智能决定癌症患者该做哪些检查，既准又少受罪。
SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction
- 用大模型动态判断每步检查是否值得做
- 减少55%检查负担，预测准确率仍顶尖
- 适合临床医生优化诊疗流程
癌症患者是否都需完整检查才能准确预测生存期？在多模态肿瘤学中，诊断手段按临床要求依次升级——从入院时收集的人口学信息，到需特殊组织分析的基因组检测。现有方法要么假定所有数据齐全，要么被动处理缺失，却无法主动判断对某位患者而言，下一步检查是否必要。本文将此问题建模为序列决策问题，提出SAGEAgent（基于经验引导的顺序采集），一个自演化的大语言模型临床代理，能权衡预测精度与检查侵入性，决定每位患者的检查路径。SAGEAgent通过临床工具将数值预测转为文本、记忆相似历史病例的事件记忆，以及积累可复用决策模式的语义记忆来推理患者状态。在包含TCGA-LGG、TCGA-GBM和BraTS的胶质瘤队列上，结合四种诊断模态的实验表明，SAGEAgent在保持竞争性生存预测性能的同时，平均检查负担降低55%。
原文摘要 · Abstract (English)
Does every cancer patient truly need a complete diagnostic workup for accurate survival prediction? In multimodal clinical oncology, diagnostic modalities follow a clinically mandated order of escalating burden -- from demographics collected at intake to genomic profiling requiring specialized tissue analysis. Current multimodal survival methods either assume all modalities are available or passively handle missing data, but none actively reason about whether acquiring the next modality is justified for a given patient along this ordered workflow. We formulate this as a sequential decision problem and propose SAGEAgent (Sequential Acquisition Guided by Experience), a self-evolving LLM-based clinical agent that decides which diagnostic modalities to acquire for each patient, balancing predictive accuracy against clinical invasiveness. SAGEAgent reasons about each patient's evolving diagnostic state through clinical tools that translate numerical predictions into text, an episodic memory that retrieves similar past cases, and a semantic memory that accumulates reusable decision patterns from experience. Experiments on a glioma cohort combining TCGA-LGG, TCGA-GBM, and BraTS with four diagnostic modalities demonstrate that SAGEAgent achieves competitive survival prediction accuracy while reducing average acquisition burden by 55%.
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