ArticleJCO clinical cancer informatics2023
Leveraging Natural Language Processing to Extract Features of Colorectal Polyps From Pathology Reports for Epidemiologic Study.
Article in JCO clinical cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review.JMIR medical informatics · 2025Pooled it
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Using Large Language Models for Text Classification in Colonoscopy Pathology Reports.Gastro hep advances · 2026Review
- Leveraging Natural Language Processing to Accurately Identify Histologically Confirmed Esophageal and Gastric Premalignant and Malignant Conditions Using a Large-Scale Health-Record Linked Genomic Biobank.Gastro hep advances · 2026Article
- Large Language Models in Population Oncology: A Contemporary Review on the Use of Large Language Models to Support Data Collection, Aggregation, and Analysis in Cancer Care and Research.JCO clinical cancer informatics · 2025Review
- Large language models for extracting histopathologic diagnoses of colorectal cancer and dysplasia from electronic health records.BMJ open gastroenterology · 2025Article
- Enhancing and Not Replacing Clinical Expertise: Improving Named-Entity Recognition in Colonoscopy Reports Through Mixed Real-Synthetic Training Sources.Journal of personalized medicine · 2025Article
- Employing Consensus-Based Reasoning with Locally Deployed LLMs for Enabling Structured Data Extraction from Surgical Pathology Reports.medRxiv : the preprint server for health sciences · 2025Article
- Revolutionizing Cytology and Cytopathology with Natural Language Processing and Chatbot Technologies: A Narrative Review on Current Trends and Future Directions.Bioengineering (Basel, Switzerland) · 2024Review
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10 authors.
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Abstract
purposeHistopathologic features are critical for studying risk factors of colorectal polyps, but remain deeply embedded within unstructured pathology reports, requiring costly and time-consuming manual abstraction for research. In this study, we developed and evaluated a natural language processing (NLP) pipeline to automatically extract histopathologic features of colorectal polyps from pathology reports, with an emphasis on individual polyp size. These data were then linked with structured electronic health record (EHR) data, creating an analysis-ready epidemiologic data set.
methodsWe obtained 24,584 pathology reports from colonoscopies performed at the University of Utah's Gastroenterology Clinic. Two investigators annotated 350 reports to determine inter-rater agreement, develop an annotation scheme, and create a reference standard for performance evaluation. The pipeline was then developed, and performance was compared against the reference for extracting polyp location, histology, size, shape, dysplasia, and the number of polyps. Finally, the pipeline was applied to 24,225 unseen reports and NLP-extracted data were linked with structured EHR data.
resultsAcross all features, our pipeline achieved a precision of 98.9%, a recall of 98.0%, and an F1-score of 98.4%. In patients with polyps, the pipeline correctly extracted 95.6% of sizes, 97.2% of polyp locations, 97.8% of histology, 98.3% of shapes, and 98.3% of dysplasia levels. When applied to unseen data, the pipeline classified 12,889 patients as having polyps, 4,907 patients without polyps, and extracted the features of 28,387 polyps. Tubular adenomas were the most common subtype (55.9%), 8.1% of polyps were advanced adenomas, and the mean polyp size was 0.57 (±0.4) cm.
conclusionOur pipeline extracted histopathologic features of colorectal polyps from colonoscopy pathology reports, most notably individual polyp sizes, with considerable accuracy. This study demonstrates the utility of NLP for extracting polyp features and linking these data with EHR data to create an epidemiologic data set to study colorectal polyp risk factors and outcomes.
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