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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
2P01CA059827-16A1
Application #
8609643
Study Section
Special Emphasis Panel (ZCA1-RPRB-C (O1))
Project Start
1997-02-01
Project End
2019-04-30
Budget Start
2014-05-15
Budget End
2015-04-30
Support Year
16
Fiscal Year
2014
Total Cost
$411,639
Indirect Cost
$144,858
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
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
Sapir, Eli; Tao, Yebin; Schipper, Matthew J et al. (2018) Stereotactic Body Radiation Therapy as an Alternative to Transarterial Chemoembolization for Hepatocellular Carcinoma. Int J Radiat Oncol Biol Phys 100:122-130

Showing the most recent 10 out of 289 publications