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Deep learning has significantly advanced the potential for automatedcontouring in radiotherapy planning. In this manuscript, guided by contemporaryliterature, we underscore three key insights: (1) High-quality training data isessential for auto-contouring algorithms; (2) Auto-contouring modelsdemonstrate commendable performance even with limited medical image data; (3)The quantitative performance of auto-contouring is reaching a plateau. Giventhese insights, we emphasize the need for the radiotherapy research communityto embrace data-centric approaches to further foster clinical adoption ofauto-contouring technologies.

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