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. 2016 Aug 21;61(16):6181-202.
doi: 10.1088/0031-9155/61/16/6181. Epub 2016 Aug 1.

Inverse 4D conformal planning for lung SBRT using particle swarm optimization

A Modiri  1 , X Gu, A Hagan, R Bland, P Iyengar, R Timmerman, A Sawant
Affiliations

Inverse 4D conformal planning for lung SBRT using particle swarm optimization

A Modiri et al. Phys Med Biol. .

Abstract

A critical aspect of highly potent regimens such as lung stereotactic body radiation therapy (SBRT) is to avoid collateral toxicity while achieving planning target volume (PTV) coverage. In this work, we describe four dimensional conformal radiotherapy using a highly parallelizable swarm intelligence-based stochastic optimization technique. Conventional lung CRT-SBRT uses a 4DCT to create an internal target volume and then, using forward-planning, generates a 3D conformal plan. In contrast, we investigate an inverse-planning strategy that uses 4DCT data to create a 4D conformal plan, which is optimized across the three spatial dimensions (3D) as well as time, as represented by the respiratory phase. The key idea is to use respiratory motion as an additional degree of freedom. We iteratively adjust fluence weights for all beam apertures across all respiratory phases considering OAR sparing, PTV coverage and delivery efficiency. To demonstrate proof-of-concept, five non-small-cell lung cancer SBRT patients were retrospectively studied. The 4D optimized plans achieved PTV coverage comparable to the corresponding clinically delivered plans while showing significantly superior OAR sparing ranging from 26% to 83% for D max heart, 10%-41% for D max esophagus, 31%-68% for D max spinal cord and 7%-32% for V 13 lung.

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Figures

Figure 1
Figure 1
3D views of beam central axes, tumor and OARs for the five patients.
Figure 2
Figure 2
Axial, sagittal and coronal views for (a) Patient1, (b) Patient2, (c) Patient3, (d) Patient4 and (e) Patient5 are shown in right, middle and left panels of each row, respectively. Contours for spinal cord, heart, esophagus and PTV are highlighted. PTV contours are shown for the clinical plan (PTVITV) as well as those of the end of exhalation (PTV50%). The dimensions are in mm.
Figure 3
Figure 3
4D optimization (a) procedural and (b) algorithmic workflow
Figure 4
Figure 4
ρ for a scenario where 120 apertures are considered and ηmin = 70
Figure 5
Figure 5
PSO algorithm used in this study
Figure 6
Figure 6
DVH comparison between ITV-based, 4D equal-weight and 4D optimized plans for the five SBRT patients. Each row is related to one patient and each column in related to one structure. DVH curves for the ITV-based plans recreated by 4D summation method are shown in red. DVH curves for the 4D equal-weight and 4D optimized plans are shown in black and green, respectively.
Figure 7
Figure 7
DVH comparison between 4D optimized plans with minimum delivery efficiencies of 10% (magenta) and 100% (blue) and also gated clinical plan at the end of exhale (orange) for Patient 1.
Figure 8
Figure 8
PTV DVH comparison between 4D optimized plan with minimum delivery efficiency of 70% and ITV-based plan considering both PTVITV and actual PTV at 50% respiratory phase. The three graphs are normalized to 95% PTV coverage with 100% prescribed dose.
Figure 9
Figure 9
Optimized aperture weights for one arbitrary beam
Figure 10
Figure 10
Simplified comparison between extra volumes irradiated when considering (a) ITV approach (Δ), and (b) 4D phase-specific motion-extension (δ) for an ideally-shaped spherical tumor
Figure 11
Figure 11
PTV margin extension performed in Eclipse treatment planning system for Patient 3 (having the largest tumor in this study). PTV contour on phase 00% (end-of-inhale), PTV contour on phase 00% with phase-specific margin extension and PTV contour created from ITV are shown in red, green and purple colors, respectively.
Figure 12
Figure 12
Comparing extra volumes irradiated when considering PTVITV versus PTV with phase-specific margin extension (for phase 00%) in 4D planning for the 5 patients of this study.
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References

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