The goal of Core D is to provide coherent, comprehensive and cost effective computer software support for all the projects and the other cores. The major areas of support include data management across multiple systems, specialized algorithms and applications for data processing, integration and visualization and sophisticated tools for decision support. Project 1 will investigate individualization of treatments for brain and head/neck cancers and Project 2 will investigate individualized dose escalation in volume-effect organs. These studies will involve acquisition of considerable patient specific imaging and clinical response data for the new comprehensive data management framework developed for Project 4. Core D will implement the data management and clinical applications needed to manage and analyze these data that provide the essential feedback for adaptive planning. Project 3 will investigate methods for acquisition, reconstruction and analysis of physiological imaging data. Core D will implement many of the sophisticated data processing and integration algorithms proposed in that project. As many of these algorithms involve specialized image processing, much of this work will be carried out in close collaboration, with Core C. Project 4 involves the development of a new comprehensive data framework that will allow accumulation and integration of patient specific data acquired prior to, during, and after treatment. These diverse data include anatomic and physiological imaging information, clinical findings, estimates of delivered dose and bookkeeping for different strategies or study protocols. The new framework will be integrated with our treatment plan optimization system to form a comprehensive platform for adaptive, patient-specific, treatment design and evaluation management. Project 4 will provide the requirement specifications and use-cases for the new data framework and software developers in Core D will create, test and manage these necessary modules.
Management and processing of diverse patient information requires an integrated infrastructure consisting of software and hardware for data storage and retrieval, specialized algorithms for data analysis and integration and sophisticated tools for decision support. A centralized software core can provide support for all of these areas to meet the needs of each project and the other cores in a coherent and cost effective manner.
|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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