ArticleEndocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists2024
Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features From Surgical Pathology Reports.
Article in Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Natural Language Processing Methods Automate Molecular Marker Extraction From Glioma Pathology Reports.Neurosurgery · 2026Article
- Navigating the complexity of WHO CNS5: the evolutionary trajectory of glioma classification and the emergence of large language models.Frontiers in oncology · 2026Review
- Developing a named entity framework for thyroid cancer staging and risk level classification using large language models.NPJ digital medicine · 2025Article
- Exploring the Potential of Claude 3 Opus in Renal Pathological Diagnosis: Performance Evaluation.JMIR medical informatics · 2024Article
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13 authors.
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Abstract
backgroundWe aim to use Natural Language Processing to automate the extraction and classification of thyroid cancer risk factors from pathology reports.
methodsWe analyzed 1410 surgical pathology reports from adult papillary thyroid cancer patients from 2010 to 2019. Structured and nonstructured reports were used to create a consensus-based ground truth dictionary and categorized them into modified recurrence risk levels. Nonstructured reports were narrative, while structured reports followed standardized formats. We developed ThyroPath, a rule-based Natural Language Processing pipeline, to extract and classify thyroid cancer features into risk categories. Training involved 225 reports (150 structured, 75 unstructured), with testing on 170 reports (120 structured, 50 unstructured) for evaluation. The pipeline's performance was assessed using both strict and lenient criteria for accuracy, precision, recall, and F1-score; a metric that combines precision and recall evaluation.
resultsIn extraction tasks, ThyroPath achieved overall strict F-1 scores of 93% for structured reports and 90% for unstructured reports, covering 18 thyroid cancer pathology features. In classification tasks, ThyroPath-extracted information demonstrated an overall accuracy of 93% in categorizing reports based on their corresponding guideline-based risk of recurrence: 76.9% for high-risk, 86.8% for intermediate risk, and 100% for both low and very low-risk cases. However, ThyroPath achieved 100% accuracy across all risk categories with human extracted pathology information.
conclusionsThyroPath shows promise in automating the extraction and risk recurrence classification of thyroid pathology reports at large scale. It offers a solution to laborious manual reviews and advancing virtual registries. However, it requires further validation before implementation.
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