Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma.

ABSTRACT:

Aims

To save time and have more consistent contours, fully automatic segmentation of targets and organs at risk (OAR) is a valuable asset in radiotherapy. Though current deep learning (DL) based models are on par with manual contouring, they are not perfect and typical errors, as false positives, occur frequently and unpredictably. While it is possible to solve this for OARs, it is far from straightforward for target structures. In order to tackle this problem, in this study, we analyzed the occurrence and the possible dose effects of automated delineation outliers.

Methods

First, a set of controlled experiments on synthetically generated outliers on the CT of a glioblastoma (GBM) patient was performed. We analyzed the dosimetric impact on outliers with different location, s

SUBMITTER: Poel R 

PROVIDER: S-EPMC9587574 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature