Robertson Scott, Koppireddy Venkata, Cumbo Jeremy, Rashidi Hooman, Albahra Samer
Center for Diagnostics and Artificial Intelligence, Cleveland Clinic Foundation, Department of Pathology and Laboratory Medicine, 9500 Euclid Ave, Cleveland, OH 44195, USA.
Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Department of Pathology, 200 Lothrop Street, Pittsburgh, PA 15261, USA.
J Pathol Inform. 2025 Mar 18;17:100436. doi: 10.1016/j.jpi.2025.100436. eCollection 2025 Apr.
Pathologists rely on access to historical diagnostic case texts for research, education, and peer learning. However, many laboratory information systems (LIS), including Epic Beaker, lack optimized search tools tailored to pathology-specific text queries. To address this need, we developed PIRO (Pathology Information Retrieval Optimizer), a web-based platform enabling efficient text searches of diagnostic archives. Built using FastAPI, Angular, and Apache Solr, PIRO supports both basic and advanced search functionalities, faceted filtering, and data extraction, while ensuring compliance with institutional privacy protocols. PIRO's capabilities extend to case cohort building, search result export, and secure access control within the institutional network. In an 8-month study, we observed significantly higher PIRO adoption rates (67 %) among pathologists compared to Epic Beaker's SlicerDicer (9 %), underscoring PIRO's usability and relevance. Additionally, we implemented a large language model (LLM) to annotate reports with a "Malignancy Risk" label, enhancing search precision and enabling future expansion of automated annotations. Ongoing work focuses on integrating PIRO with our digital pathology platform, enabling direct access to digital slides from case results. PIRO's adaptable design makes it applicable across institutions, advancing search and retrieval efficiency in pathology archives and enhancing support for pathology research and education.
病理学家依靠获取历史诊断病例文本进行研究、教育和同行学习。然而，许多实验室信息系统（LIS），包括Epic Beaker，都缺乏针对病理学特定文本查询量身定制的优化搜索工具。为满足这一需求，我们开发了PIRO（病理学信息检索优化器），这是一个基于网络的平台，可对诊断档案进行高效的文本搜索。PIRO使用FastAPI、Angular和Apache Solr构建，支持基本和高级搜索功能、分面筛选和数据提取，同时确保符合机构隐私协议。PIRO的功能扩展到病例队列构建、搜索结果导出以及机构网络内的安全访问控制。在一项为期8个月的研究中，我们观察到病理学家对PIRO的采用率（67%）显著高于Epic Beaker的SlicerDicer（9%），这突出了PIRO的可用性和相关性。此外，我们实施了一个大语言模型（LLM），用“恶性风险”标签对报告进行注释，提高搜索精度，并为未来自动注释的扩展提供可能。正在进行的工作重点是将PIRO与我们的数字病理学平台集成，以便从病例结果直接访问数字切片。PIRO的适应性设计使其适用于各个机构，提高了病理学档案的搜索和检索效率，并加强了对病理学研究和教育的支持。
