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Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Hana BaroudiKristy K BrockWenhua CaoXinru ChenCaroline ChungLaurence E CourtMohammad D El BashaMaguy FarhatSkylar S GayMary P GronbergAashish Chandra GuptaSoleil HernandezKai HuangDavid A JaffrayRebecca LimBarbara MarquezKelly NealonTucker J NethertonCallistus M NguyenBrandon ReberDong Joo RheeRamon M SalazarMihir D ShankerCarlos SjogreenMcKell WoodlandJinzhong YangCenji YuYao Zhao
Published in: Diagnostics (Basel, Switzerland) (2023)
Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.
Keyphrases
  • artificial intelligence
  • radiation therapy
  • machine learning
  • deep learning
  • weight loss
  • squamous cell carcinoma
  • big data
  • locally advanced