Dendrite: A Structured, Accessible, and Queryable Pathology Search Database for Streamlined Experiment Planning

Pathology reports contain vital information, yet a significant portion of this data remains underutilized in electronic medical record systems due to the unstructured and varied nature of reporting. Although synoptic reporting has introduced reporting standards, the majority of pathology text remains free-form, necessitating additional processing to enable accessibility for research and clinical applications. This paper presents Dendrite, a web application designed to enhance pathology research by providing intelligent search capabilities and streamlining the creation of study cohorts. Leveraging expert knowledge and natural language processing algorithms, Dendrite converts free-form pathology reports into structured formats, facilitating easier querying and analysis. Using a custom Python script, Dendrite organizes pathology report data, enabling record linkages, text searches, and structured drop-down menus for information filtering and integration. A companion web application enables data exploration and export, showcasing its potential for further analysis and research. Dendrite, derived from existing laboratory information systems, outperforms existing implementations in terms of speed, responsiveness, and flexibility. With its efficient search functionality and support for clinical research and quality improvement efforts in the pathology field, Dendrite proves to be a valuable tool for pathologists. Future enhancements encompass user management integration, integration of natural language processing and machine learning to enhance structured reporting capabilities and seamless integration of Dendrite with the vast repository of genomics and imaging data.