# Clinical Target Volume

The study introduces OncoAgent, a guideline-aware AI agent framework in which a large language model parses free-text clinical guidelines into an executable tool-call plan, generating three-dimensional target volumes without any expert-annotated training data; on planning CT from 40 mid-thoracic esophageal cancer patients (32 for training, 8 for testing), it achieved zero-shot CTV Dice of 0.842 and PTV Dice of 0.880, with no statistically significant difference from the fully supervised nnU-Net (GTV Prior) at 0.862 and 0.893 on primary metrics, and it was rated higher than that supervised baseline by blinded physicians on guideline compliance, modification effort, and clinical acceptability.

The study introduces OncoAgent, a guideline-aware AI agent framework in which a large language model parses free-text clinical guidelines into an executable tool-call plan, generating three-dimensional target volumes without any expert-annotated training data; on planning CT from 40 mid-thoracic esophageal cancer patients (32 for training, 8 for testing), it achieved zero-shot CTV Dice of 0.842 and PTV Dice of 0.880, with no statistically significant difference from the fully supervised nnU-Net (GTV Prior) at 0.862 and 0.893 on primary metrics, and it was rated higher than that supervised baseline by blinded physicians on guideline compliance, modification effort, and clinical acceptability.