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: 10299368
• 负责人: Carri Kaye Glide-Hurst
• 金额: $ 52.41万
• 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/10299368
• 关键词: Acute; 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; 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; clinical efficacy; clinical practice; comorbidity; computer science; cone-beam computed tomography; deep learning; experience; 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毁灭性并发症，发生在癌症治疗期间，之后不久，甚至许多 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、 和食管癌，已经表明，特别是，放射治疗（RT）可以导致主要的辐射诱导 心脏毒性，如充血性心力衰竭和冠状动脉疾病。一般来说， 心脏剂量评估涉及简单的心脏剂量/体积度量。然而，越来越多的证据表明， 包含在心脏内的心脏亚结构是高度放射敏感的 与评估全心脏剂量/体积指标相比，与总生存率的相关性更强。 然而，目前在常规临床实践中， 由于子结构在用于RT计划的CT模拟扫描上不可见，因此心脏MRI是有限的。 对于癌症患者来说并不广泛，并且手动描绘是繁琐的，每个病例花费6-10小时。 此外，心脏和呼吸运动使精确定位复杂化。我们的长期目标是 开发和验证临床可行的新技术，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，更有效的心脏保护策略， 办妥了一批我们在深度学习方面的专业知识以及在MR引导RT方面的经验为以下方面奠定了基础： 这一改变范式的提案，其长期目标是最佳的心脏保护，以最终减少辐射- 诱发心脏毒性。为达致整体目标，我们提出以下具体目标： 通过深度学习实现高质量、高效的心脏子结构分割和精确的合成CT生成， (ii)使用新型5D-MRI方法量化呼吸和心脏诱导的心脏子结构运动， 分次间不确定性，以导出用于稳健心脏保留的裕度和规划策略，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， 从MRI、生活质量、超声心动图和血液生物标志物中观察到的心脏功能纵向变化， MR引导的自适应放射治疗（保留）和标准X线治疗（全心脏剂量） 指标.这一多学科（肿瘤学、心脏病学、放射学和计算机科学）提案整合了国家 同时挑战使用全心脏剂量评估的护理标准。的 这项研究是创新的，因为它挑战了目前过于简化的经典模型， 通过几种尖端技术进行估计。这项研究具有重要意义，因为它具有广泛的应用 其他胸部癌症，包括肺癌、乳腺癌、淋巴瘤、食管癌和未来的儿科癌症试验。 最终，总体积极的影响是，我们的管道将产生高效的心脏子结构保留 以减少辐射相关的心脏毒性并最大限度地提高治疗效果。