A comprehensive multifaceted technical evaluation framework for implementation of auto-segmentation models in radiotherapy.

ABSTRACT:

Background

Manual contouring of organs at risk in radiotherapy is time-consuming, taking 1-4 hours per case. Automatic segmentation using deep learning has emerged as a promising solution, with many commercial options now available. However, these methods require rigorous validation before clinical use, and current evaluation approaches lack consistency and comprehensive assessment across publications.

Methods

We developed the Comprehensive Multifaceted Technical Evaluation framework, which integrates four key assessment components: quantitative geometric measures, qualitative expert evaluation, time efficiency analysis, and dosimetric evaluation. We demonstrated this framework using an in-house automatic segmentation model for brain organs at risk, trained on 100 cases and

SUBMITTER: Poel R 

PROVIDER: S-EPMC12313850 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature