Journal Articles
2021
DeCunha, Joseph M.; Poole, Christopher M.; Vallières, Martin; Torres, Jose; Camilleri-Broët, Sophie; Rayes, Roni F.; Spicer, Jonathan D.; Enger, Shirin A.
Development of patient-specific 3D models from histopathological samples for applications in radiation therapy Journal Article
In: Physica medica: PM: an international journal devoted to the applications of physics to medicine and biology: official journal of the Italian Association of Biomedical Physics (AIFB), vol. 81, pp. 162–169, 2021, ISSN: 1724-191X.
Abstract | Links | BibTeX | Tags: Algorithms, Cell Nucleus, Cellular dosimetry, Histopathology, Humans, Microdosimetry, Patient-specific, Radiometry
@article{decunha_development_2021,
title = {Development of patient-specific 3D models from histopathological samples for applications in radiation therapy},
author = {Joseph M. DeCunha and Christopher M. Poole and Martin Vallières and Jose Torres and Sophie Camilleri-Broët and Roni F. Rayes and Jonathan D. Spicer and Shirin A. Enger},
doi = {10.1016/j.ejmp.2020.12.009},
issn = {1724-191X},
year = {2021},
date = {2021-01-01},
journal = {Physica medica: PM: an international journal devoted to the applications of physics to medicine and biology: official journal of the Italian Association of Biomedical Physics (AIFB)},
volume = {81},
pages = {162--169},
abstract = {The biological effects of ionizing radiation depend on the tissue, tumor type, radiation quality, and patient-specific factors. Inter-patient variation in cell/nucleus size may influence patient-specific dose response. However, this variability in dose response is not well investigated due to lack of available cell/nucleus size data. The aim of this study was to develop methods to derive cell/nucleus size distributions from digital images of 2D histopathological samples and use them to build digital 3D models for use in cellular dosimetry. Nineteen of sixty hematoxylin and eosin stained lung adenocarcinoma samples investigated passed exclusion criterion to be analyzed in the study. A difference of gaussians blob detection algorithm was used to identify nucleus centers and quantify cell spacing. Hematoxylin content was measured to determine nucleus radius. Pouring simulations were conducted to generate one-hundred 3D models containing volumes of equivalent cell spacing and nuclei radius to those in histopathological samples. The nuclei radius distributions of non-tumoral and cancerous regions appearing in the same slide were significantly different (p textless 0.01) in all samples analyzed. The median nuclear-cytoplasmic ratio was 0.36 for non-tumoral cells and 0.50 for cancerous cells. The average cellular and nucleus packing densities in the 3D models generated were 65.9% (SD: 1.5%) and 13.3% (SD: 0.3%) respectively. Software to determine cell spacing and nuclei radius from histopathological samples was developed. 3D digital tissue models containing volumes with equivalent cell spacing, nucleus radius, and packing density to cancerous tissues were generated.},
keywords = {Algorithms, Cell Nucleus, Cellular dosimetry, Histopathology, Humans, Microdosimetry, Patient-specific, Radiometry},
pubstate = {published},
tppubtype = {article}
}
The biological effects of ionizing radiation depend on the tissue, tumor type, radiation quality, and patient-specific factors. Inter-patient variation in cell/nucleus size may influence patient-specific dose response. However, this variability in dose response is not well investigated due to lack of available cell/nucleus size data. The aim of this study was to develop methods to derive cell/nucleus size distributions from digital images of 2D histopathological samples and use them to build digital 3D models for use in cellular dosimetry. Nineteen of sixty hematoxylin and eosin stained lung adenocarcinoma samples investigated passed exclusion criterion to be analyzed in the study. A difference of gaussians blob detection algorithm was used to identify nucleus centers and quantify cell spacing. Hematoxylin content was measured to determine nucleus radius. Pouring simulations were conducted to generate one-hundred 3D models containing volumes of equivalent cell spacing and nuclei radius to those in histopathological samples. The nuclei radius distributions of non-tumoral and cancerous regions appearing in the same slide were significantly different (p textless 0.01) in all samples analyzed. The median nuclear-cytoplasmic ratio was 0.36 for non-tumoral cells and 0.50 for cancerous cells. The average cellular and nucleus packing densities in the 3D models generated were 65.9% (SD: 1.5%) and 13.3% (SD: 0.3%) respectively. Software to determine cell spacing and nuclei radius from histopathological samples was developed. 3D digital tissue models containing volumes with equivalent cell spacing, nucleus radius, and packing density to cancerous tissues were generated.2016
Pater, Piotr; Bäckstöm, Gloria; Villegas, Fernanda; Ahnesjö, Anders; Enger, Shirin A.; Seuntjens, Jan; Naqa, Issam El
Proton and light ion RBE for the induction of direct DNA double strand breaks Journal Article
In: Medical Physics, vol. 43, no. 5, pp. 2131–2140, 2016, ISSN: 2473-4209, (_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.4944870).
Abstract | Links | BibTeX | Tags: biological effects of ionising particles, biomolecular effects of radiation, Cell Nucleus, cell nucleus model, cellular effects of radiation, DNA, DNA double-strand breaks, Dosimetry, Dosimetry/exposure assessment, Energy transfer, Genomics, Ion beams, Ion radiation effects, Monte Carlo calculations, Monte Carlo methods, Monte Carlo simulations, Monte Carlo track structure, Protons, RBE, Schottky barriers, Scintigraphy
@article{pater_proton_2016b,
title = {Proton and light ion RBE for the induction of direct DNA double strand breaks},
author = {Piotr Pater and Gloria Bäckstöm and Fernanda Villegas and Anders Ahnesjö and Shirin A. Enger and Jan Seuntjens and Issam El Naqa},
url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1118/1.4944870},
doi = {10.1118/1.4944870},
issn = {2473-4209},
year = {2016},
date = {2016-01-01},
urldate = {2021-09-07},
journal = {Medical Physics},
volume = {43},
number = {5},
pages = {2131--2140},
abstract = {Purpose: To present and characterize a Monte Carlo (MC) tool for the simulation of the relative biological effectiveness for the induction of direct DNA double strand breaks () for protons and light ions. Methods: The MC tool uses a pregenerated event-by-event tracks library of protons and light ions that are overlaid on a cell nucleus model. The cell nucleus model is a cylindrical arrangement of nucleosome structures consisting of 198 DNA base pairs. An algorithm relying on k-dimensional trees and cylindrical symmetries is used to search coincidences of energy deposition sites with volumes corresponding to the sugar–phosphate backbone of the DNA molecule. Strand breaks (SBs) are scored when energy higher than a threshold is reached in these volumes. Based on the number of affected strands, they are categorized into either single strand break (SSB) or double strand break (DSB) lesions. The number of SBs composing each lesion (i.e., its size) is also recorded. is obtained by taking the ratio of DSB yields of a given radiation field to a 60Co field. The MC tool was used to obtain SSB yields, DSB yields, and as a function of linear energy transfer (LET) for protons (1H+), 4He2+, 7Li3+, and 12C6+ ions. Results: For protons, the SSB yields decreased and the DSB yields increased with LET. At ≈24.5 keV μm−1, protons generated 15% more DSBs than 12C6+ ions. The varied between 1.24 and 1.77 for proton fields between 8.5 and 30.2 keV μm−1, and it was higher for iso-LET ions with lowest atomic number. The SSB and DSB lesion sizes showed significant differences for all radiation fields. Generally, the yields of SSB lesions of sizes ≥2 and the yields of DSB lesions of sizes ≥3 increased with LET and increased for iso-LET ions of lower atomic number. On the other hand, the ratios of SSB to DSB lesions of sizes 2–4 did not show variability with LET nor projectile atomic number, suggesting that these metrics are independent of the radiation quality. Finally, a variance of up to 8% in the DSB yields was observed as a function of the particle incidence angle on the cell nucleus. This simulation effect is due to the preferential alignment of ion tracks with the DNA nucleosomes at specific angles. Conclusions: The MC tool can predict SSB and DSB yields for light ions of various LET and estimate . In addition, it can calculate the frequencies of different DNA lesion sizes, which is of interest in the context of biologically relevant absolute dosimetry of particle beams.},
note = {_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.4944870},
keywords = {biological effects of ionising particles, biomolecular effects of radiation, Cell Nucleus, cell nucleus model, cellular effects of radiation, DNA, DNA double-strand breaks, Dosimetry, Dosimetry/exposure assessment, Energy transfer, Genomics, Ion beams, Ion radiation effects, Monte Carlo calculations, Monte Carlo methods, Monte Carlo simulations, Monte Carlo track structure, Protons, RBE, Schottky barriers, Scintigraphy},
pubstate = {published},
tppubtype = {article}
}
Purpose: To present and characterize a Monte Carlo (MC) tool for the simulation of the relative biological effectiveness for the induction of direct DNA double strand breaks () for protons and light ions. Methods: The MC tool uses a pregenerated event-by-event tracks library of protons and light ions that are overlaid on a cell nucleus model. The cell nucleus model is a cylindrical arrangement of nucleosome structures consisting of 198 DNA base pairs. An algorithm relying on k-dimensional trees and cylindrical symmetries is used to search coincidences of energy deposition sites with volumes corresponding to the sugar–phosphate backbone of the DNA molecule. Strand breaks (SBs) are scored when energy higher than a threshold is reached in these volumes. Based on the number of affected strands, they are categorized into either single strand break (SSB) or double strand break (DSB) lesions. The number of SBs composing each lesion (i.e., its size) is also recorded. is obtained by taking the ratio of DSB yields of a given radiation field to a 60Co field. The MC tool was used to obtain SSB yields, DSB yields, and as a function of linear energy transfer (LET) for protons (1H+), 4He2+, 7Li3+, and 12C6+ ions. Results: For protons, the SSB yields decreased and the DSB yields increased with LET. At ≈24.5 keV μm−1, protons generated 15% more DSBs than 12C6+ ions. The varied between 1.24 and 1.77 for proton fields between 8.5 and 30.2 keV μm−1, and it was higher for iso-LET ions with lowest atomic number. The SSB and DSB lesion sizes showed significant differences for all radiation fields. Generally, the yields of SSB lesions of sizes ≥2 and the yields of DSB lesions of sizes ≥3 increased with LET and increased for iso-LET ions of lower atomic number. On the other hand, the ratios of SSB to DSB lesions of sizes 2–4 did not show variability with LET nor projectile atomic number, suggesting that these metrics are independent of the radiation quality. Finally, a variance of up to 8% in the DSB yields was observed as a function of the particle incidence angle on the cell nucleus. This simulation effect is due to the preferential alignment of ion tracks with the DNA nucleosomes at specific angles. Conclusions: The MC tool can predict SSB and DSB yields for light ions of various LET and estimate . In addition, it can calculate the frequencies of different DNA lesion sizes, which is of interest in the context of biologically relevant absolute dosimetry of particle beams.2015
Poole, Christopher M.; Ahnesjö, Anders; Enger, Shirin A.
Determination of subcellular compartment sizes for estimating dose variations in radiotherapy Journal Article
In: Radiation Protection Dosimetry, vol. 166, no. 1-4, pp. 361–364, 2015, ISSN: 1742-3406.
Abstract | Links | BibTeX | Tags: Algorithms, Breast Neoplasms, Cell Nucleus, Computer Simulation, Computer-Assisted, ErbB-2, Female, Humans, Image Processing, Imaging, Immunoenzyme Techniques, Male, Monte Carlo Method, Prostatic Neoplasms, Radiotherapy Dosage, Radiotherapy Planning, Receptor, Signal Processing, Subcellular Fractions, Three-Dimensional
@article{poole_determination_2015,
title = {Determination of subcellular compartment sizes for estimating dose variations in radiotherapy},
author = {Christopher M. Poole and Anders Ahnesjö and Shirin A. Enger},
doi = {10.1093/rpd/ncv305},
issn = {1742-3406},
year = {2015},
date = {2015-09-01},
journal = {Radiation Protection Dosimetry},
volume = {166},
number = {1-4},
pages = {361--364},
abstract = {The variation in specific energy absorbed to different cell compartments caused by variations in size and chemical composition is poorly investigated in radiotherapy. The aim of this study was to develop an algorithm to derive cell and cell nuclei size distributions from 2D histology samples, and build 3D cellular geometries to provide Monte Carlo (MC)-based dose calculation engines with a morphologically relevant input geometry. Stained and unstained regions of the histology samples are segmented using a Gaussian mixture model, and individual cell nuclei are identified via thresholding. Delaunay triangulation is applied to determine the distribution of distances between the centroids of nearest neighbour cells. A pouring simulation is used to build a 3D virtual tissue sample, with cell radii randomised according to the cell size distribution determined from the histology samples. A slice with the same thickness as the histology sample is cut through the 3D data and characterised in the same way as the measured histology. The comparison between this virtual slice and the measured histology is used to adjust the initial cell size distribution into the pouring simulation. This iterative approach of a pouring simulation with adjustments guided by comparison is continued until an input cell size distribution is found that yields a distribution in the sliced geometry that agrees with the measured histology samples. The thus obtained morphologically realistic 3D cellular geometry can be used as input to MC-based dose calculation programs for studies of dose response due to variations in morphology and size of tumour/healthy tissue cells/nuclei, and extracellular material.},
keywords = {Algorithms, Breast Neoplasms, Cell Nucleus, Computer Simulation, Computer-Assisted, ErbB-2, Female, Humans, Image Processing, Imaging, Immunoenzyme Techniques, Male, Monte Carlo Method, Prostatic Neoplasms, Radiotherapy Dosage, Radiotherapy Planning, Receptor, Signal Processing, Subcellular Fractions, Three-Dimensional},
pubstate = {published},
tppubtype = {article}
}
The variation in specific energy absorbed to different cell compartments caused by variations in size and chemical composition is poorly investigated in radiotherapy. The aim of this study was to develop an algorithm to derive cell and cell nuclei size distributions from 2D histology samples, and build 3D cellular geometries to provide Monte Carlo (MC)-based dose calculation engines with a morphologically relevant input geometry. Stained and unstained regions of the histology samples are segmented using a Gaussian mixture model, and individual cell nuclei are identified via thresholding. Delaunay triangulation is applied to determine the distribution of distances between the centroids of nearest neighbour cells. A pouring simulation is used to build a 3D virtual tissue sample, with cell radii randomised according to the cell size distribution determined from the histology samples. A slice with the same thickness as the histology sample is cut through the 3D data and characterised in the same way as the measured histology. The comparison between this virtual slice and the measured histology is used to adjust the initial cell size distribution into the pouring simulation. This iterative approach of a pouring simulation with adjustments guided by comparison is continued until an input cell size distribution is found that yields a distribution in the sliced geometry that agrees with the measured histology samples. The thus obtained morphologically realistic 3D cellular geometry can be used as input to MC-based dose calculation programs for studies of dose response due to variations in morphology and size of tumour/healthy tissue cells/nuclei, and extracellular material.2012
Enger, Shirin A.; Ahnesjö, Anders; Verhaegen, Frank; Beaulieu, Luc
Dose to tissue medium or water cavities as surrogate for the dose to cell nuclei at brachytherapy photon energies Journal Article
In: Physics in Medicine and Biology, vol. 57, no. 14, pp. 4489–4500, 2012, ISSN: 1361-6560.
Abstract | Links | BibTeX | Tags: Brachytherapy, Cell Line, Cell Nucleus, Humans, Monte Carlo Method, Photons, Radiation Dosage, Radiotherapy Dosage, Water
@article{enger_dose_2012,
title = {Dose to tissue medium or water cavities as surrogate for the dose to cell nuclei at brachytherapy photon energies},
author = {Shirin A. Enger and Anders Ahnesjö and Frank Verhaegen and Luc Beaulieu},
doi = {10.1088/0031-9155/57/14/4489},
issn = {1361-6560},
year = {2012},
date = {2012-07-01},
journal = {Physics in Medicine and Biology},
volume = {57},
number = {14},
pages = {4489--4500},
abstract = {It has been suggested that modern dose calculation algorithms should be able to report absorbed dose both as dose to the local medium, D(m,m,) and as dose to a water cavity embedded in the medium, D(w,m), using conversion factors from cavity theory. Assuming that the cell nucleus with its DNA content is the most important target for biological response, the aim of this study is to investigate, by means of Monte Carlo (MC) simulations, the relationship of the dose to a cell nucleus in a medium, D(n,m,) to D(m,m) and D(w,m), for different combinations of cell nucleus compositions and tissue media for different photon energies used in brachytherapy. As D(n,m) is very impractical to calculate directly for routine treatment planning, while D(m,m) and D(w,m) are much easier to obtain, the questions arise which one of these quantities is the best surrogate for D(n,m) and which cavity theory assumptions should one use for its estimate. The Geant4.9.4 MC code was used to calculate D(m,m,) D(w,m) and D(n,m) for photon energies from 20 (representing the lower energy end of brachytherapy for ¹⁰³Pd or ¹²⁵I) to 300 keV (close to the mean energy of (¹⁹²Ir) and for the tissue media adipose, breast, prostate and muscle. To simulate the cell and its nucleus, concentric spherical cavities were placed inside a cubic phantom (10 × 10 × 10 mm³). The diameter of the simulated nuclei was set to 14 µm. For each tissue medium, three different setups were simulated; (a) D(n,m) was calculated with nuclei embedded in tissues (MC-D(n,m)). Four different published elemental compositions of cell nuclei were used. (b) D(w,m) was calculated with MC (MC-D(w,m)) and compared with large cavity theory calculated D(w,m) (LCT-D(w,m)), and small cavity theory calculated D(w,m) (SCT-D(w,m)). (c) D(m,m) was calculated with MC (MC-D(m,m)). MC-D(w,m) is a good substitute for MC-D(n,m) for all photon energies and for all simulated nucleus compositions and tissue types. SCT-D(w,m) can be used for most energies in brachytherapy, while LCT-D(w,m) should only be considered for source spectra well below 50 keV, since contributions to the absorbed dose inside the nucleus to a large degree stem from electrons released in the surrounding medium. MC-D(m,m) is not an appropriate substitute for MC-D(n,m) for the lowest photon energies for adipose and breast tissues. The ratio of MC-D(m,m) to MC-D(n,m) for adipose and breast tissue deviates from unity by 34% and 15% respectively for the lowest photon energy (20 keV), whereas the ratio is close to unity for higher energies. For prostate and muscle tissue MC-D(m,m) is a good substitute for MC-D(n,m). However, for all photon energies and tissue types the nucleus composition with the highest hydrogen content behaves differently than other compositions. Elemental compositions of the tissue and nuclei affect considerably the absorbed dose to the cell nuclei for brachytherapy sources, in particular those at the low-energy end of the spectrum. Thus, there is a need for more accurate data for the elemental compositions of tumours and healthy cells. For the nucleus compositions and tissue types investigated, MC-D(w,m) is a good substitute to MC-D(n,m) for all simulated photon energies. Whether other studied surrogates are good approximations to MC-D(n,m) depends on the target size, target composition, composition of the surrounding tissue and photon energy.},
keywords = {Brachytherapy, Cell Line, Cell Nucleus, Humans, Monte Carlo Method, Photons, Radiation Dosage, Radiotherapy Dosage, Water},
pubstate = {published},
tppubtype = {article}
}
It has been suggested that modern dose calculation algorithms should be able to report absorbed dose both as dose to the local medium, D(m,m,) and as dose to a water cavity embedded in the medium, D(w,m), using conversion factors from cavity theory. Assuming that the cell nucleus with its DNA content is the most important target for biological response, the aim of this study is to investigate, by means of Monte Carlo (MC) simulations, the relationship of the dose to a cell nucleus in a medium, D(n,m,) to D(m,m) and D(w,m), for different combinations of cell nucleus compositions and tissue media for different photon energies used in brachytherapy. As D(n,m) is very impractical to calculate directly for routine treatment planning, while D(m,m) and D(w,m) are much easier to obtain, the questions arise which one of these quantities is the best surrogate for D(n,m) and which cavity theory assumptions should one use for its estimate. The Geant4.9.4 MC code was used to calculate D(m,m,) D(w,m) and D(n,m) for photon energies from 20 (representing the lower energy end of brachytherapy for ¹⁰³Pd or ¹²⁵I) to 300 keV (close to the mean energy of (¹⁹²Ir) and for the tissue media adipose, breast, prostate and muscle. To simulate the cell and its nucleus, concentric spherical cavities were placed inside a cubic phantom (10 × 10 × 10 mm³). The diameter of the simulated nuclei was set to 14 µm. For each tissue medium, three different setups were simulated; (a) D(n,m) was calculated with nuclei embedded in tissues (MC-D(n,m)). Four different published elemental compositions of cell nuclei were used. (b) D(w,m) was calculated with MC (MC-D(w,m)) and compared with large cavity theory calculated D(w,m) (LCT-D(w,m)), and small cavity theory calculated D(w,m) (SCT-D(w,m)). (c) D(m,m) was calculated with MC (MC-D(m,m)). MC-D(w,m) is a good substitute for MC-D(n,m) for all photon energies and for all simulated nucleus compositions and tissue types. SCT-D(w,m) can be used for most energies in brachytherapy, while LCT-D(w,m) should only be considered for source spectra well below 50 keV, since contributions to the absorbed dose inside the nucleus to a large degree stem from electrons released in the surrounding medium. MC-D(m,m) is not an appropriate substitute for MC-D(n,m) for the lowest photon energies for adipose and breast tissues. The ratio of MC-D(m,m) to MC-D(n,m) for adipose and breast tissue deviates from unity by 34% and 15% respectively for the lowest photon energy (20 keV), whereas the ratio is close to unity for higher energies. For prostate and muscle tissue MC-D(m,m) is a good substitute for MC-D(n,m). However, for all photon energies and tissue types the nucleus composition with the highest hydrogen content behaves differently than other compositions. Elemental compositions of the tissue and nuclei affect considerably the absorbed dose to the cell nuclei for brachytherapy sources, in particular those at the low-energy end of the spectrum. Thus, there is a need for more accurate data for the elemental compositions of tumours and healthy cells. For the nucleus compositions and tissue types investigated, MC-D(w,m) is a good substitute to MC-D(n,m) for all simulated photon energies. Whether other studied surrogates are good approximations to MC-D(n,m) depends on the target size, target composition, composition of the surrounding tissue and photon energy.
Journal Articles
2021
DeCunha, Joseph M.; Poole, Christopher M.; Vallières, Martin; Torres, Jose; Camilleri-Broët, Sophie; Rayes, Roni F.; Spicer, Jonathan D.; Enger, Shirin A.
Development of patient-specific 3D models from histopathological samples for applications in radiation therapy Journal Article
In: Physica medica: PM: an international journal devoted to the applications of physics to medicine and biology: official journal of the Italian Association of Biomedical Physics (AIFB), vol. 81, pp. 162–169, 2021, ISSN: 1724-191X.
Abstract | Links | BibTeX | Tags: Algorithms, Cell Nucleus, Cellular dosimetry, Histopathology, Humans, Microdosimetry, Patient-specific, Radiometry
@article{decunha_development_2021,
title = {Development of patient-specific 3D models from histopathological samples for applications in radiation therapy},
author = {Joseph M. DeCunha and Christopher M. Poole and Martin Vallières and Jose Torres and Sophie Camilleri-Broët and Roni F. Rayes and Jonathan D. Spicer and Shirin A. Enger},
doi = {10.1016/j.ejmp.2020.12.009},
issn = {1724-191X},
year = {2021},
date = {2021-01-01},
journal = {Physica medica: PM: an international journal devoted to the applications of physics to medicine and biology: official journal of the Italian Association of Biomedical Physics (AIFB)},
volume = {81},
pages = {162--169},
abstract = {The biological effects of ionizing radiation depend on the tissue, tumor type, radiation quality, and patient-specific factors. Inter-patient variation in cell/nucleus size may influence patient-specific dose response. However, this variability in dose response is not well investigated due to lack of available cell/nucleus size data. The aim of this study was to develop methods to derive cell/nucleus size distributions from digital images of 2D histopathological samples and use them to build digital 3D models for use in cellular dosimetry. Nineteen of sixty hematoxylin and eosin stained lung adenocarcinoma samples investigated passed exclusion criterion to be analyzed in the study. A difference of gaussians blob detection algorithm was used to identify nucleus centers and quantify cell spacing. Hematoxylin content was measured to determine nucleus radius. Pouring simulations were conducted to generate one-hundred 3D models containing volumes of equivalent cell spacing and nuclei radius to those in histopathological samples. The nuclei radius distributions of non-tumoral and cancerous regions appearing in the same slide were significantly different (p textless 0.01) in all samples analyzed. The median nuclear-cytoplasmic ratio was 0.36 for non-tumoral cells and 0.50 for cancerous cells. The average cellular and nucleus packing densities in the 3D models generated were 65.9% (SD: 1.5%) and 13.3% (SD: 0.3%) respectively. Software to determine cell spacing and nuclei radius from histopathological samples was developed. 3D digital tissue models containing volumes with equivalent cell spacing, nucleus radius, and packing density to cancerous tissues were generated.},
keywords = {Algorithms, Cell Nucleus, Cellular dosimetry, Histopathology, Humans, Microdosimetry, Patient-specific, Radiometry},
pubstate = {published},
tppubtype = {article}
}
The biological effects of ionizing radiation depend on the tissue, tumor type, radiation quality, and patient-specific factors. Inter-patient variation in cell/nucleus size may influence patient-specific dose response. However, this variability in dose response is not well investigated due to lack of available cell/nucleus size data. The aim of this study was to develop methods to derive cell/nucleus size distributions from digital images of 2D histopathological samples and use them to build digital 3D models for use in cellular dosimetry. Nineteen of sixty hematoxylin and eosin stained lung adenocarcinoma samples investigated passed exclusion criterion to be analyzed in the study. A difference of gaussians blob detection algorithm was used to identify nucleus centers and quantify cell spacing. Hematoxylin content was measured to determine nucleus radius. Pouring simulations were conducted to generate one-hundred 3D models containing volumes of equivalent cell spacing and nuclei radius to those in histopathological samples. The nuclei radius distributions of non-tumoral and cancerous regions appearing in the same slide were significantly different (p textless 0.01) in all samples analyzed. The median nuclear-cytoplasmic ratio was 0.36 for non-tumoral cells and 0.50 for cancerous cells. The average cellular and nucleus packing densities in the 3D models generated were 65.9% (SD: 1.5%) and 13.3% (SD: 0.3%) respectively. Software to determine cell spacing and nuclei radius from histopathological samples was developed. 3D digital tissue models containing volumes with equivalent cell spacing, nucleus radius, and packing density to cancerous tissues were generated.
2016
Pater, Piotr; Bäckstöm, Gloria; Villegas, Fernanda; Ahnesjö, Anders; Enger, Shirin A.; Seuntjens, Jan; Naqa, Issam El
Proton and light ion RBE for the induction of direct DNA double strand breaks Journal Article
In: Medical Physics, vol. 43, no. 5, pp. 2131–2140, 2016, ISSN: 2473-4209, (_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.4944870).
Abstract | Links | BibTeX | Tags: biological effects of ionising particles, biomolecular effects of radiation, Cell Nucleus, cell nucleus model, cellular effects of radiation, DNA, DNA double-strand breaks, Dosimetry, Dosimetry/exposure assessment, Energy transfer, Genomics, Ion beams, Ion radiation effects, Monte Carlo calculations, Monte Carlo methods, Monte Carlo simulations, Monte Carlo track structure, Protons, RBE, Schottky barriers, Scintigraphy
@article{pater_proton_2016b,
title = {Proton and light ion RBE for the induction of direct DNA double strand breaks},
author = {Piotr Pater and Gloria Bäckstöm and Fernanda Villegas and Anders Ahnesjö and Shirin A. Enger and Jan Seuntjens and Issam El Naqa},
url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1118/1.4944870},
doi = {10.1118/1.4944870},
issn = {2473-4209},
year = {2016},
date = {2016-01-01},
urldate = {2021-09-07},
journal = {Medical Physics},
volume = {43},
number = {5},
pages = {2131--2140},
abstract = {Purpose: To present and characterize a Monte Carlo (MC) tool for the simulation of the relative biological effectiveness for the induction of direct DNA double strand breaks () for protons and light ions. Methods: The MC tool uses a pregenerated event-by-event tracks library of protons and light ions that are overlaid on a cell nucleus model. The cell nucleus model is a cylindrical arrangement of nucleosome structures consisting of 198 DNA base pairs. An algorithm relying on k-dimensional trees and cylindrical symmetries is used to search coincidences of energy deposition sites with volumes corresponding to the sugar–phosphate backbone of the DNA molecule. Strand breaks (SBs) are scored when energy higher than a threshold is reached in these volumes. Based on the number of affected strands, they are categorized into either single strand break (SSB) or double strand break (DSB) lesions. The number of SBs composing each lesion (i.e., its size) is also recorded. is obtained by taking the ratio of DSB yields of a given radiation field to a 60Co field. The MC tool was used to obtain SSB yields, DSB yields, and as a function of linear energy transfer (LET) for protons (1H+), 4He2+, 7Li3+, and 12C6+ ions. Results: For protons, the SSB yields decreased and the DSB yields increased with LET. At ≈24.5 keV μm−1, protons generated 15% more DSBs than 12C6+ ions. The varied between 1.24 and 1.77 for proton fields between 8.5 and 30.2 keV μm−1, and it was higher for iso-LET ions with lowest atomic number. The SSB and DSB lesion sizes showed significant differences for all radiation fields. Generally, the yields of SSB lesions of sizes ≥2 and the yields of DSB lesions of sizes ≥3 increased with LET and increased for iso-LET ions of lower atomic number. On the other hand, the ratios of SSB to DSB lesions of sizes 2–4 did not show variability with LET nor projectile atomic number, suggesting that these metrics are independent of the radiation quality. Finally, a variance of up to 8% in the DSB yields was observed as a function of the particle incidence angle on the cell nucleus. This simulation effect is due to the preferential alignment of ion tracks with the DNA nucleosomes at specific angles. Conclusions: The MC tool can predict SSB and DSB yields for light ions of various LET and estimate . In addition, it can calculate the frequencies of different DNA lesion sizes, which is of interest in the context of biologically relevant absolute dosimetry of particle beams.},
note = {_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.4944870},
keywords = {biological effects of ionising particles, biomolecular effects of radiation, Cell Nucleus, cell nucleus model, cellular effects of radiation, DNA, DNA double-strand breaks, Dosimetry, Dosimetry/exposure assessment, Energy transfer, Genomics, Ion beams, Ion radiation effects, Monte Carlo calculations, Monte Carlo methods, Monte Carlo simulations, Monte Carlo track structure, Protons, RBE, Schottky barriers, Scintigraphy},
pubstate = {published},
tppubtype = {article}
}
Purpose: To present and characterize a Monte Carlo (MC) tool for the simulation of the relative biological effectiveness for the induction of direct DNA double strand breaks () for protons and light ions. Methods: The MC tool uses a pregenerated event-by-event tracks library of protons and light ions that are overlaid on a cell nucleus model. The cell nucleus model is a cylindrical arrangement of nucleosome structures consisting of 198 DNA base pairs. An algorithm relying on k-dimensional trees and cylindrical symmetries is used to search coincidences of energy deposition sites with volumes corresponding to the sugar–phosphate backbone of the DNA molecule. Strand breaks (SBs) are scored when energy higher than a threshold is reached in these volumes. Based on the number of affected strands, they are categorized into either single strand break (SSB) or double strand break (DSB) lesions. The number of SBs composing each lesion (i.e., its size) is also recorded. is obtained by taking the ratio of DSB yields of a given radiation field to a 60Co field. The MC tool was used to obtain SSB yields, DSB yields, and as a function of linear energy transfer (LET) for protons (1H+), 4He2+, 7Li3+, and 12C6+ ions. Results: For protons, the SSB yields decreased and the DSB yields increased with LET. At ≈24.5 keV μm−1, protons generated 15% more DSBs than 12C6+ ions. The varied between 1.24 and 1.77 for proton fields between 8.5 and 30.2 keV μm−1, and it was higher for iso-LET ions with lowest atomic number. The SSB and DSB lesion sizes showed significant differences for all radiation fields. Generally, the yields of SSB lesions of sizes ≥2 and the yields of DSB lesions of sizes ≥3 increased with LET and increased for iso-LET ions of lower atomic number. On the other hand, the ratios of SSB to DSB lesions of sizes 2–4 did not show variability with LET nor projectile atomic number, suggesting that these metrics are independent of the radiation quality. Finally, a variance of up to 8% in the DSB yields was observed as a function of the particle incidence angle on the cell nucleus. This simulation effect is due to the preferential alignment of ion tracks with the DNA nucleosomes at specific angles. Conclusions: The MC tool can predict SSB and DSB yields for light ions of various LET and estimate . In addition, it can calculate the frequencies of different DNA lesion sizes, which is of interest in the context of biologically relevant absolute dosimetry of particle beams.
2015
Poole, Christopher M.; Ahnesjö, Anders; Enger, Shirin A.
Determination of subcellular compartment sizes for estimating dose variations in radiotherapy Journal Article
In: Radiation Protection Dosimetry, vol. 166, no. 1-4, pp. 361–364, 2015, ISSN: 1742-3406.
Abstract | Links | BibTeX | Tags: Algorithms, Breast Neoplasms, Cell Nucleus, Computer Simulation, Computer-Assisted, ErbB-2, Female, Humans, Image Processing, Imaging, Immunoenzyme Techniques, Male, Monte Carlo Method, Prostatic Neoplasms, Radiotherapy Dosage, Radiotherapy Planning, Receptor, Signal Processing, Subcellular Fractions, Three-Dimensional
@article{poole_determination_2015,
title = {Determination of subcellular compartment sizes for estimating dose variations in radiotherapy},
author = {Christopher M. Poole and Anders Ahnesjö and Shirin A. Enger},
doi = {10.1093/rpd/ncv305},
issn = {1742-3406},
year = {2015},
date = {2015-09-01},
journal = {Radiation Protection Dosimetry},
volume = {166},
number = {1-4},
pages = {361--364},
abstract = {The variation in specific energy absorbed to different cell compartments caused by variations in size and chemical composition is poorly investigated in radiotherapy. The aim of this study was to develop an algorithm to derive cell and cell nuclei size distributions from 2D histology samples, and build 3D cellular geometries to provide Monte Carlo (MC)-based dose calculation engines with a morphologically relevant input geometry. Stained and unstained regions of the histology samples are segmented using a Gaussian mixture model, and individual cell nuclei are identified via thresholding. Delaunay triangulation is applied to determine the distribution of distances between the centroids of nearest neighbour cells. A pouring simulation is used to build a 3D virtual tissue sample, with cell radii randomised according to the cell size distribution determined from the histology samples. A slice with the same thickness as the histology sample is cut through the 3D data and characterised in the same way as the measured histology. The comparison between this virtual slice and the measured histology is used to adjust the initial cell size distribution into the pouring simulation. This iterative approach of a pouring simulation with adjustments guided by comparison is continued until an input cell size distribution is found that yields a distribution in the sliced geometry that agrees with the measured histology samples. The thus obtained morphologically realistic 3D cellular geometry can be used as input to MC-based dose calculation programs for studies of dose response due to variations in morphology and size of tumour/healthy tissue cells/nuclei, and extracellular material.},
keywords = {Algorithms, Breast Neoplasms, Cell Nucleus, Computer Simulation, Computer-Assisted, ErbB-2, Female, Humans, Image Processing, Imaging, Immunoenzyme Techniques, Male, Monte Carlo Method, Prostatic Neoplasms, Radiotherapy Dosage, Radiotherapy Planning, Receptor, Signal Processing, Subcellular Fractions, Three-Dimensional},
pubstate = {published},
tppubtype = {article}
}
The variation in specific energy absorbed to different cell compartments caused by variations in size and chemical composition is poorly investigated in radiotherapy. The aim of this study was to develop an algorithm to derive cell and cell nuclei size distributions from 2D histology samples, and build 3D cellular geometries to provide Monte Carlo (MC)-based dose calculation engines with a morphologically relevant input geometry. Stained and unstained regions of the histology samples are segmented using a Gaussian mixture model, and individual cell nuclei are identified via thresholding. Delaunay triangulation is applied to determine the distribution of distances between the centroids of nearest neighbour cells. A pouring simulation is used to build a 3D virtual tissue sample, with cell radii randomised according to the cell size distribution determined from the histology samples. A slice with the same thickness as the histology sample is cut through the 3D data and characterised in the same way as the measured histology. The comparison between this virtual slice and the measured histology is used to adjust the initial cell size distribution into the pouring simulation. This iterative approach of a pouring simulation with adjustments guided by comparison is continued until an input cell size distribution is found that yields a distribution in the sliced geometry that agrees with the measured histology samples. The thus obtained morphologically realistic 3D cellular geometry can be used as input to MC-based dose calculation programs for studies of dose response due to variations in morphology and size of tumour/healthy tissue cells/nuclei, and extracellular material.
2012
Enger, Shirin A.; Ahnesjö, Anders; Verhaegen, Frank; Beaulieu, Luc
Dose to tissue medium or water cavities as surrogate for the dose to cell nuclei at brachytherapy photon energies Journal Article
In: Physics in Medicine and Biology, vol. 57, no. 14, pp. 4489–4500, 2012, ISSN: 1361-6560.
Abstract | Links | BibTeX | Tags: Brachytherapy, Cell Line, Cell Nucleus, Humans, Monte Carlo Method, Photons, Radiation Dosage, Radiotherapy Dosage, Water
@article{enger_dose_2012,
title = {Dose to tissue medium or water cavities as surrogate for the dose to cell nuclei at brachytherapy photon energies},
author = {Shirin A. Enger and Anders Ahnesjö and Frank Verhaegen and Luc Beaulieu},
doi = {10.1088/0031-9155/57/14/4489},
issn = {1361-6560},
year = {2012},
date = {2012-07-01},
journal = {Physics in Medicine and Biology},
volume = {57},
number = {14},
pages = {4489--4500},
abstract = {It has been suggested that modern dose calculation algorithms should be able to report absorbed dose both as dose to the local medium, D(m,m,) and as dose to a water cavity embedded in the medium, D(w,m), using conversion factors from cavity theory. Assuming that the cell nucleus with its DNA content is the most important target for biological response, the aim of this study is to investigate, by means of Monte Carlo (MC) simulations, the relationship of the dose to a cell nucleus in a medium, D(n,m,) to D(m,m) and D(w,m), for different combinations of cell nucleus compositions and tissue media for different photon energies used in brachytherapy. As D(n,m) is very impractical to calculate directly for routine treatment planning, while D(m,m) and D(w,m) are much easier to obtain, the questions arise which one of these quantities is the best surrogate for D(n,m) and which cavity theory assumptions should one use for its estimate. The Geant4.9.4 MC code was used to calculate D(m,m,) D(w,m) and D(n,m) for photon energies from 20 (representing the lower energy end of brachytherapy for ¹⁰³Pd or ¹²⁵I) to 300 keV (close to the mean energy of (¹⁹²Ir) and for the tissue media adipose, breast, prostate and muscle. To simulate the cell and its nucleus, concentric spherical cavities were placed inside a cubic phantom (10 × 10 × 10 mm³). The diameter of the simulated nuclei was set to 14 µm. For each tissue medium, three different setups were simulated; (a) D(n,m) was calculated with nuclei embedded in tissues (MC-D(n,m)). Four different published elemental compositions of cell nuclei were used. (b) D(w,m) was calculated with MC (MC-D(w,m)) and compared with large cavity theory calculated D(w,m) (LCT-D(w,m)), and small cavity theory calculated D(w,m) (SCT-D(w,m)). (c) D(m,m) was calculated with MC (MC-D(m,m)). MC-D(w,m) is a good substitute for MC-D(n,m) for all photon energies and for all simulated nucleus compositions and tissue types. SCT-D(w,m) can be used for most energies in brachytherapy, while LCT-D(w,m) should only be considered for source spectra well below 50 keV, since contributions to the absorbed dose inside the nucleus to a large degree stem from electrons released in the surrounding medium. MC-D(m,m) is not an appropriate substitute for MC-D(n,m) for the lowest photon energies for adipose and breast tissues. The ratio of MC-D(m,m) to MC-D(n,m) for adipose and breast tissue deviates from unity by 34% and 15% respectively for the lowest photon energy (20 keV), whereas the ratio is close to unity for higher energies. For prostate and muscle tissue MC-D(m,m) is a good substitute for MC-D(n,m). However, for all photon energies and tissue types the nucleus composition with the highest hydrogen content behaves differently than other compositions. Elemental compositions of the tissue and nuclei affect considerably the absorbed dose to the cell nuclei for brachytherapy sources, in particular those at the low-energy end of the spectrum. Thus, there is a need for more accurate data for the elemental compositions of tumours and healthy cells. For the nucleus compositions and tissue types investigated, MC-D(w,m) is a good substitute to MC-D(n,m) for all simulated photon energies. Whether other studied surrogates are good approximations to MC-D(n,m) depends on the target size, target composition, composition of the surrounding tissue and photon energy.},
keywords = {Brachytherapy, Cell Line, Cell Nucleus, Humans, Monte Carlo Method, Photons, Radiation Dosage, Radiotherapy Dosage, Water},
pubstate = {published},
tppubtype = {article}
}
It has been suggested that modern dose calculation algorithms should be able to report absorbed dose both as dose to the local medium, D(m,m,) and as dose to a water cavity embedded in the medium, D(w,m), using conversion factors from cavity theory. Assuming that the cell nucleus with its DNA content is the most important target for biological response, the aim of this study is to investigate, by means of Monte Carlo (MC) simulations, the relationship of the dose to a cell nucleus in a medium, D(n,m,) to D(m,m) and D(w,m), for different combinations of cell nucleus compositions and tissue media for different photon energies used in brachytherapy. As D(n,m) is very impractical to calculate directly for routine treatment planning, while D(m,m) and D(w,m) are much easier to obtain, the questions arise which one of these quantities is the best surrogate for D(n,m) and which cavity theory assumptions should one use for its estimate. The Geant4.9.4 MC code was used to calculate D(m,m,) D(w,m) and D(n,m) for photon energies from 20 (representing the lower energy end of brachytherapy for ¹⁰³Pd or ¹²⁵I) to 300 keV (close to the mean energy of (¹⁹²Ir) and for the tissue media adipose, breast, prostate and muscle. To simulate the cell and its nucleus, concentric spherical cavities were placed inside a cubic phantom (10 × 10 × 10 mm³). The diameter of the simulated nuclei was set to 14 µm. For each tissue medium, three different setups were simulated; (a) D(n,m) was calculated with nuclei embedded in tissues (MC-D(n,m)). Four different published elemental compositions of cell nuclei were used. (b) D(w,m) was calculated with MC (MC-D(w,m)) and compared with large cavity theory calculated D(w,m) (LCT-D(w,m)), and small cavity theory calculated D(w,m) (SCT-D(w,m)). (c) D(m,m) was calculated with MC (MC-D(m,m)). MC-D(w,m) is a good substitute for MC-D(n,m) for all photon energies and for all simulated nucleus compositions and tissue types. SCT-D(w,m) can be used for most energies in brachytherapy, while LCT-D(w,m) should only be considered for source spectra well below 50 keV, since contributions to the absorbed dose inside the nucleus to a large degree stem from electrons released in the surrounding medium. MC-D(m,m) is not an appropriate substitute for MC-D(n,m) for the lowest photon energies for adipose and breast tissues. The ratio of MC-D(m,m) to MC-D(n,m) for adipose and breast tissue deviates from unity by 34% and 15% respectively for the lowest photon energy (20 keV), whereas the ratio is close to unity for higher energies. For prostate and muscle tissue MC-D(m,m) is a good substitute for MC-D(n,m). However, for all photon energies and tissue types the nucleus composition with the highest hydrogen content behaves differently than other compositions. Elemental compositions of the tissue and nuclei affect considerably the absorbed dose to the cell nuclei for brachytherapy sources, in particular those at the low-energy end of the spectrum. Thus, there is a need for more accurate data for the elemental compositions of tumours and healthy cells. For the nucleus compositions and tissue types investigated, MC-D(w,m) is a good substitute to MC-D(n,m) for all simulated photon energies. Whether other studied surrogates are good approximations to MC-D(n,m) depends on the target size, target composition, composition of the surrounding tissue and photon energy.