  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.
