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

机器学习算法驱动的肺部生物力学建模实现有效的肺癌放射治疗

• 批准号: RGPIN-2019-06619
• 负责人: Samani, Abbas
• 金额: $ 2.33万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715276
• 关键词: Lung; Biomechanical; Modelling; Driven; Machine

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.

肺癌是男性和女性癌症死亡的最常见原因，其5年生存率低至14%。体外放射治疗广泛应用于肺癌的治疗。然而，由于呼吸过程中肿瘤的运动和变形，这是极具挑战性的。为了向肿瘤施加足够高的辐射剂量以破坏癌细胞，同时将健康组织的剂量保持在最低限度，放射治疗系统的设计使得放射束在治疗过程中在患者呼吸时靶向移动的肿瘤。只有在整个过程中准确地估计肿瘤的移动位置及其变化形状，才能实现这一目标。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，我们采用另一种方法，包括在整个治疗过程中估计肿瘤的位置和形状。有了这些信息，就可以使放射光束跟随肿瘤的位置，而它被连续地限制在它的形状内。在这种方法中，测量可见胸部表面的运动数据并将其输入到计算机模型中，以估计肿瘤的位置和形状随时间的变化。一个有效的模型用于这种估计是我们在本研究中所要开发的最重要的元素。该模型将是一个基于呼吸生物力学的计算机程序。使用胸部运动数据来估计肿瘤随时间的运动和形状是患者特异性的（即考虑患者的特定解剖结构等）。肺放射治疗的另一个问题是放射剂量计划可能会对健康组织造成重大伤害。这种规划可以通过准确识别通常与癌症共存的肺气体捕获来改进。这可以使用图像处理方法来实现，我们将开发在计划通过这些区域的高度集中光束之前使用患者医学图像来识别这些区域。因此，提出的研究的主要目的是开发和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，这些数据可以通过光学跟踪系统进行测量，从而在整个放射治疗过程中输出肿瘤的不同位置和形状。另一个目标是准确识别肺中只有少量组织可能暴露于其体积内的辐射的气体捕获区域。这些区域可以用于有效的治疗计划，在这些区域内的辐射束浓度是最大的。该研究的长期目标是将这些发展纳入临床应用，将肿瘤运动/变形数据提供给具有运动补偿能力的放射机，以获得最佳治疗效果。预计该研究将对肺癌患者的医疗保健产生重大影响。