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

心脏毒性是癌症治疗的一种毁灭性的并发症，发生在癌症治疗期间、之后不久，甚至许多时候。 治疗数年后。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)可以导致主要的辐射诱导 心脏毒性，如充血性心力衰竭和冠状动脉疾病。通常，护理的标准是 心脏剂量评估包括简单的心脏剂量/体积测量。然而，越来越多的证据表明 心脏内包含的心脏亚结构对辐射高度敏感，对亚结构具有剂量效应。 与评估全心脏剂量/体积指标相比，与总存活率的相关性更强。 然而，在常规的临床实践中，心脏亚结构剂量的精确表征目前是 由于在用于RT计划的CT模拟扫描上看不到子结构，心脏MRI是有限的 癌症患者不能广泛使用，手工勾画很麻烦，每个病例需要6-10个小时。 此外，心脏和呼吸运动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，通过开发一种稳健和高效的 临床框架下进行心脏亚结构剂量评估，可以更有效地制定心脏节育策略 已实现。我们在深度学习方面的专业知识，加上MR引导RT的经验，为 这一改变范式的提议的长期目标是优化心脏保护，最终减少辐射- 致心脏毒性。为实现总体目标，我们提出了以下具体目标：(一)发展 通过深度学习，高质量、高效的心脏亚结构分割和准确的合成CT生成， (Ii)使用新的5D-MRI方法量化呼吸和心脏引起的心脏亚结构运动，并 分数间的不确定性，以得出利润率和稳健心脏保护的规划策略，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 MRI、生活质量、超声心动图和血液生物标志物对心功能的纵向变化 MR引导下的适配性放射治疗与标准X线全心剂量治疗 指标。这项多学科(肿瘤学、心脏病学、放射学和计算机科学)的提案将国家 最先进的技术，同时挑战使用全心脏剂量评估的护理标准。这个 提出的研究具有创新性，因为它挑战了当前过于简单化的全心剂量经典模型 通过几种尖端技术进行估计。由于其广泛的应用，因此具有重要的研究意义。 在其他胸癌，包括肺癌、乳腺癌、淋巴瘤、食道癌和未来的儿科癌症试验中。 最终，总体上的积极影响是，我们的管道将产生高效的心脏下部结构保留 以减少与辐射相关的心脏毒性，并最大限度地提高治疗效果。

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.