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

Journal: Biomedical engineering online
Published Date: Nov 1, 2023

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.

Authors

  • Peiru Liu
    General Hospital of Northern Theater Command, Department of Radiation Oncology, Shenyang, China.
  • Ying Sun
    CFAR and I2R, Agency for Science, Technology and Research, Singapore.
  • Xinzhuo Zhao
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Life Science Building, 500 Zhihui Street, Hun'nan District, Shenyang, 110169, China.
  • Ying Yan
    School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, Guizhou, China.

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