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Review
. 2023 Feb 10;13(4):667.
doi: 10.3390/diagnostics13040667.

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

Hana Baroudi  1   2 , Kristy K Brock  1   3 , Wenhua Cao  1 , Xinru Chen  1   2 , Caroline Chung  4 , Laurence E Court  1 , Mohammad D El Basha  1   2 , Maguy Farhat  4 , Skylar Gay  1   2 , Mary P Gronberg  1   2 , Aashish Chandra Gupta  1   2   3 , Soleil Hernandez  1   2 , Kai Huang  1   2 , David A Jaffray  1   3 , Rebecca Lim  1   2 , Barbara Marquez  1   2 , Kelly Nealon  1   2 , Tucker J Netherton  1 , Callistus M Nguyen  3 , Brandon Reber  2   3 , Dong Joo Rhee  1 , Ramon M Salazar  1 , Mihir D Shanker  5   6 , Carlos Sjogreen  7 , McKell Woodland  3   8 , Jinzhong Yang  1 , Cenji Yu  1   2 , Yao Zhao  1   2
Affiliations
Review

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

Hana Baroudi et al. Diagnostics (Basel). .

Abstract

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.

Keywords: artificial intelligence; quality assurance; radiotherapy treatment planning.

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Conflict of interest statement

Research reported in this publication was supported in part by the Helen Black Image Guided Fund, resources of the Image Guided Cancer Therapy Research Program at The University of Texas MD Anderson Cancer Center, a generous gift from the Apache Corporation, the National Cancer Institute of the National Institutes of Health under award numbers 1R01CA221971, P30CA016672, R01CA235564, P01CA261669, and UH3CA202665, the Wellcome Trust, the Cancer Prevention and Research institute of Texas (CPRIT), Innovation in Cancer Informatics, Varian Medical Systems, and the Tumor Measurement Initiative through the MD Anderson Strategic Initiative Development Program (STRIDE).

Figures

Figure 1
Figure 1
A typical automation workflow starts with automated segmentation of targets and organs at risk (OARs), followed by treatment planning (here described by beam setup and dose optimization). The individual tasks may be automated separately or a complete end-to-end process with no user intervention until the end. This example is from Kisling et al. [1].
Figure 2
Figure 2
Automatically generated duodenum and small bowel contours with low Dice similarity coefficient (0.61) but were deemed clinically acceptable. Although the automatically generated contour (green: duodenum, yellow: small bowel) deviated from the “ground truth” (pink: duodenum, yellow: small bowel), as highlighted by the blue arrows, the difference was deemed insignificant by clinicians because of the identical dose constraints between the two segmented organs.
See this image and copyright information in PMC

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