Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer.

ABSTRACT:

Introduction

Deep learning (DL) models are increasingly developed for auto-segmentation in radiotherapy. Qualitative analysis is of great importance for clinical implementation, next to quantitative. This study evaluates a DL segmentation model for left- and right-sided locally advanced breast cancer both quantitatively and qualitatively.

Methods

For each side a DL model was trained, including primary breast CTV (CTVp), lymph node levels 1-4, heart, lungs, humeral head, thyroid and esophagus. For evaluation, both automatic segmentation, including correction of contours when needed, and manual delineation was performed and both processes were timed. Quantitative scoring with dice-similarity coefficient (DSC), 95% Hausdorff Distance (95%HD) and surface DSC (sDSC) was used to c

SUBMITTER: Bakx N 

PROVIDER: S-EPMC10205480 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature