LLM and VLM for Radiotherapy Contouring: State of the Art, Gaps, and Opportunities
Radiotherapy contouring is entering a new phase. For years, progress was driven mainly by image segmentation: better backbones, larger datasets, and stronger 3D architectures improved the automatic outlining of visible anatomy. That approach remains highly effective for organs-at-risk (OARs), where the task is largely to identify and delineate structures that can be seen directly on imaging. Target contouring is different. Gross tumor volume (GTV), clinical target volume (CTV), nodal target volumes, and postoperative beds are not defined by pixels alone. They are shaped by disease extent, stage, pathology, surgical status, laterality, risk patterns of spread, institutional practice, and protocol logic. In real clinical workflow, radiation oncologists do not contour from images alone; they contour from images interpreted in context. ...