构建可扩展的癌症诊疗辅助框架，实现多模态数据与AI模型解耦。
The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology
- 通过7元组架构与算法不可渗透原则，实现数据处理与AI模型独立运行。
- 在4种场景中验证，模型切换时路由逻辑不变，异常下补全请求召回率达100%。
- 适合医疗系统集成者、临床AI研发者使用，支持多协议协同部署。
目标：当前肿瘤学中的多模态深度学习模型受限于紧密耦合的单体设计，难以灵活应对数据接入、临床流程和AI推理的分离需求。为此，我们提出大型癌症助手（LCA），一种模型无关的后置编排框架，用于可扩展的临床决策支持。方法：LCA被数学形式化为基于算法不可渗透原则的7元组架构，确保编排逻辑严格独立于底层黑箱AI模型。引入入口理论，利用几何深度学习（GDL）沿结构与医学轴标准化多模态患者数据。系统通过癌症切换模块动态编排数据，并通过输出标准化中间载荷（SIP）将核心AI执行与不稳定的医院信息系统隔离。结果：概念验证（PoC）在四种技术场景中验证了编排逻辑。框架在名义流程中表现出极低的编排开销；通过模型切换时保持路由投影不变，实证证明算法不可渗透性；在注入数据异常条件下，补全数据请求（SDR）生成召回率高达100%，验证了严格的容错性；多协议执行能力亦成功验证。结论：通过结构化解耦多模态数据接入与特征推理，LCA提供高度可适应且模块化的编排基础。SIP建立了清晰的架构边界，天然为下游电子病历（EMR）互操作性铺平道路，构成独立未来范式。
原文摘要 · Abstract (English)
- Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routing, and artificial intelligence (AI) inference. To address this inflexibility, we propose the Large Cancer Assistant (LCA), a model-agnostic, post-hoc orchestration framework designed for scalable clinical decision support. - Methods: The LCA is mathematically formalized as a 7-tuple architecture grounded in the principle of Algorithmic Impermeability, ensuring the orchestration logic remains strictly independent of underlying black-box AI models. We introduce the Entry Theory, leveraging Geometric Deep Learning (GDL) to standardize multimodal patient data along distinct structural and medical axes. The system dynamically orchestrates data via a Cancer Switching Module and intentionally isolates the core AI execution from volatile hospital IT infrastructures by outputting a Standardized Intermediate Payload (SIP). - Results: A Proof of Concept (PoC) validated the orchestration logic across four technical scenarios. The framework executed a nominal flow with negligible orchestration overhead. It empirically demonstrated algorithmic impermeability by maintaining an invariant routing projection during AI model swaps, and it validated strict failure-safety by achieving a 100\% recall rate in generating targeted Supplementary Data Requests (SDR) under injected data anomalies. Multi-protocol execution capability was also successfully verified. - Conclusion: By structurally decoupling multimodal ingestion from feature inference, the LCA provides a highly adaptable and modular orchestration foundation. The SIP establishes a clear architectural boundary, natively setting the stage for downstream Electronic Medical Record (EMR) interoperability as an independent future paradigm.
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