The Moiseenko Lab focuses on outcomes data analysis, functional imaging, Monte Carlo simulations, propagation of uncertainties associated with radiation therapy and modeling of tumor and normal tissue response to radiation. Dr. Moiseenko is the author of over 90 peer-reviewed articles and book chapters and contributor to AAPM task group and AAPM/ASTRO working group reports.

Outcomes Data Analysis

Normal tissues are unavoidably irradiated during the course of cancer radiation therapy. To avoid complications in normal tissues we need to know how risk of complications depends on dose distribution in organs at risk. My studies of normal tissue response include both simple statistical analysis of outcomes data to find the best predictors associated with the risk of complications, and a more mechanistic approach in attempt to find mechanisms underlying development of radiation-induced complications.

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Functional Imaging For Radiation Therapy Optimization

Normal tissue toxicity puts strict limits on dose escalation, use of hypofractionation or concurrent chemo. Routinely used planning objectives are based on dose-volume histograms and are void of 3D information. Specifically they do not differentiate between regions of normal tissue which carry high/ low functional burden or may/may not be responsible for function recovery. Similarly, tumor is not a homogenous conglomerate of cells. Level of hypoxia, tumor cell density, proliferation rate vary through the tumor volume. Use of functional imaging allows us to optimize radiotherapy plans to preserve normal tissue function and maximize probability of local control.

Monte Carlo Simulations

Treatment planning systems are known to have limitation on how they treat radiation transport through the matter. These limitations may be consequential for rapidly gaining prominence stereotactic body radiation therapy (SBRT). Because of high dose gradients to spare critical organs, e.g., spinal cord, inaccuracies in dose calculation have a potential to cause morbidity in patients receiving SBRT. Monte Carlo is known as a gold standard for dose calculation. New radiotherapy protocols, in particular SBRT, need to be benchmarked against Monte Carlo and dosimetric consequences of using dose calculation engines from treatment planning systems have to be assessed.