Reducing cardiac toxicity with deep learning and MRI-guided radiation therapy

通过深度学习和 MRI 引导放射治疗减少心脏毒性

• 批准号: 10473755
• 负责人: Carri Kaye Glide-Hurst
• 金额: $ 53.13万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-23 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10473755
• 关键词: Acute; Acute Coronary Event; Aftercare; Angiography; Atlases; Attention; Benchmarking; Biological Markers; Blood; Breast Lymphoma; Cancer Intervention; Cancer Patient; Cancer Survivor; Cardiac; Cardiac Volume; Cardiology; Cardiotoxicity; Chest; Clinical; Clinical Trials; Complex; Congestive Heart Failure; Coronary; Coronary Arteriosclerosis; Coronary artery; Coupled; Coupling; Data; Dose; Echocardiography; Emerging Technologies; Esophageal Lymphoma; Evaluation; Exhibits; Future; Generations; Goals; Heart; Heart Atrium; Hour; Image; Incidence; Lead; Life Expectancy; Linear Accelerator Radiotherapy Systems; Link; Longterm Follow-up; Lung; Lymphoma; Magnetic Resonance Imaging; Malignant Childhood Neoplasm; Malignant Neoplasms; Malignant neoplasm of esophagus; Malignant neoplasm of lung; Malignant neoplasm of thorax; Manuals; Meta-Analysis; Methods; Modality; Modeling; Modification; Morbidity - disease rate; Motion; Non-Small-Cell Lung Carcinoma; Oncology; Organ; Outcome; Patients; Pericardial effusion; Public Health; Quality of life; Radiation; Radiation Dose Unit; Radiation Therapy Oncology Group; Radiation Tolerance; Radiation therapy; Radiology Specialty; Randomized Clinical Trials; Research; Risk; Roentgen Rays; Safety; Scanning; Techniques; Technology; Therapeutic; Translations; Treatment-Related Cancer; Uncertainty; United States National Institutes of Health; Validation; X-Ray Computed Tomography; base; cancer complication; cancer therapy; cardiac magnetic resonance imaging; clinical efficacy; clinical practice; comorbidity; computer science; cone-beam computed tomography; deep learning; experience; generative adversarial network; heart function; image guided; image guided radiation therapy; image processing; imaging approach; improved; improved outcome; innovation; malignant breast neoplasm; minimally invasive; multi-task learning; multidisciplinary; multimodality; new technology; novel; pericardial sac; prospective; radiation delivery; respiratory; response; side effect; simulation; soft tissue; standard of care; treatment planning; tumor; volunteer

Cardiac toxicity is a devastating complication of cancer treatment and occurs during, shortly after, or even many years after treatment. Long-term follow up of patients undergoing thoracic radiation, such as lymphoma, lung, and esophageal cancers, has shown that in particular, radiation therapy (RT) can lead to major radiation-induced cardiac toxicities like congestive heart failure and coronary artery disease. Typically, the standard of care for cardiac dose assessment involves simple heart dose/volume metrics. However, mounting evidence suggests that cardiac substructures contained within the heart are highly radiosensitive and dose to substructures are more strongly associated with overall survival than assessing whole-heart dose/volume metrics. Nevertheless, precise characterization of cardiac substructure dose in routine clinical practice is currently limited because substructures are not visible on CT simulation scans used for RT planning, cardiac MRI are not widely available for cancer patients, and manual delineation is cumbersome, taking 6-10 hours per case. Further, precise localization is complicated by both cardiac and respiratory motion. Our long-term goal is to develop and validate clinically viable novel technologies to localize cardiac substructures for novel cancer therapies and interventions. The rationale for the proposed research is that by developing a robust and efficient clinical framework for cardiac substructure dose assessment, more effective cardiac sparing strategies can be achieved. Our expertise in deep learning coupled with experience in MR-guided RT has laid the groundwork for this paradigm-changing proposal with the long-term goal of optimal cardiac sparing to ultimately reduce radiation- induced cardiac toxicity. To attain the overall objectives, we propose the following specific aims: (i) develop high quality, efficient cardiac substructure segmentation and accurate synthetic CT generation via deep learning, (ii) quantify respiratory and cardiac-induced cardiac substructure motion using a novel 5D-MRI approach and inter-fraction uncertainties to derive margins and planning strategies for robust cardiac sparing, and (iii) evaluate the clinical efficacy of these emerging technologies in a randomized clinical trial for lung cancer evaluating longitudinal changes in cardiac function from MRI, quality of life, echocardiogram, and blood biomarkers between MR-guided adaptive radiation therapy with sparing and standard x-ray based treatment with whole-heart dose metrics. This multi-disciplinary (oncology, cardiology, radiology, and computer science) proposal integrates state of the art technologies while challenging the standard of care of using whole-heart dose evaluations. The research proposed is innovative as it challenges the current, oversimplified classic model of whole-heart dose estimates via several cutting-edge techniques. The research is significant because of its widespread application in other thoracic cancers including lung, breast, lymphoma, esophageal, and future pediatric cancer trials. Ultimately, the overall positive impact is that our pipeline will yield highly effective cardiac substructure sparing to reduce radiation-related cardiac toxicities and maximize therapeutic gains.

心脏毒性是癌症治疗的一种破坏性并发症，发生在治疗过程中、治疗后不久、甚至多次发生。 治疗后数年。对接受胸部放射治疗的患者进行长期随访，如淋巴瘤、肺癌、 和食道癌，已经表明，特别是放射治疗（RT）可以导致主要的放射诱发 心脏毒性，如充血性心力衰竭和冠状动脉疾病。通常，护理标准 心脏剂量评估涉及简单的心脏剂量/体积指标。然而，越来越多的证据表明 心脏内的心脏下部结构对放射线高度敏感，并且会对下部结构造成剂量影响 与评估全心剂量/体积指标相比，与总生存期的相关性更强。 然而，目前在常规临床实践中对心脏亚结构剂量的精确表征 由于在用于 RT 计划的 CT 模拟扫描中看不到子结构，因此心脏 MRI 是有限的 对于癌症患者来说，这种方法尚未广泛应用，并且手动描绘很麻烦，每个病例需要 6-10 小时。 此外，心脏和呼吸运动都使精确定位变得复杂。我们的长期目标是 开发和验证临床上可行的新技术，以定位新型癌症的心脏亚结构 治疗和干预。拟议研究的基本原理是通过开发一个强大而有效的 心脏亚结构剂量评估的临床框架，可以制定更有效的心脏保护策略 实现了。我们在深度学习方面的专业知识加上 MR 引导 RT 方面的经验为 这一改变范式的提案的长期目标是最佳心脏保护，最终减少辐射- 引起的心脏毒性。为实现总体目标，我们提出以下具体目标： 通过深度学习实现高质量、高效的心脏亚结构分割和准确的合成 CT 生成， (ii) 使用新型 5D-MRI 方法量化呼吸和心脏引起的心脏亚结构运动，以及 分次间的不确定性以获得稳健心脏保护的裕度和规划策略，以及（iii）评估 这些新兴技术在肺癌随机临床试验中的临床疗效评估 MRI 心脏功能、生活质量、超声心动图和血液生物标志物的纵向变化 MR 引导的适应性放射治疗，采用全心剂量的保留治疗和基于标准 X 射线的治疗 指标。这个多学科（肿瘤学、心脏病学、放射学和计算机科学）提案整合了国家 先进的技术，同时挑战使用全心剂量评估的护理标准。这 提出的研究具有创新性，因为它挑战了当前过于简化的全心剂量经典模型 通过多种尖端技术进行估算。该研究因其广泛的应用而具有重要意义 其他胸部癌症，包括肺癌、乳腺癌、淋巴瘤、食道癌和未来的儿科癌症试验。 最终，总体积极影响是我们的管道将产生高效的心脏亚结构保护 减少与辐射相关的心脏毒性并最大限度地提高治疗效果。