aceExternal evaluation of a deep learning-based algorithm for automated lung tumor and esophagus segmentation on CBCT
Online adaptive radiotherapy (ART) requires fast and accurate delineation of targets and organs at risk on cone-beam computed tomography (CBCT). Although deep learning (DL)-based CBCT auto-segmentation methods have been proposed, their clinical generalizability remains insufficiently validated. This study aimed to externally evaluate a pretrained DL model for lung tumor and esophagus segmentation on CBCT. Originally trained using C-arm linac CBCT images, the model was directly applied to an independent cohort of…
On an independent cohort from a different institution, the pretrained model segmented lung tumors on CBCT more accurately than rigid contour propagation, raising mean Dice from 0.66 to 0.76.
Applied outside its original development environment, the same model lost accuracy, with esophagus Dice falling from 0.79 to 0.72 and tumor dose metrics differing from reference contours.
The evaluation used a small cohort of 12 lung cancer patients from a single external institution, with esophagus results based on only 6 patients, so the findings may not generalize widely.
Evidence
- Peer-reviewedBiomedical Physics & Engineering Express2026-10-02
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Truvace Impact Record TRV-2026-1365, v1: “External evaluation of a deep learning-based algorithm for automated lung tumor and esophagus segmentation on CBCT.” Truvace, 2026-10-14. /record/TRV-2026-1365 (accessed at citation time). sha256 186ed84e86027a96…
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