Patient-specific microdosimetry: a proof of concept

Microscopic energy deposition distributions from ionizing radiation vary depending on biological target size and are used to predict the biological effects of an irradiation. Ionizing radiation is thought to kill cells or inhibit the cell cycle mainly by damaging DNA in the cell nucleus. The size of cells and nuclei depends on tissue type, cell cycle, and malignancy, all of which vary between patients. The aim of this study was to develop methods to perform patient-specific microdosimetry, that being, determining microdosimetric quantities in volumes that correspond to the sizes of cells and nuclei observed in a patient's tissue. A histopathological sample extracted from a stage I lung adenocarcinoma patient was analyzed. A pouring simulation was used to generate a three-dimensional tissue model from cell and nucleus size information determined from the histopathological sample. Microdosimetric distributions including f (y) and d(y) were determined for Co-60, Ir-192, Yb-169 and I-125 in a patient-specific model containing a distribution of cell and nucleus sizes. Fixed radius models and a summation method were compared to the full patient-specific model to evaluate their suitability for fast determination of patient-specific microdosimetric parameters. In the summation method, f(y) from many fixed radii models are summed. Fixed radius models do not provide a close approximation of the full patient-specific model (y) over bar (f) or (y) over bar (d) for the lower energy sources investigated, Yb-169 and I-125. The higher energy sources investigated, Co-60 and Ir-192 are less sensitive to target size variation than Yb-169 and I-125. The summation method yields the most accurate approximation of the full model d(y) for all radioisotopes investigated. The use of a summation method allows for the computation of patient-specific microdosimetric distributions with the computing power of a personal computer. With appropriate biological inputs the microdosimetric distributions computed using these methods can yield a patient-specific relative biological effectiveness as part of a multiscale treatment planning approach.