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Updated: Jan 20, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP
Ergin Soysal1, Jeremy L Warner2,3,4, Jingqi Wang1
1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas.
Abstract:
Natural language processing (NLP) technologies have been successfully applied to cancer research by enabling automated phenotypic information extraction from narratives in electronic health records (EHRs) such as pathology reports; however, developing customized NLP solutions requires substantial effort. To facilitate the adoption of NLP in cancer research, we have developed a set of customizable modules for extracting comprehensive types of cancer-related information in pathology reports (e.g., tumor size, tumor stage, and biomarkers), by leveraging the existing CLAMP system, which provides user-friendly interfaces for building customized NLP solutions for individual needs. Evaluation using annotated data at Vanderbilt University Medical Center showed that CLAMP-Cancer could extract diverse types of cancer information with good F-measures (0.80-0.98). We then applied CLAMP-Cancer to an information extraction task at Mayo Clinic and showed that we can quickly build a customized NLP system with comparable performance with an existing system at Mayo Clinic. CLAMP-Cancer is freely available for academic use.
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