Shared resource Core A will provide all administrative support to the grant. The core, also provides clinical data management and all statistical support to the research projects and other shared resource cores. Specific areas of administrative responsibility include long and short term planning, review of scientific progress and coordination of the interactions between the shared resource cores and the research projects. Toward these ends, both overall co-Program Directors will participate in this Core. Also, both internal and external scientific advisory committees will be formed to provide independent, expert, program evaluation. Purchasing and personnel management, budget and accounts management, and the preparation and submission of progress reports will also be coordinated by this administrative core. Statistical support provided by this core includes statistical design, control, and analyses of the clinical studies in Projects 1 and 2, and statistical design and analyses of the many different experiments and studies in the other projects and cores.
The entire program project effort benefits from the integrated support provided by this core. Administrative and financial organization and supervision are crucial in making possible high quality, productive research, and this core provides this necessary support. Statistical support, trial design, data management and protocol review are essential to the success of the grant's research efforts.
|Feng, Mary; Suresh, Krithika; Schipper, Matthew J et al. (2018) Individualized Adaptive Stereotactic Body Radiotherapy for Liver Tumors in Patients at High Risk for Liver Damage: A Phase 2 Clinical Trial. JAMA Oncol 4:40-47|
|Owen, Daniel Rocky; Boonstra, Phillip S; Viglianti, Benjamin L et al. (2018) Modeling Patient-Specific Dose-Function Response for Enhanced Characterization of Personalized Functional Damage. Int J Radiat Oncol Biol Phys 102:1265-1275|
|Deist, Timo M; Dankers, Frank J W M; Valdes, Gilmer et al. (2018) Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys 45:3449-3459|
|Johansson, Adam; Balter, James; Cao, Yue (2018) Rigid-body motion correction of the liver in image reconstruction for golden-angle stack-of-stars DCE MRI. Magn Reson Med 79:1345-1353|
|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|
Showing the most recent 10 out of 289 publications