ArticleJournal of thoracic disease2017
Automatic Lung-RADS™ classification with a natural language processing system.
Article in Journal of thoracic disease, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- A High-Accuracy Rule-Based Algorithm for Automated Extraction of Coronary Artery Calcium Scores from Mixed-Language Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Cancer Diagnosis: A Game-changer in Healthcare.Current pharmaceutical biotechnology · 2025Review
- Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports of Lung Cancer Screening Patients Using Transformer Models.Journal of healthcare informatics research · 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
- Automating Lung-RADS Categorization And Follow-Up Recommendations Using In-Context Learning With Large Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Review
- Developing a RadLex-Based Named Entity Recognition Tool for Mining Textual Radiology Reports: Development and Performance Evaluation Study.Journal of medical Internet research · 2021Article
- Recommendations for lung cancer screening in Southern Africa.Journal of thoracic disease · 2019Review
- [Basics and applications of Natural Language Processing (NLP) in radiology].Der Radiologe · 2018Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOur aim was to train a natural language processing (NLP) algorithm to capture imaging characteristics of lung nodules reported in a structured CT report and suggest the applicable Lung-RADS™ (LR) category.
methodsOur study included structured, clinical reports of consecutive CT lung screening (CTLS) exams performed from 08/2014 to 08/2015 at an ACR accredited Lung Cancer Screening Center. All patients screened were at high-risk for lung cancer according to the NCCN Guidelines
resultsThe sensitivity/specificity of the NLP algorithm to correctly assign LR categories for suspicious nodules (LR 4) and positive nodules (LR 3/4) were 74.1%/98.6% and 75.0%/98.8% respectively. The majority of mismatches occurred in cases where pulmonary findings were present not currently addressed by LR. Misclassifications also resulted from the failure to identify exams as follow-up and the failure to completely characterize part-solid nodules. In a sub-group analysis among structured reports with standardized language, the sensitivity and specificity to detect LR 4 nodules were 87.0% and 99.5%, respectively.
conclusionsAn NLP system can accurately suggest the appropriate LR category from CTLS exam findings when standardized reporting is used.
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