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Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification

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arxiv 2310.10867 v1 pith:ZDA3J6UF submitted 2023-10-16 physics.med-ph

Kareem A. Wahid , Carlos E. Cardenas , Barbara Marquez , Tucker J. Netherton , Benjamin H. Kann , Laurence E. Court , Renjie He , Mohamed A. Naser
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Amy C. Moreno Clifton D. Fuller David Fuentes
This is my paper · ORCID
classification physics.med-ph
keywords auto-contouringinsightsradiotherapydatadata-centricperformanceadoptionadvanced
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Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three key insights: (1) High-quality training data is essential for auto-contouring algorithms; (2) Auto-contouring models demonstrate commendable performance even with limited medical image data; (3) The quantitative performance of auto-contouring is reaching a plateau. Given these insights, we emphasize the need for the radiotherapy research community to embrace data-centric approaches to further foster clinical adoption of auto-contouring technologies.

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