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.