Accurate and complete knowledge of the power deposition and HT induced temperature distribution will aid clinical personnel in maximizing the delivery of the HT while minimizing complications. Unfortunately, such knowledge is presently unavailable. In the case of the temperature distribution, invasive sparsely sampled temperatures are typically acquired but we have shown that descriptors of the temperature distribution that rely on sparsely sampled temperatures can be biased and thus unreliable. Patient specific anatomic features make it difficult to know, a priori, the power deposition and thus its optimal delivery not possible. Numerical modeling techniques are one possible solution for overcoming these problems. During the previous funding period, we developed an FE numerical method and associated gridding algorithm to routinely compute patient specific power deposition patterns induced with a regional RF device. We verified the accuracy of the method using data obtained from phantom and clinical treatments. We have also shown, in preliminary studies, that improved temperature distributions are obtained when this numerical method is employed. Thus, we are proposing to improve the HT induced temperature distributions through the use of numerical modeling methods. Hypothesis: Prospective modeling of the EM induced power deposition will yield improved HT temperature distributions. Application of numerical modeling methods to estimate the complete temperature distribution is a more difficult and complex problem primarily due to the need to mathematically approximate the blood flow. We have shown it is both possible and feasible to reconstruct the HT induced temperature distribution using simplifying assumptions. However, we know that additional vascular data will be necessary to routinely and accurately reconstruct the complete temperature distribution. Thus, we are proposing to improve and evaluate the accuracy of our existing models of biothermal energy transport when used to reconstruct the temperatures. Hypothesis: Retrospective reconstruction of HT induced temperature distributions will be accurate and robust. Finally, because the HT induced temperature distribution is heterogeneous, empirical development of a relationship between cell survival and response is very difficult especially when confounding influences such as radiation dose and microenvironmental factors are considered. We believe that mathematical modeling methods have the potential to aid in the development and understanding these relationships. Thus, we are proposing to explore the use of mathematical modeling to estimate tumor cell survival following HT. Hypothesis: It will be possible to develop numerical models that estimate the relative importance of variations of factors that contribute to the degree of cytotoxicity from a thermoradiotherapeutic treatment.
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