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. 2008 Oct 1;72(2):610-6.
doi: 10.1016/j.ijrobp.2008.06.1488.

Evaluation of multiple breathing states using a multiple instance geometry approximation (MIGA) in inverse-planned optimization for locoregional breast treatment

Alexander Lin  1 , Jean M Moran, Robin B Marsh, James M Balter, Benedick A Fraass, Daniel L McShan, Marc L Kessler, Lori J Pierce
Affiliations

Evaluation of multiple breathing states using a multiple instance geometry approximation (MIGA) in inverse-planned optimization for locoregional breast treatment

Alexander Lin et al. Int J Radiat Oncol Biol Phys. .

Abstract

Purpose: Although previous work demonstrated superior dose distributions for left-sided breast cancer patients planned for intensity-modulated radiation therapy (IMRT) at deep inspiration breath hold compared with conventional techniques with free-breathing, such techniques are not always feasible to limit the impact of respiration on treatment delivery. This study assessed whether optimization based on multiple instance geometry approximation (MIGA) could derive an IMRT plan that is less sensitive to known respiratory motions.

Methods and materials: CT scans were acquired with an active breathing control device at multiple breath-hold states. Three inverse optimized plans were generated for eight left-sided breast cancer patients: one static IMRT plan optimized at end exhale, two (MIGA) plans based on a MIGA representation of normal breathing, and a MIGA representation of deep breathing, respectively. Breast and nodal targets were prescribed 52.2 Gy, and a simultaneous tumor bed boost was prescribed 60 Gy.

Results: With normal breathing, doses to the targets, heart, and left anterior descending (LAD) artery were equivalent whether optimizing with MIGA or on a static data set. When simulating motion due to deep breathing, optimization with MIGA appears to yield superior tumor-bed coverage, decreased LAD mean dose, and maximum heart and LAD dose compared with optimization on a static representation.

Conclusions: For left-sided breast-cancer patients, inverse-based optimization accounting for motion due to normal breathing may be similar to optimization on a static data set. However, some patients may benefit from accounting for deep breathing with MIGA with improvements in tumor-bed coverage and dose to critical structures.

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Figures

Figure 1
Figure 1
Individual Patient Data for Maximum Heart Dose, comparing static vs MIGA-based optimization at both normal and deep breathing. Abbreviations: MIGA, Multiple Instance Geometry Approximation; Gy, Gray
Figure 2
Figure 2
Individual Patient Data for Maximum LAD Dose, comparing static vs MIGA-based optimization at both normal and deep breathing. Abbreviations: LAD, Left Anterior Descending Artery; MIGA, Multiple Instance Geometry Approximation; Gy, Gray
See this image and copyright information in PMC

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