Safer lung cancer radiotherapy delivery using novel artificial intelligence methods

使用新颖的人工智能方法更安全地进行肺癌放射治疗

• 批准号: 10366916
• 负责人: Andreas Rimner
• 金额: $ 39.36万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10366916
• 关键词: Address; Affect; Anatomy; Area; Artificial Intelligence; Cancer Etiology; Cardiotoxicity; Cessation of life; Characteristics; Chest; Clinical; Combined Modality Therapy; Complication; Cone; Dangerousness; Data Set; Disease; Dose; Ensure; Equipment; Esophagus; Geometry; Goals; Heart; Image; Immunotherapy; Lead; Learning; Link; Lung; Lung CAT Scan; Lung Neoplasms; Magnetic Resonance Imaging; Malignant neoplasm of lung; Manuals; Medal; Mediastinum; Methodology; Methods; Modality; Modeling; Monitor; Morbidity - disease rate; Non-Small-Cell Lung Carcinoma; Normal tissue morphology; Organ; Patient Care; Patients; Positioning Attribute; Primary Neoplasm; Probability; Publishing; Pulmonary Inflammation; Radiation; Radiation Dose Unit; Radiation Oncologist; Radiation Oncology; Radiation Therapy Oncology Group; Radiation Toxicity; Radiation therapy; Recurrence; Research; Risk; Risk-Benefit Assessment; Safety; Scanning; Shapes; Site; Software Tools; Source; Survival Rate; System; Systemic Therapy; Technology; Testing; The Cancer Imaging Archive; Time; Tissues; Toxic effect; Training; Treatment-related toxicity; Uncertainty; Unresectable; Work; Workload; acute toxicity; automated segmentation; base; cancer radiation therapy; chemoradiation; chemotherapy; clinically relevant; cohort; cone-beam computed tomography; deep learning; effective therapy; feasibility testing; image guided radiation therapy; imaging study; improved; improved outcome; innovation; learning strategy; lymph nodes; novel; radiation response; simulation; soft tissue; standard care; standard of care; tool; treatment planning; tumor

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.

总结 肺癌是美国癌症相关死亡的主要原因。 不能手术或不能切除的疾病扩散到原发灶以外的患者的标准治疗 转移到淋巴结不幸的是，这种治疗方法具有15%-40%的高复发率， 包括免疫疗法和放射疗法的先进治疗增加了对器官的毒性。溢出 对正常危险器官（OAR）的辐射来自治疗边缘，以解释定位的不确定性 肿瘤和OAR。尽管是标准设备的一部分，但来自治疗室锥形光束的信息 计算机断层摄影扫描（CBCT）目前仅以有限的方式用于患者定位， 治疗，而无需同时在线定位肿瘤和每个OAR。该提案将采用创新的 人工智能（AI）方法，已经从CT和磁共振成像（MRI）训练 研究，以创建自动分割工具，可以准确地定位肿瘤和关键OAR在线 治疗设置。 提出的新AI方法被称为“跨模态教育学习”或CMEDL （'c-medal'）。CMEDL的主要优势是MRI数据集，即使来自不同的患者，也可以用于 引导CT/CBCT网络并“学习”提取强调组织类型之间差异的特征 并且即使在诸如纵隔的具有很少固有对比度的区域中也产生精确的分割。 为 第一次，可以称为AI引导放射治疗（AIGRT）分割工具的临床实用性将 系统研究其对治疗切缘减少和正常组织的潜在影响 CBCT上纵向分段肿瘤和健康组织的毒性建模。拟议的AIGRT工具 将提供更高的几何置信度，并为交付后的 递送剂量和治疗毒性，能够更好地评估潜在治疗的风险-获益 适应目的1：应用CMEDL方法开发计划中的肺肿瘤和OAR分割 CT目的2：将CMEDL方法扩展到每周CBCT上的纵向分段肿瘤和OAR， 结合来自规划CT的患者特定解剖和形状先验知识。目标3：确定CMEDL是否 可以通过执行自动规划来实现改善的（更安全的）肺癌放射治疗剂量特性 和交付模拟，使用内部规划系统。项目目标：开发并严格测试AIGRT 肺癌放疗治疗的工具。潜在影响：如果成功，这些创新的人工智能工具可以 常规部署，使（1）患者的切缘更小，放射治疗毒性更低，包括那些 非常难以治疗的中央肿瘤和（2）提供监测计划变更需求的工具。 这些AIGRT工具有可能被部署到其他疾病地点，一旦建立， 可作为一种实用的、可推广的技术，用于整个放射治疗过程中的几何形状引导。