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Study design: Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning.

Anand, A.; Beltran, C. J.; Brooke, M. D.; Buroker, J. R.; DeWees, T. A.; Foote, R. L.; Foss, O. R.; Hughes, C. O.; Hunzeker, A. E.; Lucido, J. J.; Morigami, M.; Moseley, D. J.; Pafundi, D. H.; Patel, S. H.; Patel, Y.; Ridgway, A. K.; Tryggestad, E. J.; Wilson, M. Z.; Xi, L.; Zverovitch, A.

2021-12-08 oncology
10.1101/2021.12.07.21266421 medRxiv
Show abstract

This document reports the design of a retrospective study to validate the clinical acceptability of a deep-learning-based model for the autosegmentation of organs-at-risk (OARs) for use in radiotherapy treatment planning for head & neck (H&N) cancer patients.

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