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

作者：Hana Baroudi, Kristy K. Brock, Wenhua Cao, Xinru Chen, Caroline Chung, Laurence E. Court, Mohammad D. El Basha, Maguy Farhat, Skylar Gay, Mary Gronberg, Aashish C. Gupta, Soleil Hernandez, Kai Huang, David A. Jaffray, Rebecca Lim, Barbara Marquez, Kelly Nealon, Tucker Netherton, Callistus Nguyen, Brandon Reber, Dong Joo Rhee, Ramon M. Salazar, Mihir Shanker, Carlos Sjogreen, McKell Woodland, Jinzhong Yang, Cenji Yu, Yao Zhao · 发表于：Diagnostics · 年份：2023 · DOI：10.3390/diagnostics13040667 · 被引用次数：103 · 研究领域：Advanced Radiotherapy Techniques、Radiation Dose and Imaging、Radiomics and Machine Learning in Medical Imaging

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.