Hyperpolarized Xenon-129 MRI as a predictive biomarker for mitigating radiation-induced lung injury - PROJECT SUMMARY/ABSTRACT Research. Lung cancer is the most common malignancy that affects both sexes, with over 238,00 new cases and 127,000 deaths per year. Most lung cancer patients will be treated with radiation as either a primary or adjuvant therapy approach. Despite advancements in lung cancer radiotherapy, radiation-induced lung injury (RILI), typically in the form of radiation pneumonitis (RP), has remained prevalent with incidences of severe RP as high as 40% of patients. RP is a serious treatment-related side effect that can lead to irreversible fibrosis and dyspnea, and greatly reduce patient quality of life. Furthermore, RP limits safely deliverable radiation dose, thereby decreasing treatment efficacy. Therefore, radiotherapy treatment strategies that can reduce or eliminate RP is an unmet clinical need. Functional avoidance radiotherapy is a novel approach to radiation treatments where healthy tissue with the greatest functional capacity is identified by quantitative imaging, and thus spared, when creating a patient-specific treatment. Indeed, hyperpolarized Xenon-129 MRI has shown great promise in identifying lung dysfunction from several pathologies (COPD, asthma, cystic fibrosis, etc.), but this technology has never been implemented to measure longitudinal radiation effects, nor implemented in a functional avoidance radiotherapy approach. Our central hypothesis is that longitudinal Xenon-129 MRI is a predictive biomarker of RP that can be used to create a functional avoidance radiotherapy strategy, which will greatly reduce RP incidence. The long-term objective of this project is to establish Xenon-129 MRI-guided functional avoidance radiotherapy as the optimal treatment strategy for lung cancer radiotherapy by minimizing RILI. Aim 1 determines the effect of baseline lung function, tumor location within the lung, and radiation modality (i.e., whether photon or proton radiotherapy) affects the radiation-response, as quantified by Xenon-129 MRI. Aim 2 establishes Xenon-129 MRI of the lung as a predictive biomarker of RP. Finally, Aim 3 develops a functional avoidance radiotherapy treatment approach and determines the reduction of severe RP as compared to standard-of-care radiotherapy using machine learning. Career Development & Training. Dr. Niedzielski’s long-term career goal is to become an independent investigator in the field of treatment-related pulmonary toxicity prevention using patient-orientated research and advanced quantitative MR imaging. This proposal includes Dr. Niedzielski’s 5-year mentored career development plan, which includes mentoring and collaborations with senior, established NIH investigators in pulmonary medicine, radiation oncology, and MRI engineering. Necessary training in prospective human subject MRI studies, clinical trial design, biostatics, and advanced radiotherapy treatment strategies are detailed in this development plan and build upon Dr. Niedzielski research expertise in toxicity modeling and predictive analytics.
