Project 4 will investigate integration of patient-specific data (acquired from anatomic and physiological imaging and clinical Studies before and during therapy) together with other data and models of tumor and normal tissue response into individualized adaptive plan optimization. Though delivering the dose distribution that was prescribed at the beginning of treatment has been the goal of modifications of standard radiation planning, it is likely that this goal is not the best overall objective for each patient, when one takes into account the additional clinical and other information which can become available during the course of therapy. That is, each patient, and their tumor, changes over the course of treatment. However, approaches to deal with these changes have been makeshift and focused, in isolation, on only one issue at a time. The sum of those single-step corrections is vety unlikely to achieve the overall best solution for the patient. Therefore, the overall goal of this project is to develop and study methods that will integrate important clinical, imaging, and biological data and changes into an overall planning strategy to optimize each patient's treatment course in order to truly individualize therapy. Thus, this project addresses needed research on utilizing imaging and clinical response factors for decision support, adaptive treatment strategies and plan optimization. In particular, Specific Aim 1 will be tasked with organizing the expanse of population and patient specific data that exists and applying that information to provide knowledge-based decision support to help facilitate the best adaptive course of patient treatment.
Specific Aim 2 will design and investigate the adaptive treatment strategies that will lead to the greatest therapeutic gain for individual patients. The goal of Specific Aim 3 is to provide the radiation therapy treatment plan or set of plans that epitomizes the recommended course of therapy for each patient. Adaptive clinical trial strategies for the treatment sites studied in Projects 1 and 2 will be formulated;and we will run simulations to test these strategies for a variety of scenarios. Uncertainty assessments determined in Project 3 will also be incorporated. As part of the adaptive strategies will depend on planning quality, optimization studies will continue towards improving the quality and ease of achieving the best plans that take advantage of improved knowledge with regard to individual patient responses.
The outcome of this research will be to demonstrate that individualized (adaptive) treatments can be both effectively and efficiently managed through the use of knowledge-guided decision support tools. The proposed work in this project is expected to drive the field toward a new paradigm of individualized, response-driven, adaptive radiotherapy plan optimization.
|Luo, Yi; El Naqa, Issam; McShan, Daniel L et al. (2017) Unraveling biophysical interactions of radiation pneumonitis in non-small-cell lung cancer via Bayesian network analysis. Radiother Oncol 123:85-92|
|Ravishankar, Saiprasad; Moore, Brian E; Nadakuditi, Raj Rao et al. (2017) Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging. IEEE Trans Med Imaging 36:1116-1128|
|Hawkins, Peter G; Boonstra, Philip S; Hobson, Stephen T et al. (2017) Radiation-induced lung toxicity in non-small-cell lung cancer: Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship. Radiother Oncol 125:66-72|
|Tseng, Huan-Hsin; Luo, Yi; Cui, Sunan et al. (2017) Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys 44:6690-6705|
|Soni, Payal D; Boonstra, Philip S; Schipper, Matthew J et al. (2017) Lower Incidence of Esophagitis in the Elderly Undergoing Definitive Radiation Therapy for Lung Cancer. J Thorac Oncol 12:539-546|
|Jochems, Arthur; Deist, Timo M; El Naqa, Issam et al. (2017) Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries. Int J Radiat Oncol Biol Phys 99:344-352|
|Le, Mai; Fessler, Jeffrey A (2017) Efficient, Convergent SENSE MRI Reconstruction for Nonperiodic Boundary Conditions via Tridiagonal Solvers. IEEE Trans Comput Imaging 3:11-21|
|Hawkins, Peter G; Boonstra, Philip S; Hobson, Stephen T et al. (2017) Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels. Transl Oncol 11:102-108|
|El Naqa, Issam; Kerns, Sarah L; Coates, James et al. (2017) Radiogenomics and radiotherapy response modeling. Phys Med Biol 62:R179-R206|
|Dess, Robert T; Sun, Yilun; Matuszak, Martha M et al. (2017) Cardiac Events After Radiation Therapy: Combined Analysis of Prospective Multicenter Trials for Locally Advanced Non-Small-Cell Lung Cancer. J Clin Oncol 35:1395-1402|
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