Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy.

ABSTRACT:

Background and purpose

Artificial intelligence advances have stimulated a new generation of autosegmentation, however clinical evaluations of these algorithms are lacking. This study assesses the clinical utility of deep learning-based autosegmentation for MR-based prostate radiotherapy planning.

Materials and methods

Data was collected prospectively for patients undergoing prostate-only radiation at our institution from June to December 2019. Geometric indices (volumetric Dice-Sørensen Coefficient, VDSC; surface Dice-Sørensen Coefficient, SDSC; added path length, APL) compared automated to final contours. Physicians reported contouring time and rated autocontours on 3-point protocol deviation scales. Descriptive statistics and univariable analyses evaluated relationships be

SUBMITTER: Cha E 

PROVIDER: S-EPMC9444280 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature