The Essential Role of Monte Carlo Simulations for Lung Dosimetry in Liver Radioembolization with 90Y Microspheres
<p>Density distribution in lungs ROI in <math display="inline"><semantics> <mrow> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi mathvariant="normal">c</mi> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>. The distribution is characterized by a range of values from 0 to 1.06 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi mathvariant="normal">c</mi> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>, a mean value of <math display="inline"><semantics> <mrow> <mn>0.221</mn> <mtext> </mtext> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi mathvariant="normal">c</mi> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>, and a median value of <math display="inline"><semantics> <mrow> <mn>0.179</mn> <mtext> </mtext> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi mathvariant="normal">c</mi> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>.</p> "> Figure 2
<p><span class="html-italic">AD</span> distribution (values expressed in Gy/GBq) within lungs (<b>A</b>) and the associated relative uncertainty map (<b>B</b>) for <span class="html-italic">LS</span> = 40%.</p> "> Figure 3
<p>Plot of the <span class="html-italic">AD</span> per unit decay to the target voxel (y-axis) versus the source-target voxel distance (x-axis) for the previously published ST kernel (9 × 9 × 9) [<a href="#B26-applsci-14-07684" class="html-bibr">26</a>] (data freely available on the website <a href="https://www.medphys.it/down_svoxel.htm" target="_blank">https://www.medphys.it/down_svoxel.htm</a>, accessed on 24 July 2024) and the lung kernel calculated in this work (32 × 32 × 32). The right plot reports the same data shown in the left plot up to a source-target of 20 mm.</p> "> Figure 4
<p>Correlation plot of the lungs’ <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>A</mi> <mi>D</mi> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math>, obtained from <span class="html-italic">MC</span> simulations with the Reference phantom (x-axis) with those from: mono-compartmental MIRD approach (MIRD), local energy deposition (LED), SVOX with ST kernel with corrections for tissue heterogeneities according to Equation (3) (SVOX_L), or without these corrections (SVOX_ST). For the SVOX_ST data, a previously published VSV kernel for soft tissue (ST) was used [<a href="#B26-applsci-14-07684" class="html-bibr">26</a>] (data freely available on the website <a href="https://www.medphys.it/down_svoxel.htm" target="_blank">https://www.medphys.it/down_svoxel.htm</a>, accessed on 24 July 2024). For each dataset, each point in the plot is associated with an increasing <span class="html-italic">LS</span> value (10%, 20%, 30%, and 40%), and a line representing the linear interpolation of each dataset serves as a qualitative eye guide only.</p> "> Figure 5
<p>Correlation plot of the lungs’ <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>A</mi> <mi>D</mi> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math>, obtained from <span class="html-italic">MC</span> simulations with the Reference phantom (on the x-axis), compared with those obtained using the new lung VSV kernel (referred to as Lung_296), those obtained after a global correction based on the mean lung density of the Reference phantom (Lung_221), and those obtained after correcting for tissue heterogeneities according to Equation (3) (Lung_L, using <math display="inline"><semantics> <mrow> <mn>0.296</mn> <mtext> </mtext> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi mathvariant="normal">c</mi> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> as the uniform tissue density). For comparison, the SVOX_ST data (already presented in <a href="#applsci-14-07684-f004" class="html-fig">Figure 4</a>) are also included. For each data series, each point in the plot corresponds to an increasing <span class="html-italic">LS</span> value (10%, 20%, 30%, and 40%), with a line representing the linear interpolation of each dataset, provided as a qualitative visual guide only.</p> "> Figure 6
<p>From left to right, this figure shows an example of the activity biodistribution for the <span class="html-italic">LS</span> = 20% case and the corresponding <span class="html-italic">AD</span> distribution maps for the <span class="html-italic">MC</span> simulation. Also included are the <span class="html-italic">AD</span> distribution maps obtained from several convolution approaches for visual comparison. The colors used to represent the activity biodistribution are purely illustrative, indicating that the activity was uniformly distributed within each region (for further details, see <a href="#sec2dot1-applsci-14-07684" class="html-sec">Section 2.1</a>). The <span class="html-italic">AD</span> distributions are reported as Gy per GBq of administered activity. For Lung_296 and Lung_L, the <span class="html-italic">AD</span> distribution does not display any dose in the liver region, reflecting the computational choice to crop the activity map to the lung region only. Note that the <span class="html-italic">AD</span> distribution for Lung_296 is represented with a different color scale, from 0 to 40 Gy/GBq, compared to the other methods, which are shown with a scale from 0 to 120 Gy/GBq, because using the latter scale, the dose values would not have been visible.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Anthropomorphic Voxelized Phantom
2.2. Classical Approaches: Mono-Compartmental Organ Level, Local Energy Deposition, and Convolution with Soft Tissue Voxel S-Values
2.3. Monte Carlo Simulations
2.4. Evaluation of Mean Absorbed Doses and Relative Uncertainties
3. Results
3.1. Monte Carlo’s Uncertainties
3.2. VSV Kernel for Lung Tissue
3.3. Monte Carlo vs. “Classical” Approaches
3.4. Monte Carlo vs. SVOX with the VSV Kernel for the Lung Tissue
3.5. Absorbed Dose Distributions
3.6. Impact on Clinical Decision Making
4. Discussion
- Radiation transport is strongly influenced by the tissue heterogeneities of the lungs, which substantially affect the absorbed dose.
- A lung tissue with a uniform density corresponding to the average density of the case under study is not an accurate descriptor of the real tissue.
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Lung Shunt | |||
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Lung Shunt | MIRD (%) | LED (%) | SVOX_ST (%) | SVOX_L (%) |
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Lung Shunt | MC | MIRD | LED | SVOX_ST | SVOX_L |
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Lung Shunt | Lung_296 (%) | Lung_221 (%) | Lung_L (%) |
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Lung Shunt | MC | Lung_296 | Lung_221 | Lung_L |
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Lung Shunt | (Gy/GBq) | AHASA (GBq) | MLA (GBq) |
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d’Andrea, E.; Lanconelli, N.; Cremonesi, M.; Patera, V.; Pacilio, M. The Essential Role of Monte Carlo Simulations for Lung Dosimetry in Liver Radioembolization with 90Y Microspheres. Appl. Sci. 2024, 14, 7684. https://doi.org/10.3390/app14177684
d’Andrea E, Lanconelli N, Cremonesi M, Patera V, Pacilio M. The Essential Role of Monte Carlo Simulations for Lung Dosimetry in Liver Radioembolization with 90Y Microspheres. Applied Sciences. 2024; 14(17):7684. https://doi.org/10.3390/app14177684
Chicago/Turabian Styled’Andrea, Edoardo, Nico Lanconelli, Marta Cremonesi, Vincenzo Patera, and Massimiliano Pacilio. 2024. "The Essential Role of Monte Carlo Simulations for Lung Dosimetry in Liver Radioembolization with 90Y Microspheres" Applied Sciences 14, no. 17: 7684. https://doi.org/10.3390/app14177684