Safer lung cancer radiotherapy delivery using novel artificial intelligence methods

摘要 肺癌是美国癌症相关死亡的主要原因。根治性放疗+化疗是 对不能手术或不能切除疾病的患者的护理标准已经超出了初级疾病的范围 肿瘤转移到淋巴结。不幸的是，这种治疗方法的复发率高达15%-40%， 包括免疫治疗和放射治疗在内的高级治疗会增加对器官的毒性。溢出效应 治疗余量对正常危险器官(OAR)的辐射，以解释定位的不确定性 肿瘤和桨。尽管是标准设备的一部分，来自治疗室锥束的信息 计算机断层扫描(CBCT)目前仅以有限的方式用于患者在 治疗，无需同时对肿瘤和每个OAR进行在线定位。这项提案将使用创新的 人工智能(AI)方法，来自CT和磁共振成像(MRI)的训练 研究，创建自动分割工具，可以在线准确定位肿瘤和关键桨 治疗方案。 新的人工智能方法论被称为“跨通道教育学习”，简称CMEDL (‘C奖牌’)。CMEDL的主要优势是，即使来自不同患者的MRI数据集也可以用于 引导CT/CBCT网络并学习提取强调组织类型差异的特征 并且即使在像纵隔这样内在对比度很小的区域也能产生准确的分割。 为 第一次，可以被称为人工智能引导的放射治疗(AIGRT)分割工具的临床实用将 系统地研究它们对治疗范围减少和正常组织的潜在影响 基于CBCT的纵向分割肿瘤和健康组织的毒性模拟。建议的AIGRT工具 将提供更高的几何可信度，并为交付后的估计提供更好的基础 提供剂量和治疗毒性，使潜在治疗能够进行更好的风险-收益评估 改编。目的1：应用CMEDL方法学开发肺肿瘤和计划中的桨叶分割 CTS.目的2：将CMEDL方法扩展到每周CBCT的纵向节段肿瘤和桨， 从计划CT中纳入患者特定的解剖和形状先验资料。目标3：确定CMEDL是否 可以通过执行自动计划来改善(更安全的)肺癌放射治疗剂量特性 和交付模拟，使用内部计划系统。项目目标：开发并严格测试AIGRT 肺癌放射治疗的工具。潜在影响：如果成功，这些创新的人工智能工具可能会 常规部署，使(1)患者的利润率更低，放射毒性更小，包括那些 很难治疗的位于中央的肿瘤，以及(2)提供监测计划变更需求的工具。 这些AIGRT工具可能会部署到其他疾病地点，一旦建立，就会得到广泛应用 作为一种实用的、可推广的技术，可在整个放射治疗过程中进行几何指导。

SUMMARY Lung cancer is the leading cause of cancer-related deaths in the U.S. Curative radiotherapy + chemotherapy is the standard of care for patients with inoperable or unresectable disease that has spread beyond the primary tumor to the lymph nodes. Unfortunately, this treatment approach has a high recurrence of 15%-40% and advanced treatments including immunotherapy combined with radiation increase toxicity to organs. Spillover radiation to normal organs at risk (OAR) results from treatment margins to account for uncertainty in localizing tumors and OARs. Despite being part of standard equipment, information from in-treatment room cone-beam computed tomography scans (CBCTs) is currently used only in limited ways for patient positioning during treatment, without simultaneous online localization of the tumor and each OAR. This proposal will use innovative artificial intelligence (AI) methods, that have been trained from both CT and magnetic resonance imaging (MRI) studies, to create auto-segmentation tools that can accurately localize the tumor and key OARs online at treatment setup. The proposed novel AI methodology is called “Cross-Modality Educed Learning” or CMEDL (‘c-medal’). The key advantage of CMEDL is that MRI datasets, even from different patients, can be used, to guide the CT/CBCT network and “learn” to extract features that emphasize the difference between tissue types and produces accurate segmentations even in areas with little inherent contrast such as the mediastinum. For the first time, the clinical utility of what could be called AI-Guided Radiotherapy (AIGRT) segmentation tools will be systematically studied in relation to their potential impact on treatment margin reduction and normal tissue toxicity modeling for longitudinally segmented tumor and healthy tissues on CBCTs. Proposed AIGRT tools would provide increased geometric confidence as well as provide a better basis for an after-delivery estimate of delivered dose, and treatment toxicity, enabling better risk-benefit assessments for potential treatment adaptations. Aim 1: Apply CMEDL methodology to develop lung tumor and OAR segmentations on planning CTs. Aim 2: Extend the CMEDL methodology to longitudinally segment tumors and OARs on weekly CBCTs, incorporating patient-specific anatomic and shape priors from planning CTs. Aim 3: Determine whether CMEDL can enable improved (safer) lung cancer radiotherapy dose characteristics by performing automated planning and delivery simulations, using in-house planning system. Project goal: To develop and rigorously test AIGRT tools for lung cancer radiotherapy treatments. Potential impact: If successful, these innovative AI tools could be deployed routinely, enabling (1) smaller margins and less radiotherapy toxicity for patients, including those with very difficult-to-treat centrally located tumors and (2) providing tools for monitoring the need for plan changes. These AIGRT tools could potentially be deployed to other disease sites, and once established be made widely available as a pragmatic, generalizable technology for geometry guidance throughout the radiation treatment.