From Vision to Context: A Review of Prompt-Driven Target Volume Segmentation in Radiotherapy
Accurate target volume (TV) segmentation is essential for radiotherapy (RT) planning but remains time-consuming. Moreover, the final contour depends on the expertise of the radiation oncologist. Although deep learning has improved medical image segmentation, image-only models cannot encode the clinical reasoning that governs TV delineation, particularly for the clinical target volume (CTV), where boundaries are defined by clinical knowledge rather than imaging features alone. Prompt-driven segmentation has recently emerged as a strategy to address this limitation. This work aims to review these approaches for RT TV segmentation with four objectives: (1) compare the different prompting techniques used; (2) compare their performance against image-only baselines; (3) assess how they are evaluated against standard metrics, including the Dice similarity coefficient (DSC), 95th Hausdorff distance, sensitivity, precision, and clinical assessment; and (4) identify the gaps needed for clinical translation. We systematically searched PubMed, Embase, and arXiv for studies incorporating geometric, clinical text, or interactive prompts into deep learning models for RT TV delineation. Thirteen studies met the inclusion criteria, spanning head and neck, breast, prostate, lung, cervix, and rectal cancers. Seven studies used clinical text prompting, four used geometric prompting, and two used interactive prompting; three combined modalities. Reported DSC ranged from 0.67 to 0.93, compared to 0.65 to 0.91 for image-only baselines. None reported sensitivity or precision. Clinical text prompting appears well suited to CTV delineation. Evidence remains limited, and standardized prompt engineering and clinical validation are needed before these approaches can be translated into routine RT practice.