ArticleJournal of digital imaging2013
Automated extraction of BI-RADS final assessment categories from radiology reports with natural language processing.
Article in Journal of digital imaging, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive value of background parenchymal enhancement on breast magnetic resonance imaging for pathological tumor response to neoadjuvant chemotherapy in breast cancers: a systematic review.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Pooled it
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled 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
- Patient perceptions and attitudes towards the use of artificial intelligence in the symptomatic breast unit.European radiology · 2026Article
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- Uncertainty-aware automatic TNM staging classification for [BMC medical informatics and decision making · 2024Article
- The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.Insights into imaging · 2024Article
- Artificial Intelligence to Improve Patient Understanding of Radiology Reports.The Yale journal of biology and medicine · 2023Review
- Qualitative assessments of density and background parenchymal enhancement on contrast-enhanced spectral mammography associated with breast cancer risk in high-risk women.The British journal of radiology · 2023Article
- A scoping review of natural language processing of radiology reports in breast cancer.Frontiers in oncology · 2023Article
- 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
- Automated Extraction of Structured Data from Text Notes in the Electronic Medical Record.Journal of general internal medicine · 2021Article
- Medical imaging and nuclear medicine: a Lancet Oncology Commission.The Lancet. Oncology · 2021Review
- A Fusion NLP Model for the Inference of Standardized Thyroid Nodule Malignancy Scores from Radiology Report Text.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Thyroid Ultrasound Reports: Will the Thyroid Imaging, Reporting, and Data System Improve Natural Language Processing Capture of Critical Thyroid Nodule Features?The Journal of surgical research · 2020Article
- Article
- [Natural language processing in radiology : Neither trivial nor impossible].Der Radiologe · 2019Review
- Automatic extraction of imaging observation and assessment categories from breast magnetic resonance imaging reports with natural language processing.Chinese medical journal · 2019Article
- Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports.JCO clinical cancer informatics · 2019Article
- Automatic inference of BI-RADS final assessment categories from narrative mammography report findings.Journal of biomedical informatics · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The objective of this study is to evaluate a natural language processing (NLP) algorithm that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) final assessment categories from radiology reports. This HIPAA-compliant study was granted institutional review board approval with waiver of informed consent. This cross-sectional study involved 1,165 breast imaging reports in the electronic medical record (EMR) from a tertiary care academic breast imaging center from 2009. Reports included screening mammography, diagnostic mammography, breast ultrasound, combined diagnostic mammography and breast ultrasound, and breast magnetic resonance imaging studies. Over 220 reports were included from each study type. The recall (sensitivity) and precision (positive predictive value) of a NLP algorithm to collect BI-RADS final assessment categories stated in the report final text was evaluated against a manual human review standard reference. For all breast imaging reports, the NLP algorithm demonstrated a recall of 100.0 % (95 % confidence interval (CI), 99.7, 100.0 %) and a precision of 96.6 % (95 % CI, 95.4, 97.5 %) for correct identification of BI-RADS final assessment categories. The NLP algorithm demonstrated high recall and precision for extraction of BI-RADS final assessment categories from the free text of breast imaging reports. NLP may provide an accurate, scalable data extraction mechanism from reports within EMRs to create databases to track breast imaging performance measures and facilitate optimal breast cancer population management strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.