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: 10674519
• 负责人: Carri Kaye Glide-Hurst
• 金额: $ 61.94万
• 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/10674519
• 关键词: Acute; Acute Coronary Event; Aftercare; Angiography; Atlases; Attention; Benchmarking; Biological Markers; Blood; Breast; 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; Esophagus; Evaluation; Exhibits; Future; Generations; Goals; Heart; Heart Atrium; Hour; Image; Incidence; 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; 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; automated treatment planning; 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 processing; improved; improved outcome; innovation; malignant breast neoplasm; minimally invasive; multi-task learning; multidisciplinary; multimodality; new technology; novel; pericardial sac; prospective; radiation delivery; radiation mitigation; randomized, clinical trials; respiratory; response; side effect; simulation; soft tissue; standard of care; treatment planning; tumor; volunteer; wearable device

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、治疗后不久，甚至许多癌症治疗期间