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.
|Johansson, Adam; Balter, James M; Cao, Yue (2018) Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification. Med Phys 45:4529-4540|
|Tseng, Huan-Hsin; Luo, Yi; Ten Haken, Randall K et al. (2018) The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy. Front Oncol 8:266|
|Jochems, Arthur; El-Naqa, Issam; Kessler, Marc et al. (2018) A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy. Acta Oncol 57:226-230|
|Rosen, Benjamin S; Hawkins, Peter G; Polan, Daniel F et al. (2018) Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy. Int J Radiat Oncol Biol Phys 102:1319-1329|
|Luo, Yi; McShan, Daniel L; Matuszak, Martha M et al. (2018) A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy. Med Phys :|
|Simeth, Josiah; Johansson, Adam; Owen, Dawn et al. (2018) Quantification of liver function by linearization of a two-compartment model of gadoxetic acid uptake using dynamic contrast-enhanced magnetic resonance imaging. NMR Biomed 31:e3913|
|Mendiratta-Lala, Mishal; Masch, William; Shankar, Prasad R et al. (2018) MR Imaging Evaluation of Hepatocellular Carcinoma Treated with Stereotactic Body Radiation Therapy (SBRT): Long Term Imaging Follow-Up. Int J Radiat Oncol Biol Phys :|
|Ohri, Nitin; Tomé, Wolfgang A; Méndez Romero, Alejandra et al. (2018) Local Control After Stereotactic Body Radiation Therapy for Liver Tumors. Int J Radiat Oncol Biol Phys :|
|Mendiratta-Lala, Mishal; Gu, Everett; Owen, Dawn et al. (2018) Imaging Findings Within the First 12 Months of Hepatocellular Carcinoma Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 102:1063-1069|
|Wang, Shulian; Campbell, Jeff; Stenmark, Matthew H et al. (2018) A model combining age, equivalent uniform dose and IL-8 may predict radiation esophagitis in patients with non-small cell lung cancer. Radiother Oncol 126:506-510|
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