Cancer treatments are now designed to uniformly treat the """"""""average"""""""" patient. The """"""""safe"""""""" dose is dictated by the most sensitive 5-15% of the patient population. However, this population-based approach is also accompanied by low expectations of tumor control. Similarly, radiation treatments have been designed to treat the tumor uniformly, although we know from biological and clinical studies that tumors have a heterogeneous response to treatment. We hypothesize that a treatment design that Integrates pretreatment patient factors with an adaptation strategy that uses the first part of treatment to assess tumor and normal tissue sensitivity will permit us to optimize therapy for the individual patient rather than giving a population-averaged treatment that is likely to be less effective. Thi proposal comprises 4 scientific Projects and 4 shared resource Cores within an integrated plan of action: Project 1 and 2 will focus on tumors within the liver and lung. These two sites were chosen for clinical demonstration as their current control rates are low and, as the volume of the harboring normal tissue organ irradiated is the critical factor in toxicity, they offer a great opportunity for adaptation. We will use physiological imaging and other methods to individualize dose redistribution in normal tissues to lower toxicity while also heterogeneously irradiating resistant tumor sub-volumes to improve outcome. Project 3 will establish the spatial and temporal precision of imaging-based methods for both tumor targeting and normal tissue response. Project 4 will develop, investigate, and improve decision support tools to take advantage of predictive models for adaptive therapy patient management. Core A provides administrative and statistical support, Core B will support the clinical therapy planning and delivery, Core C will provide analysis of all Imaging data and Core D will handle software design. We feel that we are developing a new paradigm for radiation therapy and recognize only a few other programs in the world with our ability to develop a system that combines biological assays of toxicity, imaging, planning, delivery, and clinical leadership to safely adapt therapy based on each individual's characteristics and response to therapy.

Public Health Relevance

Radiation therapy can cure patients with localized cancer, but a major limitation is that our treatments are based on the average patient. This program project will establish a framework for optimizing therapy based on the responses of individual patients. The methods developed and tested here will contribute to changing the paradigm within which cancer therapy is practiced, thus improving the efficacy and the safety of radiation therapy for al patients.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
2P01CA059827-16A1
Application #
8609639
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Deye, James
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
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
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

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