Cancer treatment courses which rely on imaging and spatially-dependent therapy involve making multiple treatment decisions (e.g., radiotherapy alone, radiotherapy plus chemotherapy, induction chemotherapy) over time. These decisions depend on complex factors, including the tumor location with respect to sensitive organs and its response to treatment, laboratory data, toxicity, anticipated side effects and survival probability. In this project, we design and develop novel statistical methodology for dynamic and personalized treatment decisions with specific application to head and neck cancer radiotherapy planning. The empirically-derived treatment rules developed in this project have the potential to improve the standard of care (i.e., treatment plans chosen by the tumor treatment board) and the quality of life of surviving patients. The methods developed in the proposal may be used to derive optimal treatment strategies across not only a variety of spatially-dependent cancer diagnoses, but also other chronic conditions including mental health disorders, substance abuse diseases, or diabetes, that require making multiple decisions that must weigh the tradeoffs between efficacy and toxicity. This Big Data collaboration effectively bridges the quantitative sciences with biomedicine, and provides quantitative techniques for leveraging the largest existing repository of head and neck cancer data in the country.

Public Health Relevance

The statistical methodology developed in this project will improve the standard of care and the quality of life of surviving head and neck cancer patients. These quantitative techniques leverage the largest existing repository of head and neck cancer data in the country, advancing our understanding of human health and disease.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA225190-03
Application #
9762879
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Obcemea, Ceferino H
Project Start
2017-09-01
Project End
2020-08-31
Budget Start
2019-09-01
Budget End
2020-08-31
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Illinois at Chicago
Department
Biostatistics & Other Math Sci
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
098987217
City
Chicago
State
IL
Country
United States
Zip Code
60612
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