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

• DOI: 10.1101/2021.12.07.21266421
• Publication Date: 2021-12-09T08:15:21Z
• Authors: Aman Anand; Chris J. Beltran; Mark D. Brooke; Justine R. Buroker; Todd A. DeWees; Robert L. Foote; Olivia R. Foss; Cían O. Hughes; Ashley E. Hunzeker; J. John Lucido; Megumi Morigami; Douglas J. Moseley; Deanna H. Pafundi; Samir H. Patel; Yojan Patel; Ana K. Ridgway; Erik J. Tryggestad; Megan Z. Wilson; Lihong Xi; Alexei Zverovitch
• Keywords: 03 medical and health sciences; 0302 clinical medicine; 3. Good health

AbstractThis 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.