ArticleJMIR formative research2023
How Natural Language Processing Can Aid With Pulmonary Oncology Tumor Node Metastasis Staging From Free-Text Radiology Reports: Algorithm Development and Validation.
Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Large language model ensemble for automated TNM staging from radiology reports.Bioinformatics (Oxford, England) · 2026Article
- Automated classification of clinical T staging pulmonary oncology from chest CT reports: a natural language processing framework for real-world data utilization.BMC medical informatics and decision making · 2026Article
- Cancer staging data collection using rules-based natural language processing for entity extraction from pathology notifications: the WA cancer staging project.BMC medical informatics and decision making · 2026Article
- Article
- Development and Assessment of a Pipeline for Extracting Structured Data From Free-Text Medical Reports Using a Large Language Model.JCO clinical cancer informatics · 2026Article
- Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study.JMIR medical informatics · 2025Article
- Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.BMC medical informatics and decision making · 2025Article
- Preliminary assessment of TNM classification performance for pancreatic cancer in Japanese radiology reports using GPT-4.Japanese journal of radiology · 2025Article
- Uncertainty-aware automatic TNM staging classification for [BMC medical informatics and decision making · 2024Article
- Natural Language Processing Algorithm Used for Staging Pulmonary Oncology from Free-Text Radiological Reports: "Including PET-CT and Validation Towards Clinical Use".Journal of imaging informatics in medicine · 2024Article
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Abstract
backgroundNatural language processing (NLP) is thought to be a promising solution to extract and store concepts from free text in a structured manner for data mining purposes. This is also true for radiology reports, which still consist mostly of free text. Accurate and complete reports are very important for clinical decision support, for instance, in oncological staging. As such, NLP can be a tool to structure the content of the radiology report, thereby increasing the report's value.
objectiveThis study describes the implementation and validation of an N-stage classifier for pulmonary oncology. It is based on free-text radiological chest computed tomography reports according to the tumor, node, and metastasis (TNM) classification, which has been added to the already existing T-stage classifier to create a combined TN-stage classifier.
methodsSpaCy, PyContextNLP, and regular expressions were used for proper information extraction, after additional rules were set to accurately extract N-stage.
resultsThe overall TN-stage classifier accuracy scores were 0.84 and 0.85, respectively, for the training (N=95) and validation (N=97) sets. This is comparable to the outcomes of the T-stage classifier (0.87-0.92).
conclusionsThis study shows that NLP has potential in classifying pulmonary oncology from free-text radiological reports according to the TNM classification system as both the T- and N-stages can be extracted with high accuracy.
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