Health AI Central
Peer-Reviewed Publication
Biomed Phys Eng Express2026October 2, 2026Journal Article

External evaluation of a deep learning-based algorithm for automated lung tumor and esophagus segmentation on CBCT.

Zhehao Zhang1, Dean Hobbis2, Jue Jiang3, Aneesh Rangnekar4, Chloe Min Seo Choi5, Michael Waters6, Harini Veeraraghavan7, Geoffrey D Hugo8
1Department of Radiation Oncology, Washington University in St Louis, 4921 Parkview Pl, Lower Leve, St. Louis, Missouri, 63130-4899, United States.
2Department of Radiation Oncology, Washington University in St Louis, 660 S Euclid Ave, St Louis, Missouri, 63110, United States.
3Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York, 10065, United States.
4Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, New York, 10065, United States.
5Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York, 10065, United States.
6Department of Radiation Oncology, Washington University School of Medicine in Saint Louis, 4921 Parkview Pl, Lower Leve, St. Louis, Missouri, 63110-1010, United States.
7Dept of Medical Physics, Memorial-Sloan Kettering Cancer Centre, 1275 York Avenue, New York City, New York, 10065, United States.
8Radiation Oncology, Washington University in St Louis, 4921 PARKVIEW PLACE, Campus Box 8224, St. Louis, Missouri, 63130-4899, United States.
PubMed DOI

Abstract

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…

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