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ガイドライン認識型AIエージェントによるゼロショット標的ボリューム自動輪郭描出

arXiv cs.CV / 2026/3/11

Tools & Practical UsageModels & Research

要点

  • 本記事では、再訓練を必要とせずに放射線療法での臨床標的ボリューム(CTV)をゼロショットで輪郭描出するためのガイドライン認識AIエージェント「OncoAgent」を紹介します。
  • OncoAgentは臨床ガイドラインのテキストを直接3D標的輪郭に変換し、nnU-Netなどの完全教師あり深層学習モデルに匹敵する高いDice類似係数を達成しています。
  • ブラインド臨床評価では、医師たちがOncoAgentを従来の教師ありベースラインよりも好み、ガイドライン遵守度の高さや修正のしやすさ、臨床受容性の向上が理由として挙げられました。
  • 本フレームワークは異なる解剖学的部位や別のガイドラインにも効果的にゼロショットで適応可能で、再訓練なしでほぼ即時に対応できるため、放射線療法計画のスケーラビリティと解釈性を向上させます。
  • 本手法は、専門家の注釈と更新時の再訓練に大きく依存する既存モデルの大きな制約を克服します。

Computer Science > Computer Vision and Pattern Recognition

arXiv:2603.09448 (cs)
[Submitted on 10 Mar 2026]

Title:A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation

View a PDF of the paper titled A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation, by Yoon Jo Kim and 9 other authors
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Abstract:Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers. While deep learning models automate this process, their rigid reliance on expert-annotated data requires costly retraining whenever clinical guidelines update. To overcome this limitation, we introduce OncoAgent, a novel guideline-aware AI agent framework that seamlessly converts textual clinical guidelines into three-dimensional target contours in a training-free manner. Evaluated on esophageal cancer cases, the agent achieves a zero-shot Dice similarity coefficient of 0.842 for the CTV and 0.880 for the planning target volume, demonstrating performance highly comparable to a fully supervised nnU-Net baseline. Notably, in a blinded clinical evaluation, physicians strongly preferred OncoAgent over the supervised baseline, rating it higher in guideline compliance, modification effort, and clinical acceptability. Furthermore, the framework generalizes zero-shot to alternative esophageal guidelines and other anatomical sites (e.g., prostate) without any retraining. Beyond mere volumetric overlap, our agent-based paradigm offers near-instantaneous adaptability to alternative guidelines, providing a scalable and transparent pathway toward interpretability in radiotherapy treatment planning.
Comments:
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2603.09448 [cs.CV]
  (or arXiv:2603.09448v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2603.09448
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arXiv-issued DOI via DataCite

Submission history

From: Han Joo Chae [view email]
[v1] Tue, 10 Mar 2026 10:00:01 UTC (1,915 KB)
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