A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning

Mingqing Wang, Qilin Zhang, Saikit Lam, Jing Cai, Ruijie Yang

Research output: Journal article publicationReview articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Treatment planning plays an important role in the process of radiotherapy (RT). The quality of the treatment plan directly and significantly affects patient treatment outcomes. In the past decades, technological advances in computer and software have promoted the development of RT treatment planning systems with sophisticated dose calculation and optimization algorithms. Treatment planners now have greater flexibility in designing highly complex RT treatment plans in order to mitigate the damage to healthy tissues better while maximizing radiation dose to tumor targets. Nevertheless, treatment planning is still largely a time-inefficient and labor-intensive process in current clinical practice. Artificial intelligence, including machine learning (ML) and deep learning (DL), has been recently used to automate RT treatment planning and has gained enormous attention in the RT community due to its great promises in improving treatment planning quality and efficiency. In this article, we reviewed the historical advancement, strengths, and weaknesses of various DL-based automated RT treatment planning techniques. We have also discussed the challenges, issues, and potential research directions of DL-based automated RT treatment planning techniques.

Original languageEnglish
Article number580919
JournalFrontiers in Oncology
Volume10
DOIs
Publication statusPublished - 23 Oct 2020

Keywords

  • artificial intelligence
  • automated learning
  • deep learning
  • machine learning
  • radiotherapy

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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