Patient-specific microdosimetry: a proof of concept

Abstract

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 and were determined for and 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 or for the lower energy sources investigated, and The higher energy sources investigated, and are less sensitive to target size variation than and The summation method yields the most accurate approximation of the full model 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.

GPU-accelerated calculation of proton microdosimetric spectra as a function of target size, proton energy, and bounding volume size

“…For both voxel sizessimulated, we calculated microdosimetricspectra in 39 different spherical target sizessuch that predictionsof energydeposition probabilitiesin targetswhose sizes correspondto biologicaltargetsof interest can be made. One such implementation has been described in past work by DeCunha et al (2021aDeCunha et al ( , 2021b, which demonstratedthe feasibility of performingpatient-specific or tissue-specific microdosimetry (DeCunha et al 2021b(DeCunha et al , 2021a. By weighting a series of microdosimetric spectra in various target sizes accordingto a patient's known cell and nucleus-sizedistributions, a patient-specific microdosimetric distribution can be calculated.…”

Haralick texture analysis for microdosimetry: characterization of Monte Carlo generated 3D specific energy distributions

“…The significance of microdosimetric investigations is demonstrated by its potential for supporting development of novel treatment modalities (e.g. nanotherapy involving gold nanoparticles (Zygmanski andSajo 2016, Sakata et al 2018)), and new understandings of radiobiological effects of radiation exposure (Pater et al 2016, DeCunha et al 2021, Bertolet et al 2021.…”

Haralick texture analysis for characterization of specific energy and absorbed dose distributions across cellular to patient length scales

“…The significance of microdosimetric investigations is demonstrated by its potential for supporting development of novel treatment modalities (e.g., nanotherapy involving gold nanoparticles 23,133 ), and new understandings of radiobiological effects of radiation exposure. [134][135][136][137] Despite the importance of microdosimetry, analyses of microdosimetric distributions often rely on first-order statistics (e.g., mean and standard deviation) 19,20 with little-to-no consideration of the spatial distribution of data. Chapter 2 introduced Haralick texture analysis for the quantitative characterization of dosimetric distributions across micro-to macroscopic length scales, focusing on examples to illustrate proof-of-concept.…”