Impact of Manual Contour Editing on Plan Quality for Online Adaptive Radiation Therapy for Head and Neck Cancer.

Abstract

Purpose: Online adaptive radiation therapy (oART) has high resource costs especially for head and neck (H&N) cancer, which requires recontouring complex targets and numerous organs-at-risk (OARs). Adaptive radiation therapy systems provide autocontours to help. We aimed to explore the optimal level of editing automatic contours to maintain plan quality in a cone beam computed tomography-based oART system for H&N cancer. In this system, influencer OAR contours are generated and reviewed first, which then drives the autocontouring of the remaining OARs and targets.

Methods and materials: Three-hundred-forty-nine adapted fractions of 44 patients with H&N cancer were retrospectively analyzed, with physician-edited OARs and targets. These contours and associated online-adapted plans served as the gold standard for comparison. We simulated 3 contour editing workflows: (1) no editing of contours; (2) only editing the influencers; and (3) editing the influencers and targets. The geometric difference was quantified using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). The dosimetric differences in target coverage and OAR doses were calculated between the gold standard and these 3 simulated workflows.

Results: Workflow 1 resulted in significantly inferior contour quality for all OARs (mean DSC, 0.85 ± 0.17 and HD95, 3.10 ± 5.80mm); hence, dosimetric data was not calculated for workflow 1. In workflow 2, the frequency of physician editing targets and remaining OARs were 80.8% to 95.7% and 2.3% (brachial plexus) to 67.7% (oral cavity), respectively, where the OAR differences were geometrically minor (mean DSC >0.95 with std ≤0.09). However, because of the unedited target contours of workflow 2 (mean DSC, 0.86-0.92 and mean HD95, 2.56-3.30 mm vs the ground-truth targets), plans were inadequate with insufficient coverage. In workflow 3, when both targets and influencers were edited (noninfluencer OARs were unedited), >95.5% of the adapted plans achieved the patient-specific dosimetry goals.

Conclusions: The cone beam computed tomography-based H&N oART workflow can be meaningfully accelerated by only editing the influencers and targets while omitting the remaining OARs without compromising the quality of the adaptive plans.