Lung Biomechanical Modelling Driven by Machine Learning Algorithm Towards Effective Lung Cancer Radiation Therapy

肺癌是男性和女性癌症死亡的最常见原因，因为它的5年存活率低至14%。外照射疗法广泛应用于肺癌的治疗。然而，由于肿瘤在呼吸过程中的运动和变形，它具有极大的挑战性。为了对肿瘤施加足够高的辐射剂量以摧毁癌细胞，同时将健康组织的剂量保持在最低限度，放射治疗系统被设计为在患者治疗过程中呼吸时，辐射束瞄准移动的肿瘤。只有在整个治疗过程中准确估计移动的肿瘤位置及其变化的形状，才能实现这一点。由于没有一种医学成像方法能够在治疗过程中对肿瘤进行无害的可视化，我们采用了另一种方法，即在整个治疗过程中估计肿瘤的不同位置和形状。有了这个信息，辐射束就可以跟随肿瘤的位置，同时它被连续地限制在它的形状上。在这种方法中，可见胸部表面的运动数据被测量并输入到计算机模型中，以估计随着时间的推移肿瘤的位置和形状。一个有效的模型用于这一估计是我们在这项研究中致力于开发的最重要的元素。该模型将是一个基于呼吸生物力学开发的计算机程序。它是特定于患者的(即考虑患者的特定解剖结构等)。使用胸部运动数据估计肿瘤随时间的运动和形状。肺放射治疗的另一个问题是，辐射剂量计划可能与对健康组织的重大伤害有关。这样的计划可以通过准确识别通常与癌症并存的肺气体捕获来改进。这可以使用图像处理方法来实现，我们将开发图像处理方法来识别这些区域，在计划让高度集中的光束穿过这些区域之前，使用患者的医学图像。因此，拟议研究的主要目标是开发并严格验证呼吸系统的计算机模型，使用生物力学来准确预测肺肿瘤的运动和形状。该模型将输入患者胸部表面的运动数据，这些数据可以使用光学跟踪系统进行测量，以输出肿瘤在整个放射治疗过程中不同的位置和形状。另一个目标是准确识别肺部的气体捕获区域，在这些区域中，只有很少的组织可能暴露在其体积内的辐射中。这些区域可用于有效的治疗计划，其中这些区域内的辐射束浓度最大化。这项研究的长期目标是将这些发展纳入临床应用，将肿瘤运动/变形数据馈送到具有运动补偿能力的放射机，以获得最佳治疗结果。这项研究预计将对肺癌患者的医疗保健产生重大影响。**

Lung cancer is the most common cause of cancer death in both men and women as its 5-year survival rate is as low as 14%. External Beam Radiation Therapy is widely used for lung cancer treatment. However, it is extremely challenging due to tumor motion and deformation during respiration. To apply sufficiently high radiation dose to tumor to destroy cancer cells while keeping dose of healthy tissue at minimum, radiation therapy systems are designed such that radiation beam targets moving tumor while patients breathes during therapy session. This can be achieved only if the moving tumor position and its varying shape are estimated accurately throughout the session. Since no medical imaging method is capable of harmless visualisation of the tumor during therapy, we pursue another approach which involves estimating the varying tumor position and shape throughout the session. Having this information the radiation beam can be made to follow the tumor position while it is confined to its shape continuously. In this approach, motion data of the visible chest's surface is measured and input in a computer model to estimate the tumor position and shape over time. An effective model to be used for this estimation is the most important elements that we aim to develop in this research. The model will be a computer program developed based on the biomechanics of respiration. It is patient specific (i.e. considers the patient's specific anatomy etc.) to estimate the tumor motion and shape over time using the chest motion data. Another issue with lung radiation therapy is that radiation dose planning maybe associated with significant harm to healthy tissue. Such planning can be improved by accurate identification of lung gas trapping normally coexisting with cancer. This can be achieved using image processing methods that we will develop to identify such regions using patient medical image before planning to have highly concentrated beams through these regions. As such, the primary objective of the proposed research is to develop and rigorously validate computer models of the respiration system using biomechanics to accurately predict lung tumor motion and shape. The model will input motion data of the patient's chest surface that can be measured using optical tracking systems to output the tumor's varying location and shape throughout the radiation therapy procedure. Another objective is accurate identification of gas trapping regions in the lung where only little tissue maybe exposed to radiation within their volume. Such regions can be utilized for effective therapy planning where radiation beam concentration within these regions is maximized. A long term objective of the research is to incorporate these developments into clinical applications where the tumor motion/deformation data is fed to radiation machines with motion compensation capability for optimal therapy outcome. The research is expected to have major impact on health care of lung cancer patients.**