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: 10646140
• 负责人: Harini Veeraraghavan
• 金额: $ 43.63万
• 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/10646140
• 关键词: Address; Affect; Anatomy; Area; Artificial Intelligence; Cancer Etiology; Cardiotoxicity; Cessation of life; Characteristics; Chest; Clinical; Collaborations; Combination immunotherapy; 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; Publishing; Pulmonary Inflammation; Radiation; Radiation Dose Unit; Radiation Oncologist; Radiation Oncology; Radiation Therapy Oncology Group; Radiation therapy; Recurrence; Research; Risk; Risk-Benefit Assessment; Safety; Scanning; Shapes; Site; Software Tools; Source; Statistical Models; 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; artificial intelligence method; automated segmentation; automated treatment planning; 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.

概括 肺癌是美国治愈性放疗 +化疗的主要原因 无法使用或无法切除疾病的患者的护理标准已扩散到原发性范围之外 不幸的是，淋巴结的肿瘤。 包括免疫疗法在内的晚期治疗和辐射增加对器官的毒性增加。 对处于危险的正常器官（OAR）的辐射是由治疗余量引起的，以解释本地化的不确定性 尽管是标准设备的一部分，但肿瘤和桨 目前，计算机断层扫描（CBCTS）仅以有限的方式用于定位 治疗，不同时在线定位肿瘤和每个桨。 从CT和磁共振成像（MRI）所获得的人工智能（AI）方法 研究，创建自动分割工具，可以准确地将肿瘤定位和关键的桨在线定位 治疗设置。 拟议的小说AI方法称为“经过跨模式教育的学习”或CMEDL （“ C-Medal”）。 指导CT/CBCT网络，并“学习”提取强调组织组织类型之间扩展的特征 并在诸如纵隔等效率上的对比度中也会产生准确的分割。 为了 第一次，称为AI引导放疗（AIGRT）分割工具的临床实用性将 要系统地研究其潜在对治疗余量减小和正常组织的影响 CBCT上纵向分割的肿瘤和健康组织的毒性建模。 将提供增加的几何置信度，并为交付后估算提供更好的基础 提供的剂量，毒性和处理毒性，从而可以更好地评估潜在治疗的风险效益 适应。目的1：应用CMEDL方法论 CTS。 纳入特定于患者的解剖学和塑造CTS的先验。 可以通过执行自动化计划来改善（更安全）的肺癌放疗剂量特征 使用内部计划系统的交付模拟。 肺癌放射治疗的工具。 常规部署，实现（1）较小的边缘和较少的放射疗法毒素。 非常难以治疗的肿瘤中心肿瘤和（2）提供用于监测需要的工具的工具。 这些Aigrt Toold可能会部署到其他疾病部位，并一旦建立广泛地建立 可作为用于辐射处理的几何形状的务实，可概括的技术。