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Analysis of Automatic Match Results for CBCT Localization of Conventionally Fractionated Lung Tumors


Michael Grams

M Grams*, L Brown, D Brinkmann, D Pafundi, D Mundy, S Park, Y Garces, K Olivier, L Fong de los Santos, Mayo Clinic, Rochester, MN

MO-B-Salon EF-9 Monday 10:00:00 AM - 12:00:00 PM Room: Salon EF

Purpose: To evaluate the dependence of an automatic match process on the size of the user-defined region of interest (ROI), the structure volume of interest (VOI), and changes in tumor volume when using CBCT for lung tumor localization and to compare these results to a gold standard defined by a physician's manual match.

Methods: Daily CBCT images for 11 lung cancer patients (109 fractions) treated with conventionally fractionated radiotherapy were retrospectively matched to a reference CT using Varian's OBI software and a 3-step automatic matching protocol. Matches were performed with 3 ROI sizes (small, medium, large), both with and without a structure VOI (ITV or PTV) used in the last step. Additionally, matches using an intensity range isolating the bony anatomy of the spine were performed. All automatic matches were compared with a physician's manual match.

Results: Automatic match results depend on ROI size and the structure VOI. Compared to the physician's manual match, automatic matches using the PTV as the structure VOI and a small ROI resulted in differences greater than or equal to 5 mm in only 1.8% of comparisons. Automatic matches with a large ROI and no VOI resulted in differences of at least 5 mm in 30.3% of comparisons. Automatic matches to the spine resulted in differences of at least 5 mm in 21.1% of comparisons. Differences between manual and automatic matches using the ITV as the structure VOI increased as tumor size decreased.

Conclusions: This study illustrates the effectiveness of an automatic matching protocol for lung cancer patients even when large changes in tumor volume occur. Optimal matching parameters included using a small ROI and the PTV as the structure VOI on the last step of the automatic match process. Users of automatic matching techniques should carefully consider how user-defined parameters affect patient localization.

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