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Core Research Technologies
Microdosimetry
At a scale comparable to a cell nucleus, where the biological damage induced by radiation occurs, the energy deposition is affected by stochastic fluctuations and cannot be accurately described with macroscopic mean values, such as the dose or the linear energy transfer (LET). Microdosimetry has been proposed as a methodology for characterizing radiation field quality in micrometer-sized volumes and represents the bridge between physical characteristics of the radiation and biological outcomes. It has been exploited by radiobiological models, such as the Microdosimetric Kinetic Model (MKM), for predicting cell survival and relative biological effectiveness (RBE). We are using microdosimetry to characterize the in- and out-of-field radiation field in particle therapy, and use it as a tool for assessing biological outcomes. We are also developing a new microdosimeter, which has unique features than existing detectors and will allow for a superior characterization of radiation quality in particle therapy.
Radiobiological Models
Radiobiological models for cell survival and RBE prediction are a key part of treatment planning systems (TPS) for calculating the dose delivered. An inaccurate determination of the RBE can lead to an underdosage to the tumor, limiting treatment success, or an overdosage to normal tissue, increasing the probability of complications. The main limitation shared by all existing models is the assumption that all variables follow a Poisson distribution and thus can be described by their mean. This assumption neglects stochastic fluctuations of energy deposition both from cell to cell and from dose fractionation (time variable), which can be especially significant in highly mixed radiation fields that occur at the beam edges and in the distal region. We have developed the general stochastic microdosimetry-based kinetic model (GSM2), that consider the stochastic behavior of both energy deposition and cell inactivation, providing the radiation response of the irradiated tissue, and predict both cell survival and RBE. We are currently conducting cell survival experiments here at the University of Miami Dwoskin Proton Therapy Center to validate GSM2. Cells are irradiated with known doses of proton radiation and cell survival is obtained. The survival is used to calculate the RBE, which is then compared to the model's predictions for validation.
Artificial Intelligence applied to Radiation Oncology
Among the major revolution of our century, artificial intelligence and data analysis are without any doubt among the most relevant. Machine and Deep Learning (ML and DL) models are nowadays systematically used in many fields. We recently started gaining interest in application of machine and deep learning techniques applied to medical physics and radiation oncology. We have implemented a machine learning model to track particles in microdosimetry to overcome experimental limitations of our new detector. We have also developed a fully machine and deep learning algorithm to predict biological effectiveness of a wide range of ions relevant both for radiotherapy and for radioprotection (ANAKIN).
Ongoing Projects
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Normal Tissue Toxicities
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Treatment Plan Optimization
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Treatment Plan Verification