PRE-ACT Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification

放射治疗是一种广泛使用的癌症治疗方法，但也有一些患者会出现副作用。乳腺癌的副作用可能包括乳房萎缩、手臂肿胀(淋巴水肿)和心脏损伤。一些增加副作用风险的因素是已知的，但目前的方法并没有使用所有可用的复杂成像和基因组数据。现在是时候利用人工智能的巨大潜力来预测副作用了。该项目将使用来自三个患者队列的丰富数据集来设计和实现一个人工智能工具，该工具可以预测乳腺癌患者手臂淋巴水肿等副作用的风险，并提供易于理解的解释，以支持患者和医生之间的共同决策。ACT前联盟结合了解决这一问题所需的计算机(MDW，AUEB-RC)、人工智能(HES-SO，ISI)、放射肿瘤学(MAASTRO，Unicancer)、医学物理学(Mirada THERAPANACEA)、遗传学(ULEIC)、心理学(CNR)和卫生经济学(MU)方面的专业知识。该项目将整合来自三个队列的数据，并构建具有内置可解释性的人工智能预测模型，以解释乳腺癌放射治疗的每个关键副作用。这些AI模型将被整合到现有的商业放射治疗软件平台中，创建一款世界领先的产品。扩展后的平台将在临床试验中得到验证，以支持有关淋巴照射的治疗决定。这项试验将采用一种创新的设计，即测试组中的患者和医疗团队将收到风险预测，但控制组中的患者和医疗团队不会。采用共同设计方法构建的沟通方案将确保人工智能成果有效地为利益攸关方量身定做。该试验将评估使用AI平台是否改变了手臂淋巴水肿率，并影响了治疗决策和生活质量。将通过迁移学习技术寻求其他类型癌症的人工智能模型的泛化。

Radiotherapy is a widely used cancer treatment, however some patients suffer side effects. In breast cancer, side effects can include breast atrophy, arm swelling (lymphedema), and heart damage. Some factors that increase risk for side effects are known, but current approaches do not use all available complex imaging and genomics data. The time is now ripe to leverage the huge potential of AI towards prediction of side effects. This project will use rich datasets from three patient cohorts to design and implement an AI tool that predicts the risk of side effects, including arm lymphedema in breast cancer patients and provides an easily understood explanation to support shared decision-making between the patient and physician. The PRE-ACT consortium combines the expertise in computing (MDW, AUEB-RC), AI (HES-SO, ISI), radiation oncology (MAASTRO, UNICANCER), medical physics (MIRADA THERAPANACEA), genetics (ULEIC), psychology (CNR) and health economics (MU) that is necessary to tackle this problem. The project will integrate data from the three cohorts and build AI predictive models with built-in explainability for each of the key side effects of breast cancer radiotherapy. These AI models will be incorporated into an existing commercial radiotherapy software platform to create a world-leading product. The extended platform will be validated in a clinical trial to support treatment decisions regarding the irradiation of lymph nodes. The trial will adopt an innovative design in which the patients and medical team in the test arm will receive the risk prediction, but those in the control arm will not. A communication package built with a co-design methodology will ensure that AI outcomes are tailored to stakeholders effectively. The trial will evaluate whether using the AI platform changed the arm lymphedema rate and impacted treatment decisions and quality-of-life. Generalizability of the AI models for other types of cancer will be sought through transfer learning techniques.