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The Statistical NTCP and TCP Models in the Proton Therapy

T Frometa

S Jang1 , T Frometa2*, A Pyakuryal3 , T Sio4 , R Piseaux5 , S Acosta6 , K Ocana7 , (1) Princeton Radiation Oncology, Princeton Radiation Oncology, Princeton Radiation Oncology, (2) Theoretical and Computational Mtehods Working Group of IMAG, Santiago De Cuba, Santiago de Cuba, (3) National Cancer Institute, Rockville, MD, (4) MDAnderson Cancer Center, Houston, Texas, (5) Northwestern University, Chicago, IL, (6) Provincial Hospital of Santiago de Cuba, Santiago De Cuba, Santiago de Cuba, (7) Faculty of Electrics of the University of Oriente, Santiago De Cuba, Santiago de Cuba


SU-F-J-187 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: The statistical models (SM) are typically used as a subjective description of a population for which there is only limited sample data, and especially in cases where the relationship between variables is known. The normal tissue complications and tumor control are frequently stochastic effects in the Radiotherapy (RT). Based on probabilistic treatments, it recently has been formulated new NTCP and TCP models for the RT. Investigating the particular requirements for their clinical use in the proton therapy (PT) is the goal of this work.

Methods: The SM can be used as phenomenological or mechanistic models. The former way allows fitting real data and getting theirparameters. In the latter one, we should do efforts for determining the parameters through the acceptable estimations, measurements, and/or simulation experiments. Experimental methodologies for determination of the parameters have been developed from the fraction cells surviving the proton irradiation curves in tumor and OAR, and precise RBE models are used for calculating the variable of effective dose. As the executions of these methodologies have a high costs, so we have developed computer tools enable to perform simulation experiments as complement to limitations of the real ones.

Results: The requirements for the use of the SM in the PT, such as validation and improvement of the elaborated and existent methodologies for determining the SM parameters and effective dose respectively, were determined.

Conclusion: The SM realistically simulates the main processes in the PT, and for this reason these can be implemented in this therapy, which are simples, computable and they have other advantages over some current models. It has been determined some negative aspects for some currently used probabilistic models in the RT, like the LKB NTCP and others derived from logistic functions; which can be improved with the proposed methods in this study.

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