Back

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.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.