中文标题#
基於檢索增強模型選擇框架的隊列感知代理用於個性化肺癌風險預測
英文标题#
Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework
中文摘要#
準確的肺癌風險預測仍然具有挑戰性，因為患者群體和臨床環境之間存在顯著的變異性 —— 沒有一個模型在所有隊列中表現最佳。 為了解決這個問題，我們提出了一個個性化的肺癌風險預測代理，通過結合隊列特定的知識與現代檢索和推理技術，動態地為每位患者選擇最合適的模型。 給定患者的 CT 掃描和結構化元數據 —— 包括人口統計、臨床和結節級特徵 —— 代理首先使用基於 FAISS 的相似性搜索，在九個多樣化的現實隊列中識別來自多機構數據庫中最相關的患者群體。 其次，將檢索到的隊列及其相關性能指標提示給大型語言模型（LLM），以從八個代表性模型中推薦最優的預測算法，包括經典的線性風險模型（例如，Mayo，Brock）、時間感知模型（例如，TD-VIT，DLSTM）以及多模態計算機視覺方法（例如，Liao，Sybil，DLS，DLI）。 這個兩階段的代理流程 —— 通過 FAISS 進行檢索，通過 LLM 進行推理 —— 實現了動態的、隊列感知的風險預測，可根據每位患者的情況進行個性化。 在此架構的基礎上，該代理支持在多樣化臨床人群中靈活且以隊列為導向的模型選擇，為現實世界中的肺癌篩查提供個性化的風險評估實踐路徑。
英文摘要#
Accurate lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings -- no single model performs best for all cohorts. To address this, we propose a personalized lung cancer risk prediction agent that dynamically selects the most appropriate model for each patient by combining cohort-specific knowledge with modern retrieval and reasoning techniques. Given a patient's CT scan and structured metadata -- including demographic, clinical, and nodule-level features -- the agent first performs cohort retrieval using FAISS-based similarity search across nine diverse real-world cohorts to identify the most relevant patient population from a multi-institutional database. Second, a Large Language Model (LLM) is prompted with the retrieved cohort and its associated performance metrics to recommend the optimal prediction algorithm from a pool of eight representative models, including classical linear risk models (e.g., Mayo, Brock), temporally-aware models (e.g., TD-VIT, DLSTM), and multi-modal computer vision-based approaches (e.g., Liao, Sybil, DLS, DLI). This two-stage agent pipeline -- retrieval via FAISS and reasoning via LLM -- enables dynamic, cohort-aware risk prediction personalized to each patient's profile. Building on this architecture, the agent supports flexible and cohort-driven model selection across diverse clinical populations, offering a practical path toward individualized risk assessment in real-world lung cancer screening.
文章页面#
基於檢索增強模型選擇框架的隊列感知代理用於個性化肺癌風險預測
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