JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
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Investigation of simple IMRT delivery techniques for non-small cell lung cancer patients with respiratory motion using 4DCT.

Techniques for generating simplified IMRT treatment plans for treating non-small cell lung cancer (NSCLC) patients with respiratory motion were investigated. To estimate and account for respiratory motion, 4-dimensional computed tomography (4DCT) datasets from 5 patients were used to design 5-field 6-MV ungated step-and-shoot intensity modulated radiotherapy (IMRT) plans delivering a dose of 66 Gy to the planning target volume (PTV). For each patient, 2 plans were generated using the mean intensity and the maximum intensity of 10 CT datasets from different breathing phases. The plans also utilized different margins around the clinical target volume/internal target volume (CTV/ITV) to account for tumor motion. To reduce the treatment time and ensure accurate dose delivery to moving targets, the number of intensity levels was minimized while maintaining dose coverage to PTV and minimizing dose to organs at risk (OARs). Dose-volume histograms (DVHs), dosimetric metrics, and outcome probabilities were evaluated for all plans. Plans using the averaged CT image dataset were inferior, requiring larger margins around the PTV, with a maximum of 1.5 cm, to ensure coverage of the tumor, and therefore increased the dose to OARs located in proximity of the tumor. The plans based on superimposed CT image datasets achieved full coverage of the tumor, while allowing tight margins around the PTV and minimizing the dose to OARs. A small number of intensity-levels (3 to 5), resulting in IMRT plans with a total of 13 to 30 segments, were sufficient for homogeneous PTV coverage, without affecting the sparing of OARs. In conclusion, a technique involving treatment planning with the superimposed CT scans of all respiratory phases, and the application of IMRT with only a small number of segments was feasible despite significant tumor motion; however, greater patient numbers are needed to support the statistical significance of the results presented in this work.

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