Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.

ABSTRACT:

Purpose

The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and neck OARs. This study aims to conduct a systematic review and meta-analysis to summarize and analyze the performance of DL algorithms in contouring head and neck OARs. The objective is to assess the advantages and limitations of DL algorithms in contour planning of head and neck OARs.

Methods

This study conducted a literature search of Pubmed, Embase and Cochrane Library databases, to include studies related to DL contouring head and neck OARs, and the dice similarity coefficient (DSC) of four categories of OARs from the results

SUBMITTER: Liu P 

PROVIDER: S-EPMC10621161 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature