Geometric and dosimetric evaluation of a commercial AI auto-contouring tool on multiple anatomical sites in CT scans.

Abstract

Current radiotherapy practices rely on manual contouring of CT scans, which is time-consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto-contouring tool to assess its potential integration into clinical workflows. This retrospective study included 223 patients with treatment sites in the pelvis, abdomen, thorax, and head and neck regions. The Ethos AI tool generated auto-contours on each patients' pre-treatment planning CT, and 45 unique structures were included across the study cohort. Multiple measures of geometric similarity were computed, including surface Dice Similarity Coefficient (sDSC) and mean distance to agreement (MDA). Dosimetric concordance was evaluated by comparing mean dose and maximum 2 cm³ dose (D_{2 cc}) between manual and AI contours. Ethos AI demonstrated high geometric accuracy for well-defined structures like the bladder, lungs, and femoral heads. Smaller structures and those with less defined boundaries, such as optic nerves and duodenum, showed lower agreement. Over 70% of auto-contours demonstrated a sDSC > 0.8, and 74% had MDA < 2.5 mm. Geometric accuracy generally correlated with dosimetric concordance, however differences in contour definitions did result in some structures exhibiting dose deviations. The Ethos AI auto-contouring tool offers promising accuracy and reliability for many anatomical structures, supporting its use in planning workflows. Auto-contouring errors, although rare, highlight the importance of ongoing QA and expert manual oversight.