Inversed-Planned Respiratory Phase Gating in Lung Conformal Radiation Therapy

Abstract

Purpose: To assess whether the optimal gating window for each beam during lung radiation therapy with respiratory gating will be dependent on a variety of patient-specific factors, such as tumor size and location and the extent of relative tumor and organ motion.

Methods and materials: To create optimal gating treatment plans, we started from an optimized clinical plan, created a plan per respiratory phase using the same beam arrangements, and used an inverse planning optimization approach to determine the optimal gating window for each beam and optimal beam weights (ie, monitor units). Two pieces of information were used for optimization: (1) the state of the anatomy at each phase, extracted from 4-dimensional computed tomography scans; and (2) the time spent in each state, estimated from a 2-minute monitoring of the patient's breathing motion. We retrospectively studied 15 lung cancer patients clinically treated by hypofractionated conformal radiation therapy, for whom 45 to 60 Gy was administered over 3 to 15 fractions using 7 to 13 beams. Mean gross tumor volume and respiratory-induced tumor motion were 82.5 cm³ and 1.0 cm, respectively.

Results: Although patients spent most of their respiratory cycle in end-exhalation (EE), our optimal gating plans used EE for only 34% of the beams. Using optimal gating, maximum and mean doses to the esophagus, heart, and spinal cord were reduced by an average of 15% to 26%, and the beam-on times were reduced by an average of 23% compared with equivalent single-phase EE gated plans (P<.034, paired 2-tailed t test).

Conclusions: We introduce a personalized respiratory-gating technique in which inverse planning optimization is used to determine patient- and beam-specific gating phases toward enhancing dosimetric quality of radiation therapy treatment plans.

FIG. 1

The three-stage optimization for optimal gating inverse planning, where the number of gating windows per beam gradually reduces from N1=10 to N3=1 over the three optimization stages (OSs). At OS3, a candidate non-EE phase (Pmax) competes with the EE phase.

FIG. 2

Box-whisker illustration of the respiratory phase probabilities for 15 patients, where the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The outliers are shown using ‘+’. See Appendix A for a detailed quantification of the respiratory phase probabilities per patient.

FIG. 3

Dice coefficient variation for PTV over nine floating respiratory phases, registered on one reference phase (EE), for the 15 patients of this study

FIG. 4

(a) Dose volume histogram comparison of the clinical EE (C-EE) plan (dashed line), the optimized EE (PSO-EE) plan (dotted line), and our proposed optimal gating plan (solid line) are shown for one of the patients of this study (patient 5). (b) For the 12 clinical beams of this case study, the beam-specific optimal gating windows are shown in red over one respiratory cycle. Two out of twelve beams (6 and 8) were removed by the optimizer. The beam-on time for this patient was 31 minutes (Appendix C).

FIG. 5

Percentage dose reduction in OARs achieved from optimal gating versus PSO-EE are shown. P (paired two-tailed T-test) values are added to the figure for each dosimetric quantity. Box whisker graphs have the same specification as FIG 2.

FIG. 6

The number of beams (out of 121) which were chosen by our optimization process to be gated at different gating windows are shown with blue bars and the duty cycles, corresponding to each phase, averaged over 15 patients, are shown with black bars.