用少量样本强化学习，让多模态大模型直接生成精准放疗方案
Transforming Multimodal Models into Action Models for Radiotherapy
- 基于大模型预训练知识，用少样本强化学习实现放疗计划自动优化
- 在前列腺癌数据上比传统强化学习方法奖励分更高、剂量分布更优
- 适合希望提升放疗规划效率与标准化的医疗机构和研发团队
放疗是关键癌症治疗手段，需精确规划以平衡肿瘤清除与健康组织保护。传统治疗规划（TP）依赖人工迭代，耗时且易受主观影响。本文提出一种新框架，通过少量样本强化学习（RL），将大型多模态基础模型（MLM）转化为放疗行动模型。该方法利用MLM对物理、辐射与解剖的先验知识，结合蒙特卡洛模拟器进行迭代优化。在前列腺癌数据上的仿真结果显示，该方法在质量与效率上均优于传统强化学习方法，获得更高奖励分数与更优剂量分布。这一概念验证表明，先进AI模型有望融入临床流程，提升放疗规划的速度、质量和标准化水平。
原文摘要 · Abstract (English)
Radiotherapy is a crucial cancer treatment that demands precise planning to balance tumor eradication and preservation of healthy tissue. Traditional treatment planning (TP) is iterative, time-consuming, and reliant on human expertise, which can potentially introduce variability and inefficiency. We propose a novel framework to transform a large multimodal foundation model (MLM) into an action model for TP using a few-shot reinforcement learning (RL) approach. Our method leverages the MLM's extensive pre-existing knowledge of physics, radiation, and anatomy, enhancing it through a few-shot learning process. This allows the model to iteratively improve treatment plans using a Monte Carlo simulator. Our results demonstrate that this method outperforms conventional RL-based approaches in both quality and efficiency, achieving higher reward scores and more optimal dose distributions in simulations on prostate cancer data. This proof-of-concept suggests a promising direction for integrating advanced AI models into clinical workflows, potentially enhancing the speed, quality, and standardization of radiotherapy treatment planning.
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