发表机构
Icahn School of Medicine at Mount Sinai; Technical University of Munich; Memorial Sloan Kettering Cancer Center（西奈山伊坎医学院; 慕尼黑工业大学; 纪念斯隆凯特琳癌症中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发了模块化软件平台STEP，用于在异构环境中编排计算病理学AI的前瞻性静默试验，已部署于三家机构支持EAGLE模型评估，可减少工程重复工作量并助力AI的真实世界评估。
AI 中文摘要
前瞻性静默试验通过在不影响患者管理的情况下评估人工智能（AI）模型在实时临床数据上的性能和操作可靠性，为AI模型的回顾性验证与临床应用搭建了重要桥梁。在计算病理学领域，开展静默试验需要整合实验室信息系统、数字病理基础设施、计算资源及模型推理流水线，而这类工作流程常采用特定应用的软件实现。我们开发了病理专用静默试验引擎（Silent Trial Engine for Pathology, STEP），这是一个可复用的软件平台，用于在异构临床与计算环境中编排计算病理学AI模型的前瞻性静默试验。STEP通过模块化适配器接口将通用试验编排逻辑与机构专属的数据访问及计算基础设施分离，支持定时病例发现、单张切片推理提交、确定性幂等性、故障恢复、结果及辅助数据采集、持久化试验与运行状态管理，以及审计日志记录，其计算适配器支持本地执行及使用LSF和Slurm的高性能计算环境。STEP已在三家机构部署，用于支持EAGLE（一款基于苏木精-伊红染色全切片图像预测EGFR突变状态的AI模型）的前瞻性静默评估。通过将试验级工作流程逻辑与站点特定集成分离，STEP可在异构病理环境中运行统一执行框架，同时保持持久且可审计的试验状态，该方法可减少重复工程工作量，并促进计算病理学AI在介入性临床部署前的系统真实世界评估。
英文摘要
Prospective silent trials provide an important bridge between retrospective validation of artificial intelligence (AI) models and their use in clinical care by evaluating model performance and operational reliability on live clinical data without influencing patient management. In computational pathology, conducting silent trials requires integration across laboratory information systems, digital pathology infrastructure, computational resources, and model inference pipelines, and these workflows are often implemented using application-specific software. We developed the Silent Trial Engine for Pathology (STEP), a reusable software platform for orchestrating prospective silent trials of computational pathology AI models across heterogeneous clinical and computational environments. STEP separates common trial orchestration from institution-specific data access and compute infrastructure through modular adapter interfaces. The platform supports scheduled case discovery, per-slide inference submission, deterministic idempotency, failure recovery, result and ancillary-data ingestion, persistent trial and run state, and audit logging, with compute adapters supporting local execution and high-performance computing environments using LSF and Slurm. STEP was deployed at three institutions to support prospective silent evaluation of EAGLE, an AI model for predicting EGFR mutation status from hematoxylin and eosin-stained whole-slide images. By separating trial-level workflow logic from site-specific integrations, STEP enables a common execution framework to operate across heterogeneous pathology environments while maintaining durable and auditable trial state. This approach may reduce duplicated engineering effort and facilitate systematic real-world evaluation of computational pathology AI before interventional clinical deployment.
