Cancer survival prediction models typically assume all diagnostic data (from demographics to costly genomic tests) is available, ignoring that acquiring each modality carries real clinical burden and cost. This paper frames modality acquisition as a sequential decision problem, introducing a self-evolving LLM agent that decides, patient by patient, whether further testing is justified, using episodic memory of similar cases and accumulated decision patterns. Tested on glioma patient cohorts, it maintains competitive prediction accuracy while cutting diagnostic burden by 55%. Applications include cost- and burden-aware clinical decision support, reducing unnecessary invasive testing in oncology workflows.
Authors: Chongyu Qu, Can Cui, Zhengyi Lu, Junchao Zhu, Tianyuan Yao, Junlin Guo, Juming Xiong, Yanfan Zhu, Yuechen Yang, Bennett A. Landman, Yuankai Huo
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