Of all cancer patients initially diagnosed with localized disease and treated with radiation therapy, approximately 45% eventually develop local recurrence and/or distant metastases and succumb to their tumors. Based on the clinical observations that the majority of treatment failures after radiation therapy manifest themselves with local relapses as the first sign, improved local tumor control will enhance long-term survival. The desire and need to improve local control rates has spurred many efforts in exploring conformal radiation therapy techniques. The main goal of these approaches is to conform a high-dose volume to the clinical target while concomitantly limiting the dose to surrounding normal tissues within their tolerances. Based on calculated dose distributions of computer optimized treatment plans, the technique could offer potentially significant improvements over the conventional treatment techniques for many tumor sites. However, there is little evidence that is derived by the computer can actually be delivered to the patient. On the contrary, there is considerable evidence suggesting that it cannot. There are many treatment uncertainties that degrade tumor dose conformity. These uncertainties include organ shape and location changes during the course of the treatment, patient setup variations, and dosimetric errors in physical procedures, etc. The applicants hypothesize that the full therapeutic benefit of conformal therapy in improving local control can be realized only if the effect of the treatment uncertainties are incorporated in the optimization of the treatment parameters. The applicants propose (1) to study the effects of different treatment uncertainties on dosimetric parameters of conformal therapy treatments through measurements and computer predictions, (2) to develop a treatment uncertainty compensating optimization model that minimizes the effects of treatment uncertainties in conformal therapy, (3) to verify with phantom and patient measurements the validity of the new optimization model. The project will be carried out in four stages: (1) the treatment plan optimization model implemented at the applicant's institution will be refined to facilitate this study, (2) a retrospective study will be conducted to model different types of treatment uncertainties, (3) the uncertainty models will be incorporated in the optimization process to minimize the effects of treatment uncertainties, and (4) dosimetry verification of the new optimization model will be performed with phantom and patient measurements.
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