The effort of the proposed project is directed towards (1) resolving a controversy about two main approaches for optimizing intensity-modulated proton therapy (IMPT), and (2) testing the validity of assumptions and ap- proximations in the proton biological effect computation models. The current approach, used almost univer- sally in the clinics, is to assume that relative biological effectiveness (RBE) of protons to be 1.1. In reality, RBE is a complex variable function of dose, linear energy transfer (LET), tissue and cell type, endpoint and other parameters. This fact is now being realized increasingly and there have been efforts to define IMPT optimiza- tion criteria in terms of variable RBE-weighted dose. RBE may be computed using one of many models, which make questionable approximations and assumptions and the parameters of tissue response and their depend- ence on LET are uncertain. Consequently, the computed RBE and the results of IMPT optimization based on RBE-weighted dose have corresponding uncertainties. Therefore, many researchers and clinical practitioners are advocating an alternative approach in which IMPT optimization and evaluation are based on the criteria that attempt to minimize LET in normal tissues and/or maximize it in the target volume while maintaining the physical (or the constant RBE=1.1 weighted) dose distributions to be the same as, or similar to, the current clinical approach. However, among the limitations of the LET-based approach is that biological effect does not depend upon LET alone, and increasing or decreasing LET in a tumor or normal tissue would not correctly re- flect its clinical consequences. While it is commonly acknowledged that current clinical approach is subopti- mal, it is unclear which of the two alternative approaches would lead to safer and more effective IMPT treat- ments. In addition to this controversy, both approaches, explicitly or implicitly, employ average of LET contri- butions from all protons of highly disparate energies. We assert that this approximation tends to underestimate the biological effect. Our hypothesis is that criteria for optimizing IMPT must incorporate variable RBE, taking into account the non-linear dependence of RBE on LET spectra and the physical and biological uncertainties to produce biologically effective dose distributions that would be safer and more effective compared to the LET- based approach and, especially, the current clinical approach of using RBE of 1.1. We propose three specific aims to test this hypothesis: (1) Investigate the pros and cons of optimization criteria based on LET vs. biologi- cally effective dose computed using current models. (2) Explore the potential of biological effect optimization (instead of optimization of biologically-effective dose) based on new or reparametrized models, including those that eliminate LET averaging. (3) Investigate the impact of physical uncertainties on biological effects through robust optimization incorporating physical uncertainties. The innovative optimization approaches and findings from this project are aimed at immediate translation into clinical practice of IMPT to improve therapeutic ratio and to facilitate clinical trials and research.

Public Health Relevance

The goals of this project are (1) to resolve a controversy about two main approaches for optimizing intensity- modulated proton therapy (IMPT), potentially the most effective form of radiotherapy, and (2) to test the validity and consequences of assumptions and approximations in the proton biological effect computation models. We propose to achieve these goals by testing the limits of the current approaches and models to compute biological effects of protons by applying them to conduct computer simulated ?virtual clinical trials? for selected cohorts of patients. We will develop novel approaches and models that overcome current limitations and apply them to the same cohorts of patients in similar virtual clinical trials and compare the results of the two sets of trials to determine the potential for improvement in therapeutic ratio.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Small Research Grants (R03)
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Special Emphasis Panel (ZCA1)
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Prasanna, Pat G
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University of Texas MD Anderson Cancer Center
United States
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