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

Aman Anand; Chris Beltran; Mark D. Brooke; Justine R. Buroker; T.A. DeWees; Robert L. Foote; Olivia R. Foss; Cían Hughes; Ashley Hunzeker; J. John Lucido; Megumi Morigami; D Moseley; Deanna Pafundi; Samir H. Patel; Yojan Patel; Ana K. Ridgway; E. Tryggestad; Megan Z. Wilson; Lihong Xi; Alexei Zverovitch

doi:10.1101/2021.12.07.21266421

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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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Anand et al. (2021) studied this question.

synapsesocial.com/papers/6a9319c9f62216e085f8c192 https://doi.org/10.1101/2021.12.07.21266421

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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

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Cite This Study

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

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Cite This Study

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